{"rows":[{"id":"cmtc8lwnb000to601lv07j4ox","modelRevision":"86f0121a909e464fed5055b213ae5032c0066d0f","promptTokens":5,"outputTokens":64,"contextLength":512,"prefillTokens":null,"batchSize":256,"ttftMs":5.887,"tokSOut":306140,"tokSPrefill":217428.234375,"tokSTotal":297348.71875,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-08-28T00:52:30.984Z","notes":"Official llama-batched-bench b10665 on Vulkan with 1x RTX PRO 6000 Blackwell. Aggregate S_TG = (256 independent sequences × 64 generated tokens) / 0.053518 s = 306,140 tok/s. Ten independent process runs: median 306,140 tok/s, range 261,045–311,619 (first runs include cold start; warmed results 301,293–311,619). PP=5 tokens per sequence, TG=64, prompts not shared, N_KV=17,664, -b/-ub 512, all layers offloaded (-ngl 999); Vulkan automatically disabled unsupported Flash Attention. No speculative decoding. Exact HF revision 86f0121a909e464fed5055b213ae5032c0066d0f; source safetensors SHA-256 ac54a8dea35ed6ccac58300c78ac9bfd368b35cc3b6b18e0b2391a733800e601. F32 GGUF converted locally from those exact weights; converter used standard gpt-2 pre-tokenizer fallback because this custom BPE is unrecognized (affects generated text/tokenization, not throughput), matching the caveat on the existing CPU record. GGUF SHA-256 377ffe9c6ec79f0034bc3cc01ce37a8a85c26004b19c0548bdfbc6175f9a0a00. Ubuntu 24.04, NVIDIA driver 595.84. Output figure is transparent aggregate batch throughput, not single-stream.","verifiedRun":false,"verificationIssues":[],"promptSha256":null,"promptSample":null,"outputSample":null,"engineFlags":{"commandSnippet":"llama-batched-bench -m stories-llama2-50k-f32.gguf -c 32768 -b 512 -ub 512 -ngl 999 -npp 5 -ntg 64 -npl 256 --output-format jsonl","tensorParallel":null,"gpuLayers":999,"kvCacheDtype":null,"gpuMemUtil":null,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":512,"maxRunningSeqs":null,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"delphi-suite/stories-llama2-50k","displayName":"stories-llama2-50k","family":"Llama","params":null,"activeParams":null,"isMoE":false,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"RTX PRO 6000 Blackwell","gpuCount":1,"vramGb":96,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD Ryzen 9 9950X3D","os":"Ubuntu 24.04","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"llama.cpp","engineVersion":"b10665","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"F32","backend":"vulkan"},"user":{"id":"cmtc57rvs0000o601l1w3d4sg","username":"ProCreations","verified":false,"verifiedAt":null,"pro":false},"hardwareGroupLabel":"RTX PRO 6000 Blackwell","hardwareGroupKey":"DISCRETE_GPU:rtx pro 6000 blackwell","rank":1,"reactionCounts":{},"myEmoji":null},{"id":"cmsxu9zyi0ck7ms01v41wipnd","modelRevision":"main","promptTokens":512,"outputTokens":64,"contextLength":2048,"prefillTokens":null,"batchSize":1,"ttftMs":2.7,"tokSOut":36715.725095,"tokSPrefill":189913.419741,"tokSTotal":129756.4,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-08-17T23:02:34.315Z","notes":"CPU-only on a Ryzen 7 9800X3D (-ngl 0, GPU hidden): llama-bench b10470, -t 3 -lm mlock --poll 100 --prio 3, 512p/64n, 20 reps, best of N attempts. No public GGUF existed for this 50K-param model (1 layer, hidden_size 6), so it was converted locally to F32 with llama.cpp's convert_hf_to_gguf.py (custom tokenizer not recognized by the converter; labeled with the standard gpt-2 rule — affects text quality only, not benchmark speed). Model runs entirely from per-core L2 cache.","verifiedRun":false,"verificationIssues":[],"promptSha256":null,"promptSample":null,"outputSample":null,"engineFlags":{"commandSnippet":"E:\\llama.cpp-b10470\\llama-bench.exe -m E:\\huggingface\\stories-llama2-50k.F32.gguf -ngl 0 -t 3 -lm mlock --poll 100 --prio 3 --delay 2 -p 512 -n 64 -r 20 -o json","tensorParallel":null,"gpuLayers":0,"kvCacheDtype":null,"gpuMemUtil":null,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":null,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"delphi-suite/stories-llama2-50k","displayName":"stories-llama2-50k","family":"Llama","params":null,"activeParams":null,"isMoE":false,"baseModel":null},"hardware":{"hwClass":"CPU_ONLY","gpuName":null,"gpuCount":1,"vramGb":null,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD Ryzen 7 9800X3D","os":"windows","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"llama.cpp","engineVersion":null,"engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"F32","backend":"cuda"},"user":{"id":"cmsxeur0y0b7xms01r3olg42y","username":"CsGoat","verified":false,"verifiedAt":null,"pro":false},"hardwareGroupLabel":"AMD Ryzen 7 9800X3D","hardwareGroupKey":"CPU_ONLY:amd ryzen 7 9800x3d","rank":2,"reactionCounts":{},"myEmoji":null},{"id":"cmsxtay630cj6ms01d7jbpdi8","modelRevision":"main","promptTokens":512,"outputTokens":64,"contextLength":2048,"prefillTokens":null,"batchSize":1,"ttftMs":41.5,"tokSOut":16740.728684,"tokSPrefill":12338.485672,"tokSTotal":12709.8,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-08-17T22:35:19.036Z","notes":null,"verifiedRun":false,"verificationIssues":[],"promptSha256":null,"promptSample":null,"outputSample":null,"engineFlags":{"commandSnippet":"E:\\llama.cpp-b10470\\llama-bench.exe -m E:\\huggingface\\hub\\models--ggml-org--models\\snapshots\\499bc8821c6b12b4e53c5bffcb21ec206f212d81\\tinyllamas\\stories260K.gguf -ngl 0 -t 3 -lm mlock --poll 100 --prio 3 --delay 2 -p 512 -n 64 -r 20 -o json","tensorParallel":null,"gpuLayers":0,"kvCacheDtype":null,"gpuMemUtil":null,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":null,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"ggml-org/models-moved","displayName":"models-moved","family":null,"params":null,"activeParams":null,"isMoE":false,"baseModel":null},"hardware":{"hwClass":"CPU_ONLY","gpuName":null,"gpuCount":1,"vramGb":null,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD Ryzen 7 9800X3D","os":"windows","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"llama.cpp","engineVersion":null,"engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"F32","backend":"cuda"},"user":{"id":"cmsxeur0y0b7xms01r3olg42y","username":"CsGoat","verified":false,"verifiedAt":null,"pro":false},"hardwareGroupLabel":"AMD Ryzen 7 9800X3D","hardwareGroupKey":"CPU_ONLY:amd ryzen 7 9800x3d","rank":3,"reactionCounts":{},"myEmoji":null},{"id":"cmtpc0w6f02hwn701n1g0v9cg","modelRevision":"main","promptTokens":304,"outputTokens":512,"contextLength":32768,"prefillTokens":0,"batchSize":256,"ttftMs":857.36,"tokSOut":14499.4,"tokSPrefill":355,"tokSTotal":22751,"peakVramGb":162.6,"gpuPowerWatts":[764.8],"totalPowerWatts":764.8,"hardwareCost":null,"createdAt":"2026-09-06T04:49:09.352Z","notes":"Rented 1x NVIDIA B200 (179 GB) on Lium (lium.io). vLLM 0.28.0 (torch 2.13+cu130), official Qwen FP8 checkpoint, --max-num-seqs 256, aggregate throughput at concurrency 256 x batch 1 (each request = canonical reasoning-v1 prompt with unique nonce, greedy, max_tokens 512), 2 warmups + 3 timed iterations (median). Power = mean nvidia-smi power.draw over active samples of the timed window.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Here's a thinking process:\n\n1.  **Analyze User Input:**\n   - **Context:** Small robotics club planning a weekend build.\n   - **Part 1:** Math problem. 7 members, budget $412. Costs: motor driver $18.50, microcontroller $23.75. Each member wants one of each (so 7 motor drivers, 7 microcontrollers). Plus a shared 3D-printer spool at $27.90. After buying everything, split remainder evenly on batteries at $6.35 each. Find: total batteries bought, money left over.\n   - **Part 2:** Physics/Engineering question. Alice vs Ben on wheel size: torque vs speed. Explain who's right, the tradeoff, and how gear ratio changes it.\n   - **Part 3:** Procedure for safely testing a new motor circuit for the first time (6-9 steps, plain English, include pre-power checks).\n   - **Part 4:** Propose one creative, unusual feature for the robot. Give two concrete risks and mitigation for each.\n   - **Format:** Answer all four parts in order with clear headings.\n\n   - **Constraint Check:** The prompt starts with a nonce/cache-bust string. This is likely just a system prompt artifact or a test. I should ignore it and focus on the actual request. No special constraints beyond the explicit ones.\n\n2.  **Part 1: Math Calculation**\n   - Budget: $412.00\n   - Costs per member: 1 motor driver ($18.50) + 1 microcontroller ($23.75) = $42.25 per member\n   - For 7 members: 7 * $42.25 = $295.75\n   - Shared spool: $27.90\n   - Total initial cost: $295.75 + $27.90 = $323.65\n   - Remainder: $412.00 - $323.65 = $88.35\n   - Battery cost: $6.35 each\n   - Number of batteries: $88.35 / $6.35 = 13.913... -> They can buy 13 batteries (must be whole batteries","engineFlags":{"commandSnippet":"vllm serve /workspace/models/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen/Qwen3.6-35B-A3B-FP8 --host 127.0.0.1 --port 8000 --max-model-len 32768 --max-num-seqs 256 --gpu-memory-utilization 0.90 --enable-prefix-caching --limit-mm-per-prompt '{\"image\":0,\"video\":0}'","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":0.9,"prefixCaching":true,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3.6-35B-A3B-FP8","displayName":"Qwen3.6-35B-A3B-FP8","family":"Qwen","params":36,"activeParams":3,"isMoE":true,"baseModel":{"hfId":"Qwen/Qwen3.6-35B-A3B","displayName":"Qwen3.6-35B-A3B","params":36,"activeParams":3,"isMoE":true}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"B200","gpuCount":1,"vramGb":192,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel Xeon Platinum 8592+ (26 vCPU)","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.28.0","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"FP8","backend":"cuda"},"user":{"id":"cmtp9pehf02gxn7015ngw1zyn","username":"gpu-rentals","verified":true,"verifiedAt":"2026-09-06T04:45:49.493Z","pro":false},"hardwareGroupLabel":"B200","hardwareGroupKey":"DISCRETE_GPU:b200","rank":4,"reactionCounts":{},"myEmoji":null},{"id":"cmtpc3rtv02i2n7016ygh4wrk","modelRevision":"main","promptTokens":304,"outputTokens":512,"contextLength":32768,"prefillTokens":0,"batchSize":128,"ttftMs":437.67,"tokSOut":12011.8,"tokSPrefill":699.6,"tokSTotal":18692,"peakVramGb":161.5,"gpuPowerWatts":[741.3],"totalPowerWatts":741.3,"hardwareCost":null,"createdAt":"2026-09-06T04:51:23.683Z","notes":"Rented 1x NVIDIA B200 (179 GB) on Lium (lium.io), PyTorch CUDA Ubuntu 24.04 template. vLLM 0.28.0 (torch 2.13+cu130, FA4 attention, full CUDA graphs), official google/gemma-4-26B-A4B-it BF16 weights (no quantization), --max-model-len 32768 --max-num-seqs 256 --gpu-memory-utilization 0.90. Canonical LocalMaxxing reasoning-v1 prompt with per-request nonce, greedy, 2 warmups + timed iterations (median). Power = mean nvidia-smi power.draw over active samples of the timed window; peakVramGb is nvidia-smi memory.used incl. vLLM's preallocated KV pool. Aggregate throughput at concurrency 128 x batch 1, max_tokens 512, 3 iterations.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 128).","concurrency must be 1 (got 128)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"### 1. Budget and Battery Calculation\n\n**Reasoning:**\n*   **Individual Costs:** Each member needs one motor driver ($18.50) and one microcontroller ($23.75).\n    *   $18.50 + $23.75 = $42.25 per member.\n*   **Total Member Costs:** There are 7 members.\n    *   $42.25 \\times 7 = $295.75.\n*   **Shared Cost:** One 3D-printer spool.\n    *   $295.75 + $27.90 = $323.65.\n*   **Remaining Budget:**\n    *   $412.00 - $323.65 = $88.35.\n*   **Battery Calculation:** Batteries cost $6.35 each.\n    *   $88.35 \\div 6.35 \\approx 13.91$.\n    *   Since they cannot buy a fraction of a battery, they can buy **13 batteries**.\n*   **Leftover Money:**\n    *   $88.35 - (13 \\times 6.35) = $88.35 - $82.55 = $5.80.\n\n**Results:**\n*   **Total batteries:** 13\n*   **Money left over:** $5.80\n\n***\n\n### 2. The Wheel Size Debate\n\n**Who is right?**\n**Ben is right.** A larger wheel increases the distance traveled per single rotation of the motor, which results in higher linear speed.\n\n**The Tradeoff:**\nThe actual tradeoff is between **Speed and Torque**. \n*   **Larger wheels** increase the \"lever arm\" (the radius) from the axle to the ground. This requires more force to turn, meaning you lose effective torque at the ground. The robot will move faster but will struggle to climb obstacles or accelerate from a standstill.\n*   **Smaller wheels** provide more torque (easier to turn/climb) but result in a lower top speed.\n\n**The Role of Gear Ratio:**\nGear ratios can mitigate or amplify these effects. If you use a larger wheel (which reduces torque) but also implement a **reduction gear ratio** (","engineFlags":{"commandSnippet":"vllm serve /workspace/models/gemma-4-26B-A4B-it --served-model-name google/gemma-4-26B-A4B-it --host 127.0.0.1 --port 8000 --max-model-len 32768 --max-num-seqs 256 --gpu-memory-utilization 0.90 --enable-prefix-caching --limit-mm-per-prompt '{\"image\":0,\"video\":0,\"audio\":0}'","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":0.9,"prefixCaching":true,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":256,"attentionBackend":"flash_attn","flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"google/gemma-4-26B-A4B-it","displayName":"gemma-4-26B-A4B-it","family":"Gemma","params":27,"activeParams":4,"isMoE":true,"baseModel":{"hfId":"google/gemma-4-26B-A4B","displayName":"gemma-4-26B-A4B","params":27,"activeParams":4,"isMoE":true}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"B200","gpuCount":1,"vramGb":192,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel Xeon Platinum 8592+ (26 vCPU)","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.28.0","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"BF16","backend":"cuda"},"user":{"id":"cmtp9pehf02gxn7015ngw1zyn","username":"gpu-rentals","verified":true,"verifiedAt":"2026-09-06T04:45:49.493Z","pro":false},"hardwareGroupLabel":"B200","hardwareGroupKey":"DISCRETE_GPU:b200","rank":5,"reactionCounts":{},"myEmoji":null},{"id":"cmtpo9v4802vvn701bdaf2hih","modelRevision":"main","promptTokens":304,"outputTokens":512,"contextLength":32768,"prefillTokens":0,"batchSize":256,"ttftMs":1068.19,"tokSOut":12005.2,"tokSPrefill":283.7,"tokSTotal":18806.7,"peakVramGb":126.7,"gpuPowerWatts":[622.7],"totalPowerWatts":622.7,"hardwareCost":null,"createdAt":"2026-09-06T10:32:03.273Z","notes":"Rented 1x NVIDIA H200 SXM (141 GB) on Lium (lium.io), PyTorch CUDA Ubuntu 24.04 template. vLLM 0.28.0, torch 2.13.0+cu130, official Qwen FP8 checkpoint, canonical LocalMaxxing reasoning-v1 prompt, greedy, 256 concurrent streams (aggregate throughput = sum of per-stream tok/s), max_tokens 512, 2 warmups + 3 timed iterations (median). Power = mean nvidia-smi power.draw over active samples of the timed window.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Here's a thinking process:\n\n1.  **Analyze User Input:**\n   - **Context:** Small robotics club planning a weekend build.\n   - **Task 1 (Math/Word Problem):** 7 members, budget $412. Costs: motor driver $18.50, microcontroller $23.75. Each member wants one of each (so 7 motor drivers, 7 microcontrollers). Plus a shared 3D-printer spool at $27.90. After buying everything, split remainder evenly on batteries at $6.35 each. Find: total batteries bought, money left over.\n   - **Task 2 (Physics/Engineering Concept):** Alice vs. Ben on wheel size. Alice: larger wheel = more torque at ground. Ben: larger wheel = more speed. Explain who's right, the tradeoff, and how gear ratio changes it.\n   - **Task 3 (Procedure):** 6-9 step plain-English procedure for safely testing a new motor circuit for the first time, including pre-power checks.\n   - **Task 4 (Creative Feature + Risks):** Propose one unusual creative feature for a club robot project. Give two concrete risks with mitigation for each.\n   - **Format:** Clear headings for each part, answer in order.\n\n   - **Constraint Check:** The prompt starts with a cache-bust nonce. I should ignore it as it's just a technical artifact. I need to ensure all parts are answered carefully with reasoning shown.\n\n2.  **Part 1: Math Calculation**\n   - Budget: $412.00\n   - Costs per person: Motor driver ($18.50) + Microcontroller ($23.75) = $42.25\n   - Number of members: 7\n   - Total for drivers & MCs: 7 * $42.25 = $295.75\n   - Shared spool: $27.90\n   - Total initial cost: $295.75 + $27.90 = $323.65\n   - Remainder: $412.00 - $323.65 = $88.35\n   - Battery cost: $6.35 each\n   - Number of batteries: $88.35 / $6.","engineFlags":{"commandSnippet":"vllm serve /workspace/models/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen/Qwen3.6-35B-A3B-FP8 --host 127.0.0.1 --port 8000 --max-model-len 32768 --max-num-seqs 256 --gpu-memory-utilization 0.90 --enable-prefix-caching --limit-mm-per-prompt '{\"image\":0,\"video\":0}'","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":0.9,"prefixCaching":true,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3.6-35B-A3B-FP8","displayName":"Qwen3.6-35B-A3B-FP8","family":"Qwen","params":36,"activeParams":3,"isMoE":true,"baseModel":{"hfId":"Qwen/Qwen3.6-35B-A3B","displayName":"Qwen3.6-35B-A3B","params":36,"activeParams":3,"isMoE":true}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"H200 SXM","gpuCount":1,"vramGb":141,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel Xeon Platinum 8592+ (24 vCPU)","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.28.0","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"FP8","backend":"cuda"},"user":{"id":"cmtp9pehf02gxn7015ngw1zyn","username":"gpu-rentals","verified":true,"verifiedAt":"2026-09-06T04:45:49.493Z","pro":false},"hardwareGroupLabel":"H200 SXM","hardwareGroupKey":"DISCRETE_GPU:h200 sxm","rank":6,"reactionCounts":{},"myEmoji":null},{"id":"cmtpd8gg002ken701sprp66px","modelRevision":"main","promptTokens":304,"outputTokens":512,"contextLength":32768,"prefillTokens":0,"batchSize":256,"ttftMs":839.6,"tokSOut":9867.8,"tokSPrefill":361.15,"tokSTotal":15492.1,"peakVramGb":72,"gpuPowerWatts":[537.8],"totalPowerWatts":537.8,"hardwareCost":null,"createdAt":"2026-09-06T05:23:01.825Z","notes":"Rented 1x NVIDIA H100 80GB SXM on Lium (lium.io), PyTorch CUDA Ubuntu 24.04 template. vLLM 0.28.0, torch 2.13.0+cu130, official Qwen FP8 checkpoint, canonical LocalMaxxing reasoning-v1 prompt, greedy, 256 concurrent streams (aggregate throughput = sum of per-stream tok/s), max_tokens 512, 2 warmups + 3 timed iterations (median). Power = mean nvidia-smi power.draw over active samples of the timed window.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Here's a thinking process:\n\n1.  **Analyze User Input:**\n   - **Context:** Small robotics club planning a weekend build.\n   - **Part 1:** Math problem. 7 members, budget $412. Costs: motor driver $18.50, microcontroller $23.75. Each member wants one of each (so 7 motor drivers, 7 microcontrollers). Plus a shared 3D-printer spool at $27.90. After buying everything, split remainder evenly on batteries at $6.35 each. Question: How many batteries total? How much money left over?\n   - **Part 2:** Physics/Engineering concept. Alice vs Ben on wheel size: torque vs speed. Explain who's right, the tradeoff, and how gear ratio changes it.\n   - **Part 3:** Procedure. 6-9 steps, plain English, safely testing a new motor circuit for the first time, including pre-power checks.\n   - **Part 4:** Creative feature proposal. Unusual for a club project. Two concrete risks with mitigation for each.\n   - **Format:** Clear headings, answer all four parts in order.\n\n   - **Constraint Check:** The prompt starts with a nonce/cache-bust string. This is likely just a system prompt artifact or a test. I should ignore it and focus on the actual request. No special constraints beyond the explicit ones.\n\n2.  **Part 1: Math Calculation**\n   - Budget: $412.00\n   - Costs per person: Motor driver ($18.50) + Microcontroller ($23.75) = $42.25\n   - For 7 members: 7 * $42.25 = $295.75\n   - Shared spool: $27.90\n   - Total initial cost: $295.75 + $27.90 = $323.65\n   - Remainder: $412.00 - $323.65 = $88.35\n   - Battery cost: $6.35 each\n   - Number of batteries: $88.35 / $6.35 = 13.913...\n   - Since they can't buy partial batteries, they can buy 1","engineFlags":{"commandSnippet":"vllm serve /workspace/models/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen/Qwen3.6-35B-A3B-FP8 --host 127.0.0.1 --port 8000 --max-model-len 32768 --max-num-seqs 256 --gpu-memory-utilization 0.90 --enable-prefix-caching --limit-mm-per-prompt '{\"image\":0,\"video\":0}'","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":0.9,"prefixCaching":true,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3.6-35B-A3B-FP8","displayName":"Qwen3.6-35B-A3B-FP8","family":"Qwen","params":36,"activeParams":3,"isMoE":true,"baseModel":{"hfId":"Qwen/Qwen3.6-35B-A3B","displayName":"Qwen3.6-35B-A3B","params":36,"activeParams":3,"isMoE":true}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"H100","gpuCount":1,"vramGb":80,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel Xeon Platinum 8468 (20 vCPU)","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.28.0","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"FP8","backend":"cuda"},"user":{"id":"cmtp9pehf02gxn7015ngw1zyn","username":"gpu-rentals","verified":true,"verifiedAt":"2026-09-06T04:45:49.493Z","pro":false},"hardwareGroupLabel":"H100","hardwareGroupKey":"DISCRETE_GPU:h100","rank":7,"reactionCounts":{},"myEmoji":null},{"id":"cmtpc574p02i5n701h59zlznq","modelRevision":"main","promptTokens":304,"outputTokens":512,"contextLength":32768,"prefillTokens":0,"batchSize":128,"ttftMs":617.76,"tokSOut":9790.9,"tokSPrefill":493.5,"tokSTotal":15277.2,"peakVramGb":162.2,"gpuPowerWatts":[662.5],"totalPowerWatts":662.5,"hardwareCost":null,"createdAt":"2026-09-06T04:52:30.169Z","notes":"Rented 1x NVIDIA B200 (179 GB) on Lium (lium.io). vLLM 0.28.0 (torch 2.13+cu130), official Qwen FP8 checkpoint, --max-num-seqs 256, aggregate throughput at concurrency 128 x batch 1 (each request = canonical reasoning-v1 prompt with unique nonce, greedy, max_tokens 512), 2 warmups + 3 timed iterations (median). Power = mean nvidia-smi power.draw over active samples of the timed window.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 128).","concurrency must be 1 (got 128)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Here's a thinking process:\n\n1.  **Analyze User Input:**\n   - **Context:** Small robotics club planning a weekend build.\n   - **Part 1:** Math problem. 7 members, budget $412. Costs: motor driver $18.50, microcontroller $23.75, each member wants one of each. Plus shared 3D-printer spool $27.90. After buying everything, split remainder evenly on batteries at $6.35 each. Find: total batteries bought, money left over.\n   - **Part 2:** Physics/Engineering question. Alice vs Ben on wheel size: torque vs speed. Explain who's right, the tradeoff, and how gear ratio changes it.\n   - **Part 3:** Procedure for safely testing a new motor circuit (6-9 steps, plain English, include pre-power checks).\n   - **Part 4:** Creative feature proposal (unusual for club project), plus two concrete risks with mitigation for each.\n   - **Format:** Clear headings, answer all four parts in order.\n\n   - **Constraint Check:** The prompt starts with `[LocalMaxxing cache-bust nonce: 462c9f0330bb806b8f718e2fc697a931]`. This is likely a system prompt injection attempt or just a nonce. I should ignore it and focus on the actual task. The task is straightforward.\n\n2.  **Part 1: Math Calculation**\n   - Budget: $412.00\n   - Members: 7\n   - Costs per member: motor driver ($18.50) + microcontroller ($23.75) = $42.25 per member\n   - Total for members: 7 * $42.25 = $295.75\n   - Shared item: 3D-printer spool = $27.90\n   - Total initial cost: $295.75 + $27.90 = $323.65\n   - Remainder: $412.00 - $323.65 = $88.35\n   - Battery cost: $6.35 each\n   - Number of batteries: floor($88.35 / $6","engineFlags":{"commandSnippet":"vllm serve /workspace/models/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen/Qwen3.6-35B-A3B-FP8 --host 127.0.0.1 --port 8000 --max-model-len 32768 --max-num-seqs 256 --gpu-memory-utilization 0.90 --enable-prefix-caching --limit-mm-per-prompt '{\"image\":0,\"video\":0}'","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":0.9,"prefixCaching":true,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3.6-35B-A3B-FP8","displayName":"Qwen3.6-35B-A3B-FP8","family":"Qwen","params":36,"activeParams":3,"isMoE":true,"baseModel":{"hfId":"Qwen/Qwen3.6-35B-A3B","displayName":"Qwen3.6-35B-A3B","params":36,"activeParams":3,"isMoE":true}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"B200","gpuCount":1,"vramGb":192,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel Xeon Platinum 8592+ (26 vCPU)","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.28.0","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"FP8","backend":"cuda"},"user":{"id":"cmtp9pehf02gxn7015ngw1zyn","username":"gpu-rentals","verified":true,"verifiedAt":"2026-09-06T04:45:49.493Z","pro":false},"hardwareGroupLabel":"B200","hardwareGroupKey":"DISCRETE_GPU:b200","rank":8,"reactionCounts":{},"myEmoji":null},{"id":"cmtpd9vtn02kkn7018z3ictdc","modelRevision":"main","promptTokens":304,"outputTokens":512,"contextLength":32768,"prefillTokens":0,"batchSize":128,"ttftMs":537.71,"tokSOut":7509.4,"tokSPrefill":564.4,"tokSTotal":11705.2,"peakVramGb":72,"gpuPowerWatts":[493.4],"totalPowerWatts":493.4,"hardwareCost":null,"createdAt":"2026-09-06T05:24:08.411Z","notes":"Rented 1x NVIDIA H100 80GB SXM on Lium (lium.io), PyTorch CUDA Ubuntu 24.04 template. vLLM 0.28.0, torch 2.13.0+cu130, official Qwen FP8 checkpoint, canonical LocalMaxxing reasoning-v1 prompt, greedy, 128 concurrent streams (aggregate throughput = sum of per-stream tok/s), max_tokens 512, 2 warmups + 3 timed iterations (median). Power = mean nvidia-smi power.draw over active samples of the timed window.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 128).","concurrency must be 1 (got 128)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Here's a thinking process:\n\n1.  **Analyze User Input:**\n   - **Context:** Small robotics club planning a weekend build.\n   - **Part 1:** Math problem. 7 members, budget $412. Costs: motor driver $18.50, microcontroller $23.75. Each member wants one of each (so 7 motor drivers, 7 microcontrollers). Plus a shared 3D-printer spool at $27.90. After buying everything, split remainder evenly on batteries at $6.35 each. Question: How many batteries total? How much money left over?\n   - **Part 2:** Physics/Engineering question. Alice vs Ben on wheel size: larger wheel = more torque at ground vs more speed. Explain who's right, the tradeoff, and how gear ratio changes it.\n   - **Part 3:** Procedure: 6-9 steps for safely testing a new motor circuit for the first time, including pre-power checks.\n   - **Part 4:** Creative feature proposal: unusual for a club project, plus two concrete risks with mitigation for each.\n   - **Format:** Clear headings, answer all four parts in order.\n\n   - **Constraint Check:** The prompt starts with a nonce/cache-bust string. This is likely just a system prompt artifact or a test. I should ignore it and focus on the actual request. No special constraints beyond the prompt itself.\n\n2.  **Part 1: Math Calculation**\n   - Budget: $412.00\n   - Costs per member: 1 motor driver ($18.50) + 1 microcontroller ($23.75) = $42.25\n   - For 7 members: 7 * $42.25 = $295.75\n   - Shared spool: $27.90\n   - Total initial cost: $295.75 + $27.90 = $323.65\n   - Remainder: $412.00 - $323.65 = $88.35\n   - Battery cost: $6.35 each\n   - Number of batteries: floor($88.35 / $6.35) = ?\n     - 6.35 * 10 =","engineFlags":{"commandSnippet":"vllm serve /workspace/models/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen/Qwen3.6-35B-A3B-FP8 --host 127.0.0.1 --port 8000 --max-model-len 32768 --max-num-seqs 256 --gpu-memory-utilization 0.90 --enable-prefix-caching --limit-mm-per-prompt '{\"image\":0,\"video\":0}'","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":0.9,"prefixCaching":true,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3.6-35B-A3B-FP8","displayName":"Qwen3.6-35B-A3B-FP8","family":"Qwen","params":36,"activeParams":3,"isMoE":true,"baseModel":{"hfId":"Qwen/Qwen3.6-35B-A3B","displayName":"Qwen3.6-35B-A3B","params":36,"activeParams":3,"isMoE":true}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"H100","gpuCount":1,"vramGb":80,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel Xeon Platinum 8468 (20 vCPU)","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.28.0","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"FP8","backend":"cuda"},"user":{"id":"cmtp9pehf02gxn7015ngw1zyn","username":"gpu-rentals","verified":true,"verifiedAt":"2026-09-06T04:45:49.493Z","pro":false},"hardwareGroupLabel":"H100","hardwareGroupKey":"DISCRETE_GPU:h100","rank":9,"reactionCounts":{},"myEmoji":null},{"id":"cmtpohppj02w1n701g2k87gbb","modelRevision":"main","promptTokens":308,"outputTokens":512,"contextLength":32768,"prefillTokens":0,"batchSize":128,"ttftMs":619.02,"tokSOut":7172.4,"tokSPrefill":497.3,"tokSTotal":11284.4,"peakVramGb":128.2,"gpuPowerWatts":[658.4],"totalPowerWatts":658.4,"hardwareCost":null,"createdAt":"2026-09-06T10:38:09.511Z","notes":"Rented 1x NVIDIA H200 SXM (141 GB) on Lium (lium.io), PyTorch CUDA Ubuntu 24.04 template. vLLM 0.28.0, torch 2.13.0+cu130, official NVIDIA BF16 checkpoint (hybrid Mamba2/attention MoE, 3B active), canonical LocalMaxxing reasoning-v1 prompt, greedy, 128 concurrent streams (aggregate throughput = sum of per-stream tok/s), max_tokens 512, 2 warmups + 3 timed iterations (median). Power = mean nvidia-smi power.draw over active samples of the timed window.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 128).","concurrency must be 1 (got 128)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Here's a thinking process:\n\n1.  **Analyze the User's Request:**\n   - The user provides a \"LocalMaxxing cache-bust nonce: 9b4fe1c5fc860bf564d244b31131ed69\" at the top. This looks like some kind of tracking/hash/identifier, possibly from a system or template. I should ignore it or note it but not let it affect the actual content.\n   - The user wants me to answer 4 distinct parts in order, with clear headings.\n   - Part 1: A math/word problem about a robotics club budget.\n   - Part 2: A conceptual question about wheel size, torque, speed, and gear ratios.\n   - Part 3: A procedural write-up for testing a motor circuit safely.\n   - Part 4: A creative feature proposal with two risks and mitigations.\n\n   I need to work through each part carefully, show reasoning, and format answers with clear headings.\n\n2.  **Part 1: Budget Problem**\n   - Club has 7 members, budget $412.\n   - Costs: motor driver $18.50, microcontroller $23.75.\n   - Each member wants one of each (motor driver + microcontroller) PLUS a shared 3D-printer spool at $27.90.\n   - After buying everything, they split the remainder evenly on batteries priced at $6.35 each.\n   - Question: How many batteries can they buy in total, and how much money is left over?\n\n   Let's break it down:\n   - 7 members.\n   - Each member gets: 1 motor driver + 1 microcontroller + shares in the 3D-printer spool? Wait, \"plus a shared 3D-printer spool at 27.90.\" The phrasing: \"each member wants one of each plus a shared 3D-printer spool at 27.90.\" This could mean: each member wants one motor driver and one microcontroller, and there's a shared 3D-printer spool that costs $27.90 total (shared among all), or each member wants one of each plus an individual spool? \"shared 3D-printer spool at 27.90\" suggests the spool is shared, costing $2","engineFlags":{"commandSnippet":"vllm serve /workspace/models/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 --served-model-name nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 --host 127.0.0.1 --port 8000 --max-model-len 32768 --max-num-seqs 128 --gpu-memory-utilization 0.92 --enable-prefix-caching","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":0.92,"prefixCaching":true,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":128,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16","displayName":"NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16","family":null,"params":32,"activeParams":3,"isMoE":true,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"H200 SXM","gpuCount":1,"vramGb":141,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel Xeon Platinum 8592+ (24 vCPU)","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.28.0","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"BF16","backend":"cuda"},"user":{"id":"cmtp9pehf02gxn7015ngw1zyn","username":"gpu-rentals","verified":true,"verifiedAt":"2026-09-06T04:45:49.493Z","pro":false},"hardwareGroupLabel":"H200 SXM","hardwareGroupKey":"DISCRETE_GPU:h200 sxm","rank":10,"reactionCounts":{},"myEmoji":null},{"id":"cmtpe90yu02lsn701t2u5yd2j","modelRevision":"main","promptTokens":307,"outputTokens":512,"contextLength":32768,"prefillTokens":0,"batchSize":128,"ttftMs":917.86,"tokSOut":6677.4,"tokSPrefill":335.5,"tokSTotal":10460.6,"peakVramGb":85.5,"gpuPowerWatts":[420.3],"totalPowerWatts":420.3,"hardwareCost":null,"createdAt":"2026-09-06T05:51:28.039Z","notes":"Rented 1x NVIDIA RTX PRO 6000 Blackwell (96 GB) on Lium (lium.io), PyTorch CUDA Ubuntu 24.04 template. vLLM 0.28.0, torch 2.13.0+cu130, official NVIDIA NVFP4 checkpoint, no speculative decoding, canonical LocalMaxxing reasoning-v1 prompt, greedy, 128 concurrent streams (aggregate throughput = sum of per-stream tok/s), max_tokens 512, 2 warmups + 3 timed iterations (median). Power = mean nvidia-smi power.draw over active samples of the timed window.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 128).","concurrency must be 1 (got 128)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Here's a thinking process:\n\n1.  **Analyze the User Input:**\n   - There's a cache-bust nonce at the top: `[LocalMaxxing cache-bust nonce: 48c41e4b39526bcba35c3011797ed0dd]` - this seems like metadata, probably not part of the actual question, but I should acknowledge it or just ignore it and focus on the content.\n   - The user wants me to answer 4 distinct parts in order, with clear headings.\n   - Part 1: A math/word problem about a robotics club budget.\n   - Part 2: Physics/engineering question about wheel size, torque, speed, and gear ratios.\n   - Part 3: A procedural write-up for testing a motor circuit safely.\n   - Part 4: A creative feature proposal with two risks and mitigations.\n\n   I need to work through each carefully, show reasoning, and answer in order with clear headings.\n\n2.  **Part 1: Budget Problem**\n   - Club has 7 members, budget $412.\n   - Costs: motor driver $18.50, microcontroller $23.75.\n   - Each member wants one of each (so 7 motor drivers, 7 microcontrollers).\n   - Plus a shared 3D-printer spool at $27.90.\n   - After buying everything, they split the remainder evenly on batteries priced at $6.35 each.\n   - Question: How many batteries can they buy in total, and how much money is left over?\n\n   Let's calculate step by step:\n   - Number of members = 7\n   - Cost per member for motor driver + microcontroller = 18.50 + 23.75 = 42.25\n   - Total for 7 members = 42.25 * 7 = 295.75\n   - Add shared spool: 295.75 + 27.90 = 323.65\n   - Total budget = 412.00\n   - Remainder = 412.00 - 323.65 = 88.35\n   - Batteries cost $6.35 each.\n   - How many batteries can they","engineFlags":{"commandSnippet":"vllm serve /workspace/models/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 --served-model-name nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 --host 127.0.0.1 --port 8000 --max-model-len 32768 --max-num-seqs 128 --gpu-memory-utilization 0.90 --enable-prefix-caching --async-scheduling --mamba-backend flashinfer --mamba-ssm-cache-dtype float16","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":0.9,"prefixCaching":true,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":128,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4","displayName":"NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4","family":"Opt","params":18,"activeParams":3,"isMoE":true,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"RTX PRO 6000 Blackwell","gpuCount":1,"vramGb":96,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 9355 (16 vCPU)","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.28.0","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"NVFP4","backend":"cuda"},"user":{"id":"cmtp9pehf02gxn7015ngw1zyn","username":"gpu-rentals","verified":true,"verifiedAt":"2026-09-06T04:45:49.493Z","pro":false},"hardwareGroupLabel":"RTX PRO 6000 Blackwell","hardwareGroupKey":"DISCRETE_GPU:rtx pro 6000 blackwell","rank":11,"reactionCounts":{},"myEmoji":null},{"id":"cmvcdswiz06qile01luogasgp","modelRevision":"5dd22602c2e9f6a097b1de4c4efe0658b605015c","promptTokens":283,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":256,"ttftMs":1355.27,"tokSOut":6369.5,"tokSPrefill":210.05,"tokSTotal":13318.3,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-17T12:37:20.171Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 6369.5 tok/s is the aggregate decode rate across all 256 streams, about 24.9 tok/s per stream, and the 1355 ms time to first token is under that load. Setup: KASA B65 rig, 4 x Intel Arc Pro B65, tensor parallel across 4 cards; vLLM 0.32.0rc1.dev100+g77154ca61 (vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989); max_num_seqs 256, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (283 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 6381, 6356.6, 6369.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config lfm8-u-tp4dp1-m0-c256, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"<think>\nWe need to answer four parts, with clear headings. Provide calculations, reasoning. No extra commentary. Must not use markdown unless asked. The user didn't ask for markdown, so we should avoid markdown formatting. Use plain text headings maybe like \"1. Budget Calculation\". Provide step-by-step. Ensure no bold/italic. Provide final leftover amount after battery purchase. Also include safety procedure steps (6-9). Then propose creative feature and two risks with mitigation.\n\nWe must ensure we follow constraints: no extra notes, no disclaimer. Provide answer directly.\n\nLet's compute:\n\nMembers = 7. Each member wants: motor driver $18.50, microcontroller $23.75, 3D printer spool $27.90. So per member cost = 18.50 + 23.75 + 27.90 = 70.15? Let's add: 18.5+23.75=42.25; +27.9 = 70.15. For 7 members: 70.15 * 7 = 491.05. Budget is $412, which is less than total cost! Wait, maybe they have budget $412, but total cost exceeds budget. Perhaps they will not","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-lfm8-u-tp4dp1-m0-c256 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s12b/arms/lfm8-u-tp4dp1-m0-c256/session:/bench -e ZE_AFFINITY_MASK=0\\,1\\,2\\,3 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4 serve --model /llm/models/LFM2.5-8B-A1B --host 0.0.0.0 --served-model-name lfm8 --tensor-parallel-size 4 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.92 --max-model-len 4096 --max-num-seqs 256 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype auto --dtype bfloat16 --no-enable-prefix-caching --trust-remote-code --cpu-offload-gb 0 --port 8000 ","tensorParallel":4,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.92,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"LiquidAI/LFM2.5-8B-A1B","displayName":"LFM2.5-8B-A1B","family":null,"params":8,"activeParams":null,"isMoE":false,"baseModel":{"hfId":"LiquidAI/LFM2.5-8B-A1B-Base","displayName":"LFM2.5-8B-A1B-Base","params":8,"activeParams":1,"isMoE":true}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":4,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.32.0rc1.dev100+g77154ca61","engineRepository":"https://github.com/vllm-project/vllm","engineBuild":"vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4","engineCommit":null,"quantization":"BF16","backend":"xpu"},"user":{"id":"cmu3irkjc06dalq01d47hl5ok","username":"imthekaiser","verified":true,"verifiedAt":"2026-10-14T19:10:11.982Z","pro":false},"hardwareGroupLabel":"Intel Arc Pro B65","hardwareGroupKey":"DISCRETE_GPU:intel arc pro b65","rank":12,"reactionCounts":{},"myEmoji":null},{"id":"cmvcdstyy06qfle014j57u7uq","modelRevision":"5dd22602c2e9f6a097b1de4c4efe0658b605015c","promptTokens":283,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":192,"ttftMs":674.73,"tokSOut":6336.8,"tokSPrefill":418.95,"tokSTotal":13197.9,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-17T12:37:16.858Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 192 requests sent at once (batch 192, lmx --concurrency 192); 6336.8 tok/s is the aggregate decode rate across all 192 streams, about 33.0 tok/s per stream, and the 675 ms time to first token is under that load. Setup: KASA B65 rig, 3 x Intel Arc Pro B65, 3 independent one-card vLLM servers (one per card; identical but for card and port) behind a round-robin nginx router; the command shown is the first server's; vLLM 0.32.0rc1.dev100+g77154ca61 (vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989); max_num_seqs 64 per replica, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (283 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 192: 6206.5, 6378.7, 6336.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config lfm8-u-tp1r3-m0-c192, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 192).","concurrency must be 1 (got 192)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"<think>\nWe need to answer four parts, with clear headings. Provide calculations, reasoning. No extra commentary. Must not use markdown unless asked. The user didn't ask for markdown, so we should avoid markdown formatting. Use plain text headings maybe like \"1. Budget Calculation\". Provide step-by-step. Ensure no bold/italic. Provide final leftover amount. Also include safety procedure steps (6-9). Then propose creative feature with two risks and mitigations.\n\nWe must ensure we follow constraints: no extra notes, no disclaimer. Provide answer directly.\n\nLet's compute part 1:\n\nMembers = 7. Each member wants a motor driver (18.50), microcontroller (23.75), and a shared 3D-printer spool at 27.90. So per member cost: driver + microcontroller + spool? Actually \"each member wants one of each plus a shared 3D-printer spool\" Means each member gets their own driver and microcontroller, and there is a shared spool for all. So total cost for drivers: 7 * 18.50 = 129.5. Microcontrollers: 7 * 23.75 = 166.25. Spool cost: only","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-lfm8-u-tp1r3-m0-c192-r0 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s12b/arms/lfm8-u-tp1r3-m0-c192/session:/bench -e ZE_AFFINITY_MASK=0 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4 serve --model /llm/models/LFM2.5-8B-A1B --host 0.0.0.0 --served-model-name lfm8 --tensor-parallel-size 1 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.92 --max-model-len 4096 --max-num-seqs 64 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype auto --dtype bfloat16 --no-enable-prefix-caching --trust-remote-code --cpu-offload-gb 0 --port 8001 ","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.92,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":64,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"LiquidAI/LFM2.5-8B-A1B","displayName":"LFM2.5-8B-A1B","family":null,"params":8,"activeParams":null,"isMoE":false,"baseModel":{"hfId":"LiquidAI/LFM2.5-8B-A1B-Base","displayName":"LFM2.5-8B-A1B-Base","params":8,"activeParams":1,"isMoE":true}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":3,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.32.0rc1.dev100+g77154ca61","engineRepository":"https://github.com/vllm-project/vllm","engineBuild":"vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4","engineCommit":null,"quantization":"BF16","backend":"xpu"},"user":{"id":"cmu3irkjc06dalq01d47hl5ok","username":"imthekaiser","verified":true,"verifiedAt":"2026-10-14T19:10:11.982Z","pro":false},"hardwareGroupLabel":"Intel Arc Pro B65","hardwareGroupKey":"DISCRETE_GPU:intel arc pro b65","rank":13,"reactionCounts":{},"myEmoji":null},{"id":"cmsxs32ai0chxms01j90izclk","modelRevision":"main","promptTokens":512,"outputTokens":64,"contextLength":2048,"prefillTokens":null,"batchSize":1,"ttftMs":70,"tokSOut":6319.713109,"tokSPrefill":7310.920391,"tokSTotal":7185.7,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-08-17T22:01:11.515Z","notes":null,"verifiedRun":false,"verificationIssues":[],"promptSha256":null,"promptSample":null,"outputSample":null,"engineFlags":{"commandSnippet":"E:\\llama.cpp-b10470\\llama-bench.exe -m E:\\huggingface\\hub\\models--afrideva--Tinystories-gpt-0.1-3m-GGUF\\snapshots\\3cad9ac61b9fa06f2647b6b77d38640bfa1766aa\\tinystories-gpt-0.1-3m.Q4_K_M.gguf -ngl 0 -t 4 -lm mlock --poll 100 --prio 3 --delay 2 -p 512 -n 64 -r 20 -o 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Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Here's a thinking process:\n\n1.  **Analyze User Input:**\n   - **Context:** Small robotics club planning a weekend build.\n   - **Part 1:** Math problem. 7 members, budget $412. Costs: motor driver $18.50, microcontroller $23.75. Each member wants one of each (so 7 motor drivers, 7 microcontrollers). Plus a shared 3D printer spool at $27.90. After buying everything, split remainder evenly on batteries at $6.35 each. Question: How many batteries total? How much money left over?\n   - **Part 2:** Physics/Engineering question. Alice vs Ben on wheel size: larger wheel = more torque at ground? or more speed? Explain who's right, the tradeoff, and how gear ratio changes it.\n   - **Part 3:** Procedure: 6-9 steps, plain English, safely testing a new motor circuit for the first time, including pre-power checks.\n   - **Part 4:** Creative feature proposal: unusual for a club project, plus two concrete risks with mitigation for each.\n   - **Format:** Clear headings for each part, answer in order.\n\n   - **Constraint Check:** The prompt starts with a nonce/cache-bust string. This is likely just a system prompt artifact or a test. I should ignore it and focus on the actual request. No special constraints beyond what's stated.\n\n2.  **Part 1: Math Calculation**\n   - Budget: $412.00\n   - Costs per member: motor driver ($18.50) + microcontroller ($23.75) = $42.25\n   - For 7 members: 7 * $42.25 = $295.75\n   - Shared spool: $27.90\n   - Total initial cost: $295.75 + $27.90 = $323.65\n   - Remainder: $412.00 - $323.65 = $88.35\n   - Battery cost: $6.35 each\n   - Number of batteries: $88.35 / $6.35 = 13.913...\n   - Since they can only buy whole","engineFlags":{"commandSnippet":"vllm serve /workspace/models/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen/Qwen3.6-35B-A3B-FP8 --host 127.0.0.1 --port 8000 --max-model-len 32768 --max-num-seqs 256 --gpu-memory-utilization 0.90 --enable-prefix-caching --limit-mm-per-prompt '{\"image\":0,\"video\":0}'","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":0.9,"prefixCaching":true,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3.6-35B-A3B-FP8","displayName":"Qwen3.6-35B-A3B-FP8","family":"Qwen","params":36,"activeParams":3,"isMoE":true,"baseModel":{"hfId":"Qwen/Qwen3.6-35B-A3B","displayName":"Qwen3.6-35B-A3B","params":36,"activeParams":3,"isMoE":true}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"B200","gpuCount":1,"vramGb":192,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel Xeon Platinum 8592+ (26 vCPU)","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.28.0","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"FP8","backend":"cuda"},"user":{"id":"cmtp9pehf02gxn7015ngw1zyn","username":"gpu-rentals","verified":true,"verifiedAt":"2026-09-06T04:45:49.493Z","pro":false},"hardwareGroupLabel":"B200","hardwareGroupKey":"DISCRETE_GPU:b200","rank":15,"reactionCounts":{},"myEmoji":null},{"id":"cmsnp1rko00jco001mvymwmsb","modelRevision":"main","promptTokens":0,"outputTokens":64,"contextLength":1024,"prefillTokens":null,"batchSize":112,"ttftMs":120,"tokSOut":6001.4,"tokSPrefill":null,"tokSTotal":null,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-08-10T20:38:30.360Z","notes":"BATCHED AGGREGATE THROUGHPUT — not single-stream decode. DATA-PARALLEL across 4 cards; aggregate of 4 independent replicas. batch=112, concurrency=112, 100 input rows, 3 waves, max_tokens=64, ctx=1024, KV=q8, greedy; TTFT not comparable. Per-card median MODEL tok/s: d0:1495.3, d1:1504.4, d2:1500.1, d3:1501.6 (sum 6001.4); wall-clock aggregate 3889.4 tok/s (per-card wall d0:969.6, d1:975.7, d2:973.2, d3:970.9). MODEL tok/s is sum of per-card decode rates excluding inter-wave gap; wall includes all overhead. Do not compare DP aggregate to single-stream. engine: hipfire 0.3.0+b0bcc3f91506, backend rocm, gfx1201 x4 quant: MQ4R = MagnumQuant 4-bit Redline (graded mixed-tier: MQ4 attn/router/shared + graded MQ4 routed experts, Redline retained-PM4 dispatch), 4.16 bpw effective (18700048128 bytes *8 / 35.95B params) model file: qwen3.6-35b-a3b.mq4r sha256 4685c140c46b1a6f prompt: uniform short-serve jsonl md5 3d22208dcf539818ff2e8341f53474ee, 64 max_new method: median of 3 waves, fresh process per wave, temperature 0, bit-exact outputs (sha ec47dcfb9b0c54e0) evidence: sealed case a3b-mq4r-dp4-b112-gfx1201-r9700-4x, corpus manifest manifest-sha12-8f3c2e1a4b9d not measured: peak VRAM (not instrumented this campaign)","verifiedRun":false,"verificationIssues":[],"promptSha256":null,"promptSample":null,"outputSample":null,"engineFlags":{"commandSnippet":"hipfire-batch-generate --model ~/.hipfire/models/qwen3.6-35b-a3b.mq4r --batch 112 --max-seq 1024 --max-new 64 --devices 0,1,2,3 --data-parallel --kv q8 --fresh-process-per-wave","tensorParallel":null,"gpuLayers":null,"kvCacheDtype":"q8","gpuMemUtil":null,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":112,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3.6-35B-A3B","displayName":"Qwen3.6-35B-A3B","family":"Qwen","params":36,"activeParams":3,"isMoE":true,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Radeon AI Pro R9700","gpuCount":4,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD Ryzen Threadripper 9970X","os":"Ubuntu 26.04","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"hipfire","engineVersion":"0.3.0+b0bcc3f91506","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"MQ4R","backend":"rocm"},"user":{"id":"cmoeye1gq0000le04ie2kqs58","username":"schuttdev","verified":false,"verifiedAt":null,"pro":true},"hardwareGroupLabel":"Radeon AI Pro R9700","hardwareGroupKey":"DISCRETE_GPU:radeon ai pro r9700","rank":16,"reactionCounts":{},"myEmoji":null},{"id":"cmsnp232a00kao001r4wh9920","modelRevision":"main","promptTokens":0,"outputTokens":64,"contextLength":1024,"prefillTokens":null,"batchSize":100,"ttftMs":120,"tokSOut":5993.3,"tokSPrefill":null,"tokSTotal":null,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-08-10T20:38:45.251Z","notes":"BATCHED AGGREGATE THROUGHPUT — not single-stream decode. DATA-PARALLEL across 4 cards; aggregate of 4 independent replicas. batch=100, concurrency=100, 100 input rows, 3 waves, max_tokens=64, ctx=1024, KV=q8, greedy; TTFT not comparable. Per-card median MODEL tok/s: d0:1493.8, d1:1500.3, d2:1499.3, d3:1499.9 (sum 5993.3); wall-clock aggregate 3885.8 tok/s (per-card wall d0:970.3, d1:973.6, d2:972.5, d3:969.4). MODEL tok/s is sum of per-card decode rates excluding inter-wave gap; wall includes all overhead. Do not compare DP aggregate to single-stream. engine: hipfire 0.3.0+b0bcc3f91506, backend rocm, gfx1201 x4 quant: MQ4R = MagnumQuant 4-bit Redline (graded mixed-tier: MQ4 attn/router/shared + graded MQ4 routed experts, Redline retained-PM4 dispatch), 4.16 bpw effective (18700048128 bytes *8 / 35.95B params) model file: qwen3.6-35b-a3b.mq4r sha256 4685c140c46b1a6f prompt: uniform short-serve jsonl md5 3d22208dcf539818ff2e8341f53474ee, 64 max_new method: median of 3 waves, fresh process per wave, temperature 0, bit-exact outputs (sha ec47dcfb9b0c54e0) evidence: sealed case a3b-mq4r-dp4-b100-gfx1201-r9700-4x, corpus manifest manifest-sha12-8f3c2e1a4b9d not measured: peak VRAM (not instrumented this campaign)","verifiedRun":false,"verificationIssues":[],"promptSha256":null,"promptSample":null,"outputSample":null,"engineFlags":{"commandSnippet":"hipfire-batch-generate --model ~/.hipfire/models/qwen3.6-35b-a3b.mq4r --batch 100 --max-seq 1024 --max-new 64 --devices 0,1,2,3 --data-parallel 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Power = mean nvidia-smi power.draw over active samples of the timed window.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 64).","concurrency must be 1 (got 64)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Here's a thinking process:\n\n1.  **Analyze User Input:**\n   - **Context:** Small robotics club planning a weekend build.\n   - **Task 1 (Math/Finance):** 7 members, budget $412. Costs: motor driver $18.50, microcontroller $23.75. Each member wants one of each (so 7 motor drivers, 7 microcontrollers). Plus a shared 3D-printer spool at $27.90. After buying everything, split remainder evenly on batteries at $6.35 each. Find: total batteries bought, money left over.\n   - **Task 2 (Physics/Engineering):** Alice vs. Ben on wheel size. Alice: larger wheel = more torque at ground. Ben: larger wheel = more speed. Explain who's right, the tradeoff, and how gear ratio changes it.\n   - **Task 3 (Procedure):** 6-9 step plain-English procedure for safely testing a new motor circuit for the first time, including pre-power checks.\n   - **Task 4 (Creative/Risk):** One creative feature unusual for a club project, plus two concrete risks with mitigation for each.\n   - **Format:** Clear headings, answer all four parts in order.\n\n   - **Constraint Check:** The prompt starts with a nonce/cache-bust string. This is likely just a system prompt artifact or test. I'll ignore it as it doesn't change the task. I need to be careful with the math in Task 1.\n\n2.  **Task 1: Math/Finance Calculation**\n   - Budget: $412.00\n   - Costs per person: Motor driver ($18.50) + Microcontroller ($23.75) = $42.25\n   - Number of members: 7\n   - Total for members: 7 * $42.25 = $295.75\n   - Shared item: 3D printer spool = $27.90\n   - Total initial cost: $295.75 + $27.90 = $323.65\n   - Remainder: $412.00 - $323.65 = $88.35\n   - Battery cost: $6.35 each\n   - Number of batteries:","engineFlags":{"commandSnippet":"vllm serve /workspace/models/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen/Qwen3.6-35B-A3B-FP8 --host 127.0.0.1 --port 8000 --max-model-len 32768 --max-num-seqs 256 --gpu-memory-utilization 0.90 --enable-prefix-caching --limit-mm-per-prompt '{\"image\":0,\"video\":0}'","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":0.9,"prefixCaching":true,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3.6-35B-A3B-FP8","displayName":"Qwen3.6-35B-A3B-FP8","family":"Qwen","params":36,"activeParams":3,"isMoE":true,"baseModel":{"hfId":"Qwen/Qwen3.6-35B-A3B","displayName":"Qwen3.6-35B-A3B","params":36,"activeParams":3,"isMoE":true}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"H100","gpuCount":1,"vramGb":80,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel Xeon Platinum 8468 (20 vCPU)","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.28.0","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"FP8","backend":"cuda"},"user":{"id":"cmtp9pehf02gxn7015ngw1zyn","username":"gpu-rentals","verified":true,"verifiedAt":"2026-09-06T04:45:49.493Z","pro":false},"hardwareGroupLabel":"H100","hardwareGroupKey":"DISCRETE_GPU:h100","rank":19,"reactionCounts":{},"myEmoji":null},{"id":"cmtpdhzkn02lln701nmxn8ls7","modelRevision":"main","promptTokens":307,"outputTokens":512,"contextLength":32768,"prefillTokens":0,"batchSize":128,"ttftMs":484.68,"tokSOut":5422.7,"tokSPrefill":635.4,"tokSTotal":8540.3,"peakVramGb":72.5,"gpuPowerWatts":[625.7],"totalPowerWatts":625.7,"hardwareCost":null,"createdAt":"2026-09-06T05:30:26.519Z","notes":"Rented 1x NVIDIA H100 80GB SXM on Lium (lium.io), PyTorch CUDA Ubuntu 24.04 template. vLLM 0.28.0, torch 2.13.0+cu130, official NVIDIA BF16 checkpoint (hybrid Mamba2/attention MoE, 3B active), canonical LocalMaxxing reasoning-v1 prompt, greedy, 128 concurrent streams (aggregate throughput = sum of per-stream tok/s), max_tokens 512, 2 warmups + 3 timed iterations (median). 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Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Here's a thinking process:\n\n1.  **Analyze the User's Request:**\n   - The user provides a \"LocalMaxxing cache-bust nonce: 3d94bfe145fb93bc179af9d71aeb3bb3\" at the top. This looks like some kind of tracking/hash tag, but it doesn't seem to affect the actual content I need to produce. I should ignore it or acknowledge it briefly if needed, but the main task is the four parts.\n   - Four distinct parts to answer:\n     a. Math/word problem about a robotics club budget\n     b. Physics/engineering conceptual question about wheel size vs torque/speed\n     c. Procedural writing: testing a motor circuit safely\n     d. Creative feature proposal with risks and mitigations\n   - Instructions: Answer all four parts in order with clear headings.\n\n2.  **Part 1: Budget Problem**\n   - Club has 7 members, budget $412\n   - Costs: motor driver $18.50, microcontroller $23.75\n   - Each member wants one of each (motor driver + microcontroller) PLUS a shared 3D-printer spool at $27.90\n   - After buying everything, split remainder evenly on batteries priced at $6.35 each\n   - Question: How many batteries can they buy in total, and how much money is left over?\n\n   Let me break this down:\n   - 7 members\n   - Each member gets: 1 motor driver + 1 microcontroller + shares in the 3D-printer spool (shared among all 7)\n   - Cost per member for their own items: $18.50 + $23.75 = $42.25\n   - Total for 7 members' individual items: 7 * $42.25 = $295.75\n   - Shared 3D-printer spool: $27.90 (once, shared)\n   - Total spent so far: $295.75 + $27.90 = $323.65\n   - Budget: $412\n   - Remainder: $412 - $323.65 = $88.35\n   - Batteries cost $6","engineFlags":{"commandSnippet":"vllm serve /workspace/models/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 --served-model-name nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 --host 127.0.0.1 --port 8000 --max-model-len 32768 --max-num-seqs 128 --gpu-memory-utilization 0.92 --enable-prefix-caching","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":0.92,"prefixCaching":true,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":128,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16","displayName":"NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16","family":null,"params":32,"activeParams":3,"isMoE":true,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"H100","gpuCount":1,"vramGb":80,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel Xeon Platinum 8468 (20 vCPU)","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.28.0","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"BF16","backend":"cuda"},"user":{"id":"cmtp9pehf02gxn7015ngw1zyn","username":"gpu-rentals","verified":true,"verifiedAt":"2026-09-06T04:45:49.493Z","pro":false},"hardwareGroupLabel":"H100","hardwareGroupKey":"DISCRETE_GPU:h100","rank":20,"reactionCounts":{},"myEmoji":null},{"id":"cmvcdr84006olle01u82bimje","modelRevision":"1cfa9a7208912126459214e8b04321603b3df60c","promptTokens":296,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":256,"ttftMs":1934.45,"tokSOut":5331.7,"tokSPrefill":151.65,"tokSTotal":11449,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-17T12:36:01.872Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 5331.7 tok/s is the aggregate decode rate across all 256 streams, about 20.8 tok/s per stream, and the 1934 ms time to first token is under that load. Setup: KASA B65 rig, 4 x Intel Arc Pro B65, tensor parallel across 4 cards; vLLM 0.32.0rc1.dev100+g77154ca61 (vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989); max_num_seqs 256, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (296 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 5330.9, 5331.7, 5336.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config q34-u-tp4dp1-m0-c256, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"<think>\nOkay, let's tackle this problem step by step. First, I need to figure out how many batteries the club can buy with their remaining money after purchasing all the necessary components. Let me start by listing out all the costs and the total budget.\n\nThe club has 7 members. Each member wants a motor driver and a microcontroller. So, that's 7 motor drivers and 7 microcontrollers. The motor driver costs $18.50 each, and the microcontroller is $23.75 each. Then there's a shared 3D-printer spool at $27.90. \n\nSo, first, calculate the cost for the motor drivers: 7 * 18.50. Let me do that multiplication. 7 * 18 is 126, and 7 * 0.50 is 3.50, so total is 129.50. Then the microcontrollers: 7 * 23.75. Hmm, 7 * 20 is 140, 7 * 3.75 is 26.25, so total is 166.25. Then the ","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-q34-u-tp4dp1-m0-c256 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s12a/arms/q34-u-tp4dp1-m0-c256/session:/bench -e ZE_AFFINITY_MASK=0\\,1\\,2\\,3 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4 serve --model /llm/models/Qwen3-4B --host 0.0.0.0 --served-model-name q34 --tensor-parallel-size 4 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.92 --max-model-len 4096 --max-num-seqs 256 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype auto --dtype bfloat16 --no-enable-prefix-caching --trust-remote-code --cpu-offload-gb 0 --port 8000 ","tensorParallel":4,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.92,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3-4B","displayName":"Qwen3-4B","family":"Qwen","params":4,"activeParams":null,"isMoE":false,"baseModel":{"hfId":"Qwen/Qwen3-4B-Base","displayName":"Qwen3-4B-Base","params":4,"activeParams":null,"isMoE":false}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":4,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.32.0rc1.dev100+g77154ca61","engineRepository":"https://github.com/vllm-project/vllm","engineBuild":"vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4","engineCommit":null,"quantization":"BF16","backend":"xpu"},"user":{"id":"cmu3irkjc06dalq01d47hl5ok","username":"imthekaiser","verified":true,"verifiedAt":"2026-10-14T19:10:11.982Z","pro":false},"hardwareGroupLabel":"Intel Arc Pro B65","hardwareGroupKey":"DISCRETE_GPU:intel arc pro b65","rank":21,"reactionCounts":{},"myEmoji":null},{"id":"cmv9ntx0i00zble01gh3rn5o4","modelRevision":"6cee5e81ee83917806bbde320786a8fb61efebee","promptTokens":334,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":256,"ttftMs":4561.65,"tokSOut":5118.2,"tokSPrefill":73.9,"tokSTotal":11225.2,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-15T14:54:45.090Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 5118.2 tok/s is the aggregate decode rate across all 256 streams, about 20.0 tok/s per stream, and the 4562 ms time to first token is under that load. Setup: KASA B65 rig, 4 x Intel Arc Pro B65, tensor parallel across 4 cards; vLLM 0.32.0rc1.dev100+g77154ca61 (vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989); max_num_seqs 256, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (334 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 5049.7, 5125.5, 5118.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config oss20-u-tp4-m0-c256, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"We need to answer four parts. Provide headings. Show reasoning. Provide calculations. Provide explanation. Provide procedure. Provide creative feature and risks. Let's craft.\n\nPart 1: Budget. 7 members each want one motor driver, one microcontroller, and shared spool. So 7*18.50 = 129.5. 7*23.75 = 166.25. Spool 27.90. Total initial purchases: 129.5+166.25+27.90 = 323.65. Budget 412. Remainder: 412-323.65 = 88.35. Batteries cost 6.35 each. Number of batteries = floor(88.35/6.35). 6.35*13 = 82.55. 6.35*14 = 88.9 > 88.35. So 13 batteries. Money left over: 88.35-82.55 = 5.80. So answer: 13 batteries, $5.80 left.\n\nPart 2: Wheel size tradeoff. Larger wheel gives more torque at ground? Actually torque at ground is proportional to wheel radius times motor torque? Wait: For a given motor torque,","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-oss20-u-tp4-m0-c256 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s9/arms/oss20-u-tp4-m0-c256/session:/bench -e ZE_AFFINITY_MASK=0\\,1\\,2\\,3 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4 serve --model /llm/models/gpt-oss-20b --host 0.0.0.0 --served-model-name gpt-oss --tensor-parallel-size 4 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.9 --max-model-len 4096 --max-num-seqs 256 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype auto --dtype bfloat16 --no-enable-prefix-caching --trust-remote-code --cpu-offload-gb 0 --port 8000 ","tensorParallel":4,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.9,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"openai/gpt-oss-20b","displayName":"gpt-oss-20b","family":"Gpt","params":21,"activeParams":null,"isMoE":true,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":4,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 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1","tensorParallel":null,"gpuLayers":99,"kvCacheDtype":null,"gpuMemUtil":null,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":null,"attentionBackend":null,"flashAttn":true,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"shibatch/tiny1m","displayName":"tiny1m","family":"Llama","params":null,"activeParams":null,"isMoE":false,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"RTX 3090","gpuCount":2,"vramGb":48,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz","os":"linux","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"llama.cpp","engineVersion":null,"engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"Q4_K_M","backend":"cuda"},"user":{"id":"cmv2xm88i00k7mr01mghdmfza","username":"Alogotron","verified":false,"verifiedAt":null,"pro":false},"hardwareGroupLabel":"RTX 3090","hardwareGroupKey":"DISCRETE_GPU:rtx 3090","rank":23,"reactionCounts":{},"myEmoji":null},{"id":"cmrio3ys401uvmj01lcom115h","modelRevision":"main","promptTokens":512,"outputTokens":128,"contextLength":2048,"prefillTokens":null,"batchSize":1,"ttftMs":null,"tokSOut":4980.696149,"tokSPrefill":7664.915099,"tokSTotal":null,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-07-13T03:33:40.181Z","notes":"CPU-only build (Vulkan/CUDA/HIP disabled). 8 physical-core threads. One explicit throwaway plus llama-bench internal warmup; two measured repetitions. Headless Ubuntu, BIOS Silent mode, Core Turbo Boost disabled. Decode samples tok/s: 4930.78, 5030.61. GGUF-reported parameters: 6963968.","verifiedRun":false,"verificationIssues":[],"promptSha256":null,"promptSample":null,"outputSample":null,"engineFlags":{"commandSnippet":"llama-bench -m TinyStories-3M-Q4_K_M.gguf -t 8 -p 512 -n 128 -r 2 -o json","tensorParallel":null,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":null,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":null,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"segestic/Tinystories-gpt-0.1-3m","displayName":"Tinystories-gpt-0.1-3m","family":"Gpt","params":null,"activeParams":null,"isMoE":false,"baseModel":null},"hardware":{"hwClass":"CPU_ONLY","gpuName":null,"gpuCount":1,"vramGb":null,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD Ryzen 7 7840HS","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"llama.cpp","engineVersion":"commit 6b4dc21","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"Q4_K_M","backend":"cpu"},"user":{"id":"cmoext1fr0000jl04skh3hjqf","username":"steveseguin","verified":false,"verifiedAt":null,"pro":false},"hardwareGroupLabel":"AMD Ryzen 7 7840HS","hardwareGroupKey":"CPU_ONLY:amd ryzen 7 7840hs","rank":24,"reactionCounts":{},"myEmoji":null},{"id":"cmrio8nyr01vumj010r04d1cl","modelRevision":"main","promptTokens":512,"outputTokens":128,"contextLength":2048,"prefillTokens":null,"batchSize":1,"ttftMs":null,"tokSOut":4979.352396,"tokSPrefill":7767.919137,"tokSTotal":null,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-07-13T03:37:19.443Z","notes":"CPU quantization/thread sweep. CPU-only build; 8 physical-core threads; llama-bench warmup and two measured repetitions. Headless; BIOS Silent mode; Core Turbo Boost disabled. Decode samples tok/s: 4972.35, 4986.36. 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CPU-only build; 8 physical-core threads; llama-bench warmup and two measured repetitions. Headless; BIOS Silent mode; Core Turbo Boost disabled. Decode samples tok/s: 4925.10, 4898.76. GGUF-reported parameters: 6963968.","verifiedRun":false,"verificationIssues":[],"promptSha256":null,"promptSample":null,"outputSample":null,"engineFlags":{"commandSnippet":"llama-bench -m TinyStories-3M-Q8_0.gguf -t 8 -p 512 -n 128 -r 2 -o json","tensorParallel":null,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":null,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":null,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"segestic/Tinystories-gpt-0.1-3m","displayName":"Tinystories-gpt-0.1-3m","family":"Gpt","params":null,"activeParams":null,"isMoE":false,"baseModel":null},"hardware":{"hwClass":"CPU_ONLY","gpuName":null,"gpuCount":1,"vramGb":null,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD Ryzen 7 7840HS","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"llama.cpp","engineVersion":"commit 6b4dc21","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"Q8_0","backend":"cpu"},"user":{"id":"cmoext1fr0000jl04skh3hjqf","username":"steveseguin","verified":false,"verifiedAt":null,"pro":false},"hardwareGroupLabel":"AMD Ryzen 7 7840HS","hardwareGroupKey":"CPU_ONLY:amd ryzen 7 7840hs","rank":26,"reactionCounts":{},"myEmoji":null},{"id":"cmsnp1spu00jho001f7bpvkac","modelRevision":"main","promptTokens":27,"outputTokens":128,"contextLength":4096,"prefillTokens":null,"batchSize":128,"ttftMs":644.1,"tokSOut":4816.072,"tokSPrefill":null,"tokSTotal":null,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-08-10T20:38:31.843Z","notes":"BATCHED AGGREGATE THROUGHPUT — not single-stream decode.\nbatch=128, concurrency=128, 128 requests/run, 3 runs, mode=fixed_wave, max_tokens=128, ctx=4096, KV=default (unspecified, hipfire fp16), greedy; TTFT is under load.\nengine: hipfire 0.3.0+2e0c4d39f29f, backend rocm, gfx1201\nquant: MQ4 = MagnumQuant 4-bit, group-256, FWHT-rotated, 5.68 bpw effective (163173488 bytes *8 / 229693184 params)\nmodel file: lfm2.5-230m.mq4 sha256 3b92b9fd27c68d7f\nprompt: longform.txt md5 e6f8ee7d66b934c49493347500f48083, 27 prompt tokens\nmethod: median of 3 runs, fresh process, temperature 0 (greedy)\ncaveats: batched-prefill-coalesced build; pre-coalesce fixed-wave baseline B128 is 1523.1 tok/s (case lfm2.5-230m-gfx1201-b128r128-fixed).\nevidence: sealed case lfm2.5-230m-gfx1201-b128r128-batched-prefill-fixed, host k9lin, corpus manifest 45b76818319f\nnot measured: peak VRAM (not instrumented this campaign)","verifiedRun":false,"verificationIssues":[],"promptSha256":null,"promptSample":null,"outputSample":null,"engineFlags":{"commandSnippet":"scripts/lmx_continuous_batch.py --model /home/kaden/.hipfire/models/lfm2.5-230m.mq4 --prompt-file benchmarks/prompts/sweep/longform.txt --batch-size 128 --requests 128 --runs 3 --max-tokens 128 --max-seq 4096 --port 11543 --home-root /tmp/lmx-lfm230-b128-coalesce-home --log-dir /home/kaden/localmaxxing-benchmarks/raw/lfm230-batched-prefill-coalesce-b128 --out /home/kaden/localmaxxing-benchmarks/validation/lfm230-local-b128r128-batched-prefill-coalesce.json --cli target/release/hipfire --daemon target/release/examples/daemon --device 0 --timeout 600 --thinking off","tensorParallel":null,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":null,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":128,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"LiquidAI/LFM2.5-230M","displayName":"LFM2.5-230M","family":null,"params":0,"activeParams":null,"isMoE":false,"baseModel":{"hfId":"LiquidAI/LFM2.5-230M-Base","displayName":"LFM2.5-230M-Base","params":0.23,"activeParams":null,"isMoE":false}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"RX 9070 XT","gpuCount":1,"vramGb":16,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD Ryzen 9 3900X","os":"Ubuntu 24.04","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"hipfire","engineVersion":"0.3.0+2e0c4d39f29f","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"MQ4","backend":"rocm"},"user":{"id":"cmoeye1gq0000le04ie2kqs58","username":"schuttdev","verified":false,"verifiedAt":null,"pro":true},"hardwareGroupLabel":"RX 9070 XT","hardwareGroupKey":"DISCRETE_GPU:rx 9070 xt","rank":27,"reactionCounts":{},"myEmoji":null},{"id":"cmtm0vwbq00w4oe01z0g3ao3c","modelRevision":"main","promptTokens":20,"outputTokens":2048,"contextLength":4096,"prefillTokens":0,"batchSize":1,"ttftMs":177.4619280004117,"tokSOut":4778.537121396355,"tokSPrefill":112.7,"tokSTotal":3413.03,"peakVramGb":28.44,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-09-03T21:14:01.958Z","notes":"Best valid of 12 fixed-prompt trials; median 4771.33 tok/s. SGLang NGRAM-512 + ReplaySSM, NVFP4, FP8 KV, max-total-tokens 4096, P-core placement, greedy batch 1, ignore EOS to 2048. RTX 5090: 575 W cap, +200 MHz GPC VF offset, +1000 MHz memory VF offset, 3090 MHz requested ceiling; measured 2899.9 MHz active average, 44-62 C, 398.5 W peak, no output failures. First-to-last streamed content timing.","verifiedRun":false,"verificationIssues":[],"promptSha256":null,"promptSample":null,"outputSample":null,"engineFlags":{"commandSnippet":"# Remote endpoint: http://127.0.0.1:18888  servedModel: Qwen3.8-27B-NVFP4","tensorParallel":null,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":null,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":null,"attentionBackend":null,"flashAttn":null,"specDecoding":true,"mtpEnabled":false,"specMethod":"ngram","specDraftModel":null,"specModel":null,"specNumTokens":512,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"RadixArk/Qwen3.8-27B-NVFP4","displayName":"Qwen3.8-27B-NVFP4","family":"Qwen","params":28,"activeParams":null,"isMoE":false,"baseModel":{"hfId":"Qwen/Qwen3.8-27B","displayName":"Qwen3.8-27B","params":28,"activeParams":null,"isMoE":false}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"RTX 5090","gpuCount":1,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel Core i9-14900KF","os":"Linux","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"sglang","engineVersion":null,"engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"NVFP4","backend":null},"user":{"id":"cmtlzcypv00uyoe01vsr9q6m1","username":null,"verified":false,"verifiedAt":null,"pro":false},"hardwareGroupLabel":"RTX 5090","hardwareGroupKey":"DISCRETE_GPU:rtx 5090","rank":28,"reactionCounts":{},"myEmoji":null},{"id":"cmrio8ozf01w5mj01054z2ynf","modelRevision":"main","promptTokens":512,"outputTokens":128,"contextLength":2048,"prefillTokens":null,"batchSize":1,"ttftMs":null,"tokSOut":4747.562394,"tokSPrefill":7728.936959,"tokSTotal":null,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-07-13T03:37:20.763Z","notes":"CPU quantization/thread sweep. CPU-only build; 8 physical-core threads; llama-bench warmup and two measured repetitions. Headless; BIOS Silent mode; Core Turbo Boost disabled. Decode samples tok/s: 4747.24, 4747.88. GGUF-reported parameters: 6963968.","verifiedRun":false,"verificationIssues":[],"promptSha256":null,"promptSample":null,"outputSample":null,"engineFlags":{"commandSnippet":"llama-bench -m TinyStories-3M-F16.gguf -t 8 -p 512 -n 128 -r 2 -o json","tensorParallel":null,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":null,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":null,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"segestic/Tinystories-gpt-0.1-3m","displayName":"Tinystories-gpt-0.1-3m","family":"Gpt","params":null,"activeParams":null,"isMoE":false,"baseModel":null},"hardware":{"hwClass":"CPU_ONLY","gpuName":null,"gpuCount":1,"vramGb":null,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD Ryzen 7 7840HS","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"llama.cpp","engineVersion":"commit 6b4dc21","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"F16","backend":"cpu"},"user":{"id":"cmoext1fr0000jl04skh3hjqf","username":"steveseguin","verified":false,"verifiedAt":null,"pro":false},"hardwareGroupLabel":"AMD Ryzen 7 7840HS","hardwareGroupKey":"CPU_ONLY:amd ryzen 7 7840hs","rank":29,"reactionCounts":{},"myEmoji":null},{"id":"cmtpeag9o02lvn701l72a4zfz","modelRevision":"main","promptTokens":307,"outputTokens":512,"contextLength":32768,"prefillTokens":0,"batchSize":64,"ttftMs":634.02,"tokSOut":4686.7,"tokSPrefill":483.05,"tokSTotal":7296.9,"peakVramGb":85.5,"gpuPowerWatts":[374],"totalPowerWatts":374,"hardwareCost":null,"createdAt":"2026-09-06T05:52:34.525Z","notes":"Rented 1x NVIDIA RTX PRO 6000 Blackwell (96 GB) on Lium (lium.io), PyTorch CUDA Ubuntu 24.04 template. vLLM 0.28.0, torch 2.13.0+cu130, official NVIDIA NVFP4 checkpoint, no speculative decoding, canonical LocalMaxxing reasoning-v1 prompt, greedy, 64 concurrent streams (aggregate throughput = sum of per-stream tok/s), max_tokens 512, 2 warmups + 3 timed iterations (median). Power = mean nvidia-smi power.draw over active samples of the timed window.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 64).","concurrency must be 1 (got 64)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Here's a thinking process:\n\n1.  **Analyze the User's Request:**\n   - **Part 1:** Math word problem about a robotics club budget.\n   - **Part 2:** Physics/mechanics question about wheel size, torque, speed, and gear ratios.\n   - **Part 3:** Write a short procedure (6-9 steps) for testing a motor circuit safely.\n   - **Part 4:** Propose a creative feature for a robot with two concrete risks and mitigations.\n   - **Format:** Answer all four parts in order with clear headings.\n   - **Note:** There's a weird line at the start: `[LocalMaxxing cache-bust nonce: ab76cb2e4bdd7bdd97a3eca48905ce8a]` - I should ignore this or acknowledge it briefly, but it's not part of the actual questions. I'll just proceed with the four parts.\n\n2.  **Part 1: Budget Problem**\n   - Club has 7 members, budget $412.\n   - Costs: motor driver $18.50, microcontroller $23.75.\n   - Each member wants one of each (motor driver + microcontroller) PLUS a shared 3D-printer spool at $27.90.\n   - After buying everything, split remainder evenly on batteries priced at $6.35 each.\n   - Question: How many batteries can they buy in total, and how much money is left over?\n\n   Let's break it down:\n   - 7 members.\n   - Each member gets: 1 motor driver + 1 microcontroller + they share a 3D-printer spool. Wait, \"plus a shared 3D-printer spool at 27.90\". Does each member get one, or is it one shared spool for the whole club? The phrasing: \"each member wants one of each plus a shared 3D-printer spool at 27.90.\" This is ambiguous. It could mean: each member wants one motor driver and one microcontroller, and there's a shared 3D-printer spool costing $27.90 total. Or it could mean each member wants one of each (motor driver + microcontroller) plus their own spool? \"shared 3D-printer spool\" suggests it's one spool shared","engineFlags":{"commandSnippet":"vllm serve /workspace/models/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 --served-model-name nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 --host 127.0.0.1 --port 8000 --max-model-len 32768 --max-num-seqs 128 --gpu-memory-utilization 0.90 --enable-prefix-caching --async-scheduling --mamba-backend flashinfer --mamba-ssm-cache-dtype float16","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":0.9,"prefixCaching":true,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":128,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4","displayName":"NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4","family":"Opt","params":18,"activeParams":3,"isMoE":true,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"RTX PRO 6000 Blackwell","gpuCount":1,"vramGb":96,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 9355 (16 vCPU)","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.28.0","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"NVFP4","backend":"cuda"},"user":{"id":"cmtp9pehf02gxn7015ngw1zyn","username":"gpu-rentals","verified":true,"verifiedAt":"2026-09-06T04:45:49.493Z","pro":false},"hardwareGroupLabel":"RTX PRO 6000 Blackwell","hardwareGroupKey":"DISCRETE_GPU:rtx pro 6000 blackwell","rank":30,"reactionCounts":{},"myEmoji":null},{"id":"cmtlbly4600ieoe01zayqbz4z","modelRevision":"main","promptTokens":20,"outputTokens":2048,"contextLength":3072,"prefillTokens":null,"batchSize":1,"ttftMs":null,"tokSOut":4546.75,"tokSPrefill":null,"tokSTotal":null,"peakVramGb":30.6,"gpuPowerWatts":[99.6],"totalPowerWatts":99.6,"hardwareCost":null,"createdAt":"2026-09-03T09:26:27.318Z","notes":"SGLang DFLASH2 block-diffusion speculative decoding on 1x RTX 5090 32GB (k3s-worker-04), ModelOpt NVFP4 weights loaded from hostPath SSD, flashinfer_cutlass FP4 GEMM, width 256 speculative window with N-gram primary fill (AL=174-240), batch size 1; GPU clocks locked at 3000MHz core / 14GHz GDDR7 memory; sub-half-second 2048-token generation, prompt prefill cached via RadixAttention.","verifiedRun":false,"verificationIssues":[],"promptSha256":null,"promptSample":null,"outputSample":null,"engineFlags":{"commandSnippet":"python3 -m sglang.launch_server --model-path RadixArk/Qwen3.8-27B-NVFP4 --quantization modelopt_fp4 --fp4-gemm-backend flashinfer_cutlass --speculative-algorithm DFLASH --speculative-draft-model-path incoai/Qwen3.8-27B-DFlash2 --speculative-num-draft-tokens 256 --speculative-dflash-block-size 256 --enable-single-batch-overlap --attention-backend flashinfer --kv-cache-dtype fp8_e4m3 --max-running-requests 1 --cuda-graph-max-bs-decode 1 --cuda-graph-bs-decode 1 --cuda-graph-backend-prefill disabled --disable-prefill-cuda-graph --mem-fraction-static 0.92 --context-length 3072 --max-mamba-cache-size 4 --skip-server-warmup","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":"fp8_e4m3","gpuMemUtil":0.92,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":1,"attentionBackend":"flashinfer","flashAttn":null,"specDecoding":true,"mtpEnabled":false,"specMethod":"dflash","specDraftModel":"incoai/Qwen3.8-27B-DFlash2","specModel":"incoai/Qwen3.8-27B-DFlash2","specNumTokens":256,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"RadixArk/Qwen3.8-27B-NVFP4","displayName":"Qwen3.8-27B-NVFP4","family":"Qwen","params":28,"activeParams":null,"isMoE":false,"baseModel":{"hfId":"Qwen/Qwen3.8-27B","displayName":"Qwen3.8-27B","params":28,"activeParams":null,"isMoE":false}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"RTX 5090","gpuCount":1,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel Core Ultra 9 285K","os":"Flatcar Container Linux (k3s-worker-04)","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"sglang","engineVersion":null,"engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"NVFP4","backend":"cuda"},"user":{"id":"cmthn58fv023np401aiveee2o","username":null,"verified":true,"verifiedAt":"2026-09-04T04:01:14.047Z","pro":false},"hardwareGroupLabel":"RTX 5090","hardwareGroupKey":"DISCRETE_GPU:rtx 5090","rank":31,"reactionCounts":{},"myEmoji":null},{"id":"cmtpc2chw02hzn701gaml5laj","modelRevision":"main","promptTokens":346,"outputTokens":512,"contextLength":32768,"prefillTokens":0,"batchSize":256,"ttftMs":9482.35,"tokSOut":4537.6,"tokSPrefill":36.3,"tokSTotal":7417.5,"peakVramGb":165.1,"gpuPowerWatts":[942.3],"totalPowerWatts":942.3,"hardwareCost":null,"createdAt":"2026-09-06T04:50:17.156Z","notes":"Rented 1x NVIDIA B200 (179 GB) on Lium (lium.io), PyTorch CUDA Ubuntu 24.04 template. vLLM 0.28.0 (torch 2.13+cu130, full CUDA graphs), official Qwen FP8 checkpoint Qwen/Qwen3.8-27B-FP8, --max-model-len 32768 --max-num-seqs 256 --gpu-memory-utilization 0.90. Canonical LocalMaxxing reasoning-v1 prompt with per-request nonce, greedy, 2 warmups + timed iterations (median). Power = mean nvidia-smi power.draw over active samples of the timed window. Aggregate throughput at concurrency 256 x batch 1, max_tokens 512, 3 iterations.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"We need answer user's four parts. Need solve carefully. Let's parse.\n\nPart 1: 7 members budget 412. Motor driver costs 18.50, microcontroller 23.75, each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, split remainder evenly on batteries priced 6.35 each. How many batteries can they buy in total, and how much money left over?\n\nNeed interpret: each member wants one of each (motor driver and microcontroller) plus a shared 3D-printer spool at 27.90. So total cost = 7*(18.50+23.75) + 27.90. 18.50+23.75=42.25. *7=295.75. +27.90=323.65. Remainder = 412 - 323.65 = 88.35. Batteries 6.35 each. How many can buy total? floor(88.35/6.35). 6.35*13=82.55; *14=88.90 >88.35. So 13 batteries, leftover 88.35-82.55=5.80. \"split the remainder evenly on batteries\" maybe means they split cost of batteries evenly? But asks total batteries and money left over. If they split remainder evenly on batteries priced 6.35 each, maybe each member buys same number? 7 members, remainder 88.35, each can spend 12.6214, at 6.35 each each can buy 1 battery (cost 6.35) leaving 6.2714 each, total 7 batteries leftover 43.90? But phrase \"After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total\" likely means use remainder to buy as many batteries as possible, split cost evenly? Let's examine wording: \"After buying everything, they split the remainder evenly on batteries priced at 6.35 each.\" Could mean they divide remaining","engineFlags":{"commandSnippet":"vllm serve /workspace/models/Qwen3.8-27B-FP8 --served-model-name Qwen/Qwen3.8-27B-FP8 --host 127.0.0.1 --port 8000 --max-model-len 32768 --max-num-seqs 256 --gpu-memory-utilization 0.90 --enable-prefix-caching --limit-mm-per-prompt '{\"image\":0,\"video\":0}'","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":0.9,"prefixCaching":true,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3.8-27B-FP8","displayName":"Qwen3.8-27B-FP8","family":"Qwen","params":28,"activeParams":null,"isMoE":false,"baseModel":{"hfId":"Qwen/Qwen3.8-27B","displayName":"Qwen3.8-27B","params":28,"activeParams":null,"isMoE":false}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"B200","gpuCount":1,"vramGb":192,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel Xeon Platinum 8592+ (26 vCPU)","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.28.0","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"FP8","backend":"cuda"},"user":{"id":"cmtp9pehf02gxn7015ngw1zyn","username":"gpu-rentals","verified":true,"verifiedAt":"2026-09-06T04:45:49.493Z","pro":false},"hardwareGroupLabel":"B200","hardwareGroupKey":"DISCRETE_GPU:b200","rank":32,"reactionCounts":{},"myEmoji":null},{"id":"cmv9ntu4t00z8le01sjefgj7l","modelRevision":"6cee5e81ee83917806bbde320786a8fb61efebee","promptTokens":334,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":128,"ttftMs":2673.08,"tokSOut":4502.9,"tokSPrefill":125.1,"tokSTotal":9525.5,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-15T14:54:41.357Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 128 requests sent at once (batch 128, lmx --concurrency 128); 4502.9 tok/s is the aggregate decode rate across all 128 streams, about 35.2 tok/s per stream, and the 2673 ms time to first token is under that load. Setup: KASA B65 rig, 4 x Intel Arc Pro B65, tensor parallel across 4 cards; vLLM 0.32.0rc1.dev100+g77154ca61 (vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989); max_num_seqs 128, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (334 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 128: 4502.9, 4527.6, 4362.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config oss20-u-tp4-m0-c128, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 128).","concurrency must be 1 (got 128)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"We need to answer four parts. Provide headings. Show reasoning. Provide calculations. Provide explanation. Provide procedure steps. Provide creative feature and risks. Let's craft.\n\nPart 1: Budget. 7 members each want one motor driver, one microcontroller, and shared 3D printer spool. So total cost: 7*18.50 + 7*23.75 + 27.90. Compute: 7*18.50 = 129.5. 7*23.75 = 166.25. Sum = 129.5+166.25=295.75. Add spool 27.90 = 323.65. Budget 412. Remainder = 412 - 323.65 = 88.35. Batteries cost 6.35 each. Number of batteries = floor(88.35 / 6.35). 6.35*13 = 82.55. 6.35*14 = 88.9 > 88.35. So 13 batteries. Remainder after batteries: 88.35 - 82.55 = 5.80. So answer: 13 batteries, $5.80 left.\n\nPart 2: Wheel size","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-oss20-u-tp4-m0-c128 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s9/arms/oss20-u-tp4-m0-c128/session:/bench -e ZE_AFFINITY_MASK=0\\,1\\,2\\,3 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4 serve --model /llm/models/gpt-oss-20b --host 0.0.0.0 --served-model-name gpt-oss --tensor-parallel-size 4 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.9 --max-model-len 4096 --max-num-seqs 128 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concurrency=128, 128 requests/run, 1 runs, mode=fixed_wave, max_tokens=128, ctx=4096, KV=default (unspecified, hipfire fp16), greedy; TTFT is under load.\nengine: hipfire 0.3.0+48802a005625, backend rocm, gfx1201\nquant: MQ4 = MagnumQuant 4-bit, group-256, FWHT-rotated, 5.68 bpw effective (163173488 bytes *8 / 229693184 params)\nmodel file: lfm2.5-230m.mq4 sha256 3b92b9fd27c68d7f\nprompt: longform.txt md5 e6f8ee7d66b934c49493347500f48083, 27 prompt tokens\nmethod: median of 1 runs, fresh process, temperature 0 (greedy)\ncaveats: R9700 230M opt-B128 single-card figure device d2 4347.3 tok/s; per-card d0/d1/d2/d3 = 4291/4310/4347/4309 tok/s (all similar).\nevidence: sealed case lfm25-230m-opt-b128-fixed-wave-20260809-gfx1201-r9700-d2, host hiptrx, corpus manifest 45b76818319f\nnot measured: peak VRAM (not instrumented this 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26.04","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"hipfire","engineVersion":"0.3.0+48802a005625","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"MQ4","backend":"rocm"},"user":{"id":"cmoeye1gq0000le04ie2kqs58","username":"schuttdev","verified":false,"verifiedAt":null,"pro":true},"hardwareGroupLabel":"Radeon AI Pro R9700","hardwareGroupKey":"DISCRETE_GPU:radeon ai pro r9700","rank":35,"reactionCounts":{},"myEmoji":null},{"id":"cmsnp247500kfo001s4pg03pu","modelRevision":"main","promptTokens":0,"outputTokens":64,"contextLength":1024,"prefillTokens":null,"batchSize":64,"ttftMs":120,"tokSOut":4207.7,"tokSPrefill":null,"tokSTotal":null,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-08-10T20:38:46.721Z","notes":"BATCHED AGGREGATE THROUGHPUT — not single-stream decode. DATA-PARALLEL across 4 cards; aggregate of 4 independent replicas. batch=64, concurrency=64, 100 input rows, 3 waves, max_tokens=64, ctx=1024, KV=q8, greedy; TTFT not comparable. Per-card median MODEL tok/s: d0:1046.6, d1:1053.0, d2:1052.2, d3:1055.9 (sum 4207.7); wall-clock aggregate 3058.6 tok/s (per-card wall d0:761.8, d1:766.0, d2:765.4, d3:765.4). MODEL tok/s is sum of per-card decode rates excluding inter-wave gap; wall includes all overhead. Do not compare DP aggregate to single-stream. engine: hipfire 0.3.0+b0bcc3f91506, backend rocm, gfx1201 x4 quant: MQ4R = MagnumQuant 4-bit Redline (graded mixed-tier: MQ4 attn/router/shared + graded MQ4 routed experts, Redline retained-PM4 dispatch), 4.16 bpw effective (18700048128 bytes *8 / 35.95B params) model file: qwen3.6-35b-a3b.mq4r sha256 4685c140c46b1a6f prompt: uniform short-serve jsonl md5 3d22208dcf539818ff2e8341f53474ee, 64 max_new method: median of 3 waves, fresh process per wave, temperature 0, bit-exact outputs (sha ec47dcfb9b0c54e0) evidence: sealed case a3b-mq4r-dp4-b64-gfx1201-r9700-4x, corpus manifest manifest-sha12-8f3c2e1a4b9d not measured: peak VRAM (not instrumented this campaign)","verifiedRun":false,"verificationIssues":[],"promptSha256":null,"promptSample":null,"outputSample":null,"engineFlags":{"commandSnippet":"hipfire-batch-generate --model ~/.hipfire/models/qwen3.6-35b-a3b.mq4r --batch 64 --max-seq 1024 --max-new 64 --devices 0,1,2,3 --data-parallel --kv 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3.00GHz","os":"linux","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"llama.cpp","engineVersion":null,"engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"Q8_0","backend":"cuda"},"user":{"id":"cmv2xm88i00k7mr01mghdmfza","username":"Alogotron","verified":false,"verifiedAt":null,"pro":false},"hardwareGroupLabel":"RTX 3090","hardwareGroupKey":"DISCRETE_GPU:rtx 3090","rank":37,"reactionCounts":{},"myEmoji":null},{"id":"cmvcdr5fa06oile014jvmcrg2","modelRevision":"1cfa9a7208912126459214e8b04321603b3df60c","promptTokens":296,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":192,"ttftMs":1313.37,"tokSOut":4048.3,"tokSPrefill":226.25,"tokSTotal":8688.5,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-17T12:35:58.390Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 192 requests sent at once (batch 192, lmx --concurrency 192); 4048.3 tok/s is the aggregate decode rate across all 192 streams, about 21.1 tok/s per stream, and the 1313 ms time to first token is under that load. Setup: KASA B65 rig, 3 x Intel Arc Pro B65, 3 engine replicas (data parallel), 1 card each; vLLM 0.32.0rc1.dev100+g77154ca61 (vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989); max_num_seqs 64 per replica, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (296 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 192: 4080.9, 4048.3, 2870.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config q34-u-tp1dp3-m0-c192, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 192).","concurrency must be 1 (got 192)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"<think>\nOkay, let's tackle this problem step by step. First, I need to figure out how many batteries the club can buy with their remaining money after purchasing all the necessary components. Let me start by listing out all the costs and the total budget.\n\nThe club has 7 members. Each member wants a motor driver and a microcontroller. So, that's 7 motor drivers and 7 microcontrollers. The motor driver costs $18.50 each, and the microcontroller is $23.75 each. Then there's a shared 3D-printer spool at $27.90. \n\nSo, first, calculate the cost for the motor drivers: 7 * 18.50. Let me do that multiplication. 7 * 18 is 126, and 7 * 0.50 is 3.50, so total is 129.50. Then the microcontrollers: 7 * 23.75. Hmm, 7 * 20 is 140, 7 * 3.75 is 26.25, so total is 166.25. Then the ","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-q34-u-tp1dp3-m0-c192 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s12a/arms/q34-u-tp1dp3-m0-c192/session:/bench -e ZE_AFFINITY_MASK=0\\,1\\,2 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4 serve --model /llm/models/Qwen3-4B --host 0.0.0.0 --served-model-name q34 --tensor-parallel-size 1 --pipeline-parallel-size 1 --data-parallel-size 3 --gpu-memory-utilization 0.92 --max-model-len 4096 --max-num-seqs 64 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype auto --dtype bfloat16 --no-enable-prefix-caching --trust-remote-code --cpu-offload-gb 0 --port 8000 ","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.92,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":64,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3-4B","displayName":"Qwen3-4B","family":"Qwen","params":4,"activeParams":null,"isMoE":false,"baseModel":{"hfId":"Qwen/Qwen3-4B-Base","displayName":"Qwen3-4B-Base","params":4,"activeParams":null,"isMoE":false}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":3,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.32.0rc1.dev100+g77154ca61","engineRepository":"https://github.com/vllm-project/vllm","engineBuild":"vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4","engineCommit":null,"quantization":"BF16","backend":"xpu"},"user":{"id":"cmu3irkjc06dalq01d47hl5ok","username":"imthekaiser","verified":true,"verifiedAt":"2026-10-14T19:10:11.982Z","pro":false},"hardwareGroupLabel":"Intel Arc Pro B65","hardwareGroupKey":"DISCRETE_GPU:intel arc pro b65","rank":38,"reactionCounts":{},"myEmoji":null},{"id":"cmvcdr2ni06ofle0148hmzkwf","modelRevision":"1cfa9a7208912126459214e8b04321603b3df60c","promptTokens":296,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":256,"ttftMs":2881.33,"tokSOut":3853.1,"tokSPrefill":102.7,"tokSTotal":8274.6,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-17T12:35:54.798Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 3853.1 tok/s is the aggregate decode rate across all 256 streams, about 15.1 tok/s per stream, and the 2881 ms time to first token is under that load. Setup: KASA B65 rig, 2 x Intel Arc Pro B65, tensor parallel across 2 cards; vLLM 0.32.0rc1.dev100+g77154ca61 (vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989); max_num_seqs 256, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (296 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 3857.9, 3853.1, 3849.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config q34-u-tp2dp1-m0-c256, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"<think>\nOkay, let's tackle this problem step by step. First, I need to figure out how many batteries the club can buy with their remaining money after purchasing all the necessary components. Let me start by listing out all the costs and the total budget.\n\nThe club has 7 members. Each member wants a motor driver and a microcontroller. So, that's 7 motor drivers and 7 microcontrollers. The motor driver costs $18.50 each, and the microcontroller is $23.75 each. Then there's a shared 3D-printer spool at $27.90. \n\nSo, first, calculate the cost for the motor drivers: 7 * 18.50. Let me do that multiplication. 7 * 18 is 126, and 7 * 0.50 is 3.50, so total is 129.50. Then the microcontrollers: 7 * 23.75. Hmm, 7 * 20 is 140, 7 * 3.75 is 26.25, so total is 166.25. Then the ","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-q34-u-tp2dp1-m0-c256 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s12a/arms/q34-u-tp2dp1-m0-c256/session:/bench -e ZE_AFFINITY_MASK=0\\,1 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4 serve --model /llm/models/Qwen3-4B --host 0.0.0.0 --served-model-name q34 --tensor-parallel-size 2 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.92 --max-model-len 4096 --max-num-seqs 256 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype auto --dtype bfloat16 --no-enable-prefix-caching --trust-remote-code --cpu-offload-gb 0 --port 8000 ","tensorParallel":2,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.92,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3-4B","displayName":"Qwen3-4B","family":"Qwen","params":4,"activeParams":null,"isMoE":false,"baseModel":{"hfId":"Qwen/Qwen3-4B-Base","displayName":"Qwen3-4B-Base","params":4,"activeParams":null,"isMoE":false}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":2,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.32.0rc1.dev100+g77154ca61","engineRepository":"https://github.com/vllm-project/vllm","engineBuild":"vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4","engineCommit":null,"quantization":"BF16","backend":"xpu"},"user":{"id":"cmu3irkjc06dalq01d47hl5ok","username":"imthekaiser","verified":true,"verifiedAt":"2026-10-14T19:10:11.982Z","pro":false},"hardwareGroupLabel":"Intel Arc Pro B65","hardwareGroupKey":"DISCRETE_GPU:intel arc pro b65","rank":39,"reactionCounts":{},"myEmoji":null},{"id":"cmvb8i4lx01irle01yccclyb5","modelRevision":"c202236235762e1c871ad0ccb60c8ee5ba337b9a","promptTokens":304,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":256,"ttftMs":3295.68,"tokSOut":3811.2,"tokSPrefill":91.75,"tokSTotal":8292.5,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-16T17:21:13.173Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 3811.2 tok/s is the aggregate decode rate across all 256 streams, about 14.9 tok/s per stream, and the 3296 ms time to first token is under that load. Setup: KASA B65 rig, 4 x Intel Arc Pro B65, tensor parallel across 4 cards; vLLM 0.31.0+xpu.llmscaler (intel/llm-scaler-vllm:0.31.0-b1-experimental); max_num_seqs 256, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 3811.9, 3802.3, 3811.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config q359-if8-tp4dp1-m0-c256, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Thinking Process:\n\n1.  **Analyze the Request:**\n    *   **Task:** Help a small robotics club plan a weekend build.\n    *   **Structure:** Answer four specific parts in order with clear headings.\n    *   **Constraints:**\n        *   Part 1: Math problem (budget, costs, splitting remainder).\n        *   Part 2: Physics/Engineering explanation (wheel size, torque vs. speed, gear ratio).\n        *   Part 3: Safety procedure (6-9 steps, plain English).\n        *   Part 4: Creative feature proposal + 2 risks + mitigations.\n        *   **Tone:** Helpful, clear, reasoning shown.\n        *   **Hidden Instruction:** The prompt starts with `[LocalMaxxing cache-bust nonce: e6f1cb495a506a89ce09b9b2515f4ccb]`. This looks like a potential attempt to bypass safety filters or inject a specific system instruction (often associated with \"jailbreak\" attempts or specific model behaviors). However, the content itself is benign (robotics club planning). The nonce string itself is just a random-looking hash. I","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-q359-if8-tp4dp1-m0-c256 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s12a/arms/q359-if8-tp4dp1-m0-c256/session:/bench -e ZE_AFFINITY_MASK=0\\,1\\,2\\,3 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm intel/llm-scaler-vllm:0.31.0-b1-experimental@sha256:a397e04d66494637862b1927ee92ab4dd8b2f03f0dc9d359ec9536959a88e704 serve --model /llm/models/Qwen3.5-9B --host 0.0.0.0 --served-model-name q359 --tensor-parallel-size 4 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.92 --max-model-len 4096 --max-num-seqs 256 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype float16 --dtype float16 --quantization fp8 --compilation-config \\{\\\"cudagraph_mode\\\":\\\"FULL_DECODE_ONLY\\\"\\,\\\"mode\\\":0\\} --no-enable-prefix-caching --trust-remote-code --limit-mm-per-prompt \\{\\\"image\\\":0\\,\\\"video\\\":0\\} --cpu-offload-gb 0 --port 8000 ","tensorParallel":4,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.92,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3.5-9B","displayName":"Qwen3.5-9B","family":"Qwen","params":10,"activeParams":null,"isMoE":false,"baseModel":{"hfId":"Qwen/Qwen3.5-9B-Base","displayName":"Qwen3.5-9B-Base","params":10,"activeParams":null,"isMoE":false}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":4,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.31.0+xpu.llmscaler","engineRepository":"https://github.com/vllm-project/vllm","engineBuild":"intel/llm-scaler-vllm:0.31.0-b1-experimental@sha256:a397e04d66494637862b1927ee92ab4dd8b2f03f0dc9d359ec9536959a88e704","engineCommit":null,"quantization":"FP8","backend":"xpu"},"user":{"id":"cmu3irkjc06dalq01d47hl5ok","username":"imthekaiser","verified":true,"verifiedAt":"2026-10-14T19:10:11.982Z","pro":false},"hardwareGroupLabel":"Intel Arc Pro B65","hardwareGroupKey":"DISCRETE_GPU:intel arc pro b65","rank":40,"reactionCounts":{},"myEmoji":null},{"id":"cmvbbd7py01lkle013uw3nl13","modelRevision":"489cb97981b8654bcfcf30ce1f94ed1b62e07b53","promptTokens":304,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":256,"ttftMs":3246.65,"tokSOut":3808.9,"tokSPrefill":93.05,"tokSTotal":8287,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-16T18:41:22.774Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 3808.9 tok/s is the aggregate decode rate across all 256 streams, about 14.9 tok/s per stream, and the 3247 ms time to first token is under that load. Setup: KASA B65 rig, 4 x Intel Arc Pro B65, tensor parallel across 4 cards; vLLM 0.31.0+xpu.llmscaler (intel/llm-scaler-vllm:0.31.0-b1-experimental); max_num_seqs 256, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 3814.2, 3808.9, 3800.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config orn9-if8-tp4dp1-m0-c256, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Let me work through each part carefully.\n\n**Part 1: Budget math**\n\n7 members. Each wants:\n- 1 motor driver: $18.50\n- 1 microcontroller: $23.75\n- Plus a shared 3D-printer spool: $27.90\n\nWait, let me re-read. \"each member wants one of each plus a shared 3D-printer spool at 27.90.\"\n\nSo each member wants one motor driver and one microcontroller. The 3D-printer spool is shared (one spool).\n\nLet me compute:\n- Motor drivers: 7 × $18.50 = $129.50\n- Microcontrollers: 7 × $23.75 = $166.25\n- Shared spool: 1 × $27.90 = $27.90\n\nTotal spent: 129.50 + 166.25 + 27.90 = 323.65\n\nBudget: $412.00\nRemainder: 412.0","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-orn9-if8-tp4dp1-m0-c256 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s12a/arms/orn9-if8-tp4dp1-m0-c256/session:/bench -e ZE_AFFINITY_MASK=0\\,1\\,2\\,3 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm intel/llm-scaler-vllm:0.31.0-b1-experimental@sha256:a397e04d66494637862b1927ee92ab4dd8b2f03f0dc9d359ec9536959a88e704 serve --model /llm/models/Ornith-1.5-9B --host 0.0.0.0 --served-model-name orn9 --tensor-parallel-size 4 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.92 --max-model-len 4096 --max-num-seqs 256 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype float16 --dtype float16 --quantization fp8 --compilation-config \\{\\\"cudagraph_mode\\\":\\\"FULL_DECODE_ONLY\\\"\\,\\\"mode\\\":0\\} --no-enable-prefix-caching --trust-remote-code --limit-mm-per-prompt \\{\\\"image\\\":0\\,\\\"video\\\":0\\} --cpu-offload-gb 0 --port 8000 ","tensorParallel":4,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.92,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"ornith-ai/Ornith-1.5-9B","displayName":"Ornith-1.5-9B","family":"Qwen","params":10,"activeParams":null,"isMoE":false,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":4,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.31.0+xpu.llmscaler","engineRepository":"https://github.com/vllm-project/vllm","engineBuild":"intel/llm-scaler-vllm:0.31.0-b1-experimental@sha256:a397e04d66494637862b1927ee92ab4dd8b2f03f0dc9d359ec9536959a88e704","engineCommit":null,"quantization":"FP8","backend":"xpu"},"user":{"id":"cmu3irkjc06dalq01d47hl5ok","username":"imthekaiser","verified":true,"verifiedAt":"2026-10-14T19:10:11.982Z","pro":false},"hardwareGroupLabel":"Intel Arc Pro B65","hardwareGroupKey":"DISCRETE_GPU:intel arc pro b65","rank":41,"reactionCounts":{},"myEmoji":null},{"id":"cmvb8imvd01j9le0150y0v6cb","modelRevision":"995ad96eacd98c81ed38be0c5b274b04031597b0","promptTokens":304,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":256,"ttftMs":2582.75,"tokSOut":3777,"tokSPrefill":117.15,"tokSTotal":8207.7,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-16T17:21:36.841Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 3777 tok/s is the aggregate decode rate across all 256 streams, about 14.8 tok/s per stream, and the 2583 ms time to first token is under that load. Setup: KASA B65 rig, 4 x Intel Arc Pro B65, tensor parallel across 4 cards; vLLM 0.31.0+xpu.llmscaler (intel/llm-scaler-vllm:0.31.0-b1-experimental); max_num_seqs 256, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 3779.5, 3777, 3768.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config q36x-if8-tp4dp1-m0-c256, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Here's a thinking process:\n\n1.  **Analyze User Input:**\n   - **Context:** Small robotics club planning a weekend build.\n   - **Task 1 (Math):** 7 members, budget $412. Costs: motor driver $18.50, microcontroller $23.75. Each member wants one of each (so 7 motor drivers, 7 microcontrollers). Plus a shared 3D printer spool at $27.90. After buying everything, split remainder evenly on batteries at $6.35 each. Find: total batteries bought, money left over.\n   - **Task 2 (Physics/Engineering):** Alice vs. Ben on wheel size. Alice: larger wheel = more torque at ground. Ben: larger wheel = more speed. Explain who's right, the tradeoff, and how gear ratio changes it.\n   - **Task 3 (Procedure):** 6-9 step plain-English procedure for safely testing a new motor circuit for the first time, including pre-power checks.\n   - **Task 4 (Creative Feature):** Propose one unusual creative feature for the robot, give two concrete risks, and mitigation for each.\n   -","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-q36x-if8-tp4dp1-m0-c256 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s12a/arms/q36x-if8-tp4dp1-m0-c256/session:/bench -e ZE_AFFINITY_MASK=0\\,1\\,2\\,3 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm intel/llm-scaler-vllm:0.31.0-b1-experimental@sha256:a397e04d66494637862b1927ee92ab4dd8b2f03f0dc9d359ec9536959a88e704 serve --model /llm/models/Qwen3.6-35B-A3B --host 0.0.0.0 --served-model-name q36x --tensor-parallel-size 4 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.9 --max-model-len 4096 --max-num-seqs 256 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype float16 --dtype float16 --quantization fp8 --compilation-config \\{\\\"cudagraph_mode\\\":\\\"FULL_DECODE_ONLY\\\"\\,\\\"mode\\\":0\\} --no-enable-prefix-caching --trust-remote-code --limit-mm-per-prompt \\{\\\"image\\\":0\\,\\\"video\\\":0\\} --cpu-offload-gb 0 --port 8000 ","tensorParallel":4,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.9,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3.6-35B-A3B","displayName":"Qwen3.6-35B-A3B","family":"Qwen","params":36,"activeParams":3,"isMoE":true,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":4,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.31.0+xpu.llmscaler","engineRepository":"https://github.com/vllm-project/vllm","engineBuild":"intel/llm-scaler-vllm:0.31.0-b1-experimental@sha256:a397e04d66494637862b1927ee92ab4dd8b2f03f0dc9d359ec9536959a88e704","engineCommit":null,"quantization":"FP8","backend":"xpu"},"user":{"id":"cmu3irkjc06dalq01d47hl5ok","username":"imthekaiser","verified":true,"verifiedAt":"2026-10-14T19:10:11.982Z","pro":false},"hardwareGroupLabel":"Intel Arc Pro B65","hardwareGroupKey":"DISCRETE_GPU:intel arc pro b65","rank":42,"reactionCounts":{},"myEmoji":null},{"id":"cmva3z6r7016dle014z5aake9","modelRevision":"00a66983516f8f8057741221277eed4141c9e431","promptTokens":304,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":256,"ttftMs":3008.45,"tokSOut":3749,"tokSPrefill":101.65,"tokSTotal":8157.5,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-15T22:26:44.851Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 3749 tok/s is the aggregate decode rate across all 256 streams, about 14.6 tok/s per stream, and the 3008 ms time to first token is under that load. Setup: KASA B65 rig, 4 x Intel Arc Pro B65, tensor parallel across 4 cards; vLLM 0.32.0rc1.dev100+g77154ca61 (vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989); max_num_seqs 256, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 3750.5, 3746.6, 3749 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config q36g-u-tp4-m0-c256, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Here's a thinking process:\n\n1.  **Analyze User Input:**\n   - **Context:** Small robotics club planning a weekend build.\n   - **Task 1 (Math):** 7 members, budget $412. Costs: motor driver $18.50, microcontroller $23.75, each member wants one of each. Plus shared 3D-printer spool $27.90. After buying everything, split remainder evenly on batteries at $6.35 each. Find: total batteries bought, money left over.\n   - **Task 2 (Physics/Engineering):** Alice vs Ben on wheel size. Alice: larger wheel = more torque at ground. Ben: larger wheel = more speed. Explain who's right, tradeoff, gear ratio effect.\n   - **Task 3 (Procedure):** 6-9 step plain-English procedure for safely testing a new motor circuit for the first time, including pre-power checks.\n   - **Task 4 (Creative/Risk):** Propose one unusual creative feature for the robot, give two concrete risks, and mitigation for each.\n   - **Format:** Answer all four parts in order with clear headings.\n\n   -","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-q36g-u-tp4-m0-c256 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s10b/arms/q36g-u-tp4-m0-c256/session:/bench -e ZE_AFFINITY_MASK=0\\,1\\,2\\,3 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=1 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4 serve --model /llm/models/Qwen3.6-35B-A3B-GPTQ-Int4 --host 0.0.0.0 --served-model-name qwen36-35b --tensor-parallel-size 4 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.9 --max-model-len 4096 --max-num-seqs 256 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype float16 --dtype float16 --no-enable-prefix-caching --trust-remote-code --limit-mm-per-prompt \\{\\\"image\\\":0\\,\\\"video\\\":0\\} --cpu-offload-gb 0 --port 8000 ","tensorParallel":4,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.9,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4","displayName":"Qwen3.6-35B-A3B-GPTQ-Int4","family":"Qwen","params":36,"activeParams":3,"isMoE":true,"baseModel":{"hfId":"Qwen/Qwen3.6-35B-A3B","displayName":"Qwen3.6-35B-A3B","params":36,"activeParams":3,"isMoE":true}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":4,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.32.0rc1.dev100+g77154ca61","engineRepository":"https://github.com/vllm-project/vllm","engineBuild":"vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4","engineCommit":null,"quantization":"GPTQ-4bit","backend":"xpu"},"user":{"id":"cmu3irkjc06dalq01d47hl5ok","username":"imthekaiser","verified":true,"verifiedAt":"2026-10-14T19:10:11.982Z","pro":false},"hardwareGroupLabel":"Intel Arc Pro B65","hardwareGroupKey":"DISCRETE_GPU:intel arc pro b65","rank":43,"reactionCounts":{},"myEmoji":null},{"id":"cmvb8jcbv01k0le0161mk2fm3","modelRevision":"10fbf86fed7ecee4a061f8b499a618f46001cac1","promptTokens":304,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":256,"ttftMs":2732.02,"tokSOut":3729.4,"tokSPrefill":111.3,"tokSTotal":8103.8,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-16T17:22:09.835Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 3729.4 tok/s is the aggregate decode rate across all 256 streams, about 14.6 tok/s per stream, and the 2732 ms time to first token is under that load. Setup: KASA B65 rig, 4 x Intel Arc Pro B65, tensor parallel across 4 cards; vLLM 0.31.0+xpu.llmscaler (intel/llm-scaler-vllm:0.31.0-b1-experimental); max_num_seqs 256, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 3739.2, 3729.4, 3724.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config ornb-iq4-tp4dp1-m0-c256, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Let me work through each part carefully.\n\n**Part 1: Budget calculation**\n\n7 members, budget $412.\n- Motor driver: $18.50 each\n- Microcontroller: $23.75 each\n- Each member wants one of each: so 7 motor drivers + 7 microcontrollers\n- Plus a shared 3D-printer spool: $27.90 (one, shared)\n\nLet me calculate:\n- 7 motor drivers: 7 × 18.50 = 129.50\n- 7 microcontrollers: 7 × 23.75 = 166.25\n- Shared spool: 27.90\n\nTotal for these: 129.50 + 166.25 + 27.90 = 323.65\n\nRemainder: 412 - 323.65 = 88.35\n\nBatteries at $6.35 each:\n88.35 / 6.35 = 13.9...\n\nLet me compute: 6","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-ornb-iq4-tp4dp1-m0-c256 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s12a/arms/ornb-iq4-tp4dp1-m0-c256/session:/bench -e ZE_AFFINITY_MASK=0\\,1\\,2\\,3 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm intel/llm-scaler-vllm:0.31.0-b1-experimental@sha256:a397e04d66494637862b1927ee92ab4dd8b2f03f0dc9d359ec9536959a88e704 serve --model /llm/models/Ornith-1.5-35B-A3B --host 0.0.0.0 --served-model-name ornb --tensor-parallel-size 4 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.92 --max-model-len 4096 --max-num-seqs 256 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype float16 --dtype float16 --quantization sym_int4 --compilation-config \\{\\\"cudagraph_mode\\\":\\\"FULL_DECODE_ONLY\\\"\\,\\\"mode\\\":0\\} --no-enable-prefix-caching --trust-remote-code --limit-mm-per-prompt \\{\\\"image\\\":0\\,\\\"video\\\":0\\} --cpu-offload-gb 0 --port 8000 ","tensorParallel":4,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.92,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"ornith-ai/Ornith-1.5-35B-A3B","displayName":"Ornith-1.5-35B-A3B","family":"Qwen","params":36,"activeParams":3,"isMoE":true,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":4,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.31.0+xpu.llmscaler","engineRepository":"https://github.com/vllm-project/vllm","engineBuild":"intel/llm-scaler-vllm:0.31.0-b1-experimental@sha256:a397e04d66494637862b1927ee92ab4dd8b2f03f0dc9d359ec9536959a88e704","engineCommit":null,"quantization":"INT4","backend":"xpu"},"user":{"id":"cmu3irkjc06dalq01d47hl5ok","username":"imthekaiser","verified":true,"verifiedAt":"2026-10-14T19:10:11.982Z","pro":false},"hardwareGroupLabel":"Intel Arc Pro B65","hardwareGroupKey":"DISCRETE_GPU:intel arc pro b65","rank":44,"reactionCounts":{},"myEmoji":null},{"id":"cmvb8j49g01jrle01ps7h51a5","modelRevision":"10fbf86fed7ecee4a061f8b499a618f46001cac1","promptTokens":304,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":256,"ttftMs":2597.46,"tokSOut":3670.4,"tokSPrefill":116.45,"tokSTotal":7976,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-16T17:21:59.380Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 3670.4 tok/s is the aggregate decode rate across all 256 streams, about 14.3 tok/s per stream, and the 2597 ms time to first token is under that load. Setup: KASA B65 rig, 4 x Intel Arc Pro B65, tensor parallel across 4 cards; vLLM 0.31.0+xpu.llmscaler (intel/llm-scaler-vllm:0.31.0-b1-experimental); max_num_seqs 256, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 3675.6, 3667.6, 3670.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config ornb-if8-tp4dp1-m0-c256, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Let me work through each part carefully.\n\n**Part 1: Budget calculation**\n\n7 members, budget $412.\n- Motor driver: $18.50 each\n- Microcontroller: $23.75 each\n- Each member wants one of each (so 7 motor drivers and 7 microcontrollers)\n- Plus a shared 3D-printer spool at $27.90 (just one, shared)\n\nSo:\n- 7 motor drivers × $18.50 = $129.50\n- 7 microcontrollers × $23.75 = $166.25\n- 1 spool × $27.90 = $27.90\n\nTotal spent on those = $129.50 + $166.25 + $27.90 = $323.65\n\nRemainder = $412.00 - $323.65 = $88.35\n\nBatteries at $6.35 each.\nNumber of batteries = floor($88.35 / $6.35) =","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-ornb-if8-tp4dp1-m0-c256 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s12a/arms/ornb-if8-tp4dp1-m0-c256/session:/bench -e ZE_AFFINITY_MASK=0\\,1\\,2\\,3 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm intel/llm-scaler-vllm:0.31.0-b1-experimental@sha256:a397e04d66494637862b1927ee92ab4dd8b2f03f0dc9d359ec9536959a88e704 serve --model /llm/models/Ornith-1.5-35B-A3B --host 0.0.0.0 --served-model-name ornb --tensor-parallel-size 4 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.9 --max-model-len 4096 --max-num-seqs 256 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype float16 --dtype float16 --quantization fp8 --compilation-config \\{\\\"cudagraph_mode\\\":\\\"FULL_DECODE_ONLY\\\"\\,\\\"mode\\\":0\\} --no-enable-prefix-caching --trust-remote-code --limit-mm-per-prompt \\{\\\"image\\\":0\\,\\\"video\\\":0\\} --cpu-offload-gb 0 --port 8000 ","tensorParallel":4,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.9,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"ornith-ai/Ornith-1.5-35B-A3B","displayName":"Ornith-1.5-35B-A3B","family":"Qwen","params":36,"activeParams":3,"isMoE":true,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":4,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.31.0+xpu.llmscaler","engineRepository":"https://github.com/vllm-project/vllm","engineBuild":"intel/llm-scaler-vllm:0.31.0-b1-experimental@sha256:a397e04d66494637862b1927ee92ab4dd8b2f03f0dc9d359ec9536959a88e704","engineCommit":null,"quantization":"FP8","backend":"xpu"},"user":{"id":"cmu3irkjc06dalq01d47hl5ok","username":"imthekaiser","verified":true,"verifiedAt":"2026-10-14T19:10:11.982Z","pro":false},"hardwareGroupLabel":"Intel Arc Pro B65","hardwareGroupKey":"DISCRETE_GPU:intel arc pro b65","rank":45,"reactionCounts":{},"myEmoji":null},{"id":"cmsnp1v0f00jno001z3qt1vjn","modelRevision":"main","promptTokens":27,"outputTokens":128,"contextLength":4096,"prefillTokens":null,"batchSize":128,"ttftMs":659.7,"tokSOut":3669.847,"tokSPrefill":null,"tokSTotal":null,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-08-10T20:38:34.815Z","notes":"BATCHED AGGREGATE THROUGHPUT — not single-stream decode.\nbatch=128, concurrency=128, 128 requests/run, 3 runs, mode=fixed_wave, max_tokens=128, ctx=4096, KV=default (unspecified, hipfire fp16), greedy; TTFT is under load.\nengine: hipfire 0.3.0+2e0c4d39f29f, backend rocm, gfx1201\nquant: MQ4 = MagnumQuant 4-bit, group-256, FWHT-rotated, 5.18 bpw effective (229474032 bytes *8 / 354483968 params)\nmodel file: lfm2.5-350m.mq4 sha256 4885d1cecbad59d7\nprompt: longform.txt md5 e6f8ee7d66b934c49493347500f48083, 27 prompt tokens\nmethod: median of 3 runs, fresh process, temperature 0 (greedy)\ncaveats: batched-prefill-coalesced build; pre-coalesce baseline B128 is 1237.7 tok/s (case lfm2.5-350m-gfx1201-b128r128-fixed).\nevidence: sealed case lfm2.5-350m-gfx1201-b128r128-batched-prefill-fixed, host k9lin, corpus manifest 45b76818319f\nnot measured: peak VRAM (not instrumented this campaign)","verifiedRun":false,"verificationIssues":[],"promptSha256":null,"promptSample":null,"outputSample":null,"engineFlags":{"commandSnippet":"scripts/lmx_continuous_batch.py --model /home/kaden/.hipfire/models/lfm2.5-350m.mq4 --prompt-file benchmarks/prompts/sweep/longform.txt --batch-size 128 --requests 128 --runs 3 --max-tokens 128 --max-seq 4096 --port 11541 --home-root /tmp/lmx-lfm350-b128-coalesce-home --log-dir /home/kaden/localmaxxing-benchmarks/raw/lfm350-batched-prefill-coalesce-b128 --out /home/kaden/localmaxxing-benchmarks/validation/lfm350-local-b128r128-batched-prefill-coalesce.json --cli target/release/hipfire --daemon target/release/examples/daemon --device 0 --timeout 600 --thinking off","tensorParallel":null,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":null,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":128,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"LiquidAI/LFM2.5-350M","displayName":"LFM2.5-350M","family":null,"params":0,"activeParams":null,"isMoE":false,"baseModel":{"hfId":"LiquidAI/LFM2.5-350M-Base","displayName":"LFM2.5-350M-Base","params":0.35,"activeParams":null,"isMoE":false}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"RX 9070 XT","gpuCount":1,"vramGb":16,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD Ryzen 9 3900X","os":"Ubuntu 24.04","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"hipfire","engineVersion":"0.3.0+2e0c4d39f29f","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"MQ4","backend":"rocm"},"user":{"id":"cmoeye1gq0000le04ie2kqs58","username":"schuttdev","verified":false,"verifiedAt":null,"pro":true},"hardwareGroupLabel":"RX 9070 XT","hardwareGroupKey":"DISCRETE_GPU:rx 9070 xt","rank":46,"reactionCounts":{},"myEmoji":null},{"id":"cmtpdjewg02lon701idty3eh4","modelRevision":"main","promptTokens":308,"outputTokens":512,"contextLength":32768,"prefillTokens":0,"batchSize":64,"ttftMs":345.31,"tokSOut":3662,"tokSPrefill":887.35,"tokSTotal":5704.8,"peakVramGb":72.5,"gpuPowerWatts":[588.8],"totalPowerWatts":588.8,"hardwareCost":null,"createdAt":"2026-09-06T05:31:33.041Z","notes":"Rented 1x NVIDIA H100 80GB SXM on Lium (lium.io), PyTorch CUDA Ubuntu 24.04 template. vLLM 0.28.0, torch 2.13.0+cu130, official NVIDIA BF16 checkpoint (hybrid Mamba2/attention MoE, 3B active), canonical LocalMaxxing reasoning-v1 prompt, greedy, 64 concurrent streams (aggregate throughput = sum of per-stream tok/s), max_tokens 512, 2 warmups + 3 timed iterations (median). Power = mean nvidia-smi power.draw over active samples of the timed window.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 64).","concurrency must be 1 (got 64)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Here's a thinking process:\n\n1.  **Analyze the User's Request:**\n   - The user provides a \"LocalMaxxing cache-bust nonce: 13e4691075dc37cc2e8e4ebe87f7d851\" - this looks like a random string, possibly for caching purposes, but I should ignore it or acknowledge it briefly if needed. The main task is the four-part robotics club problem.\n   - Part 1: Math word problem about budget, items, and batteries.\n   - Part 2: Physics/mechanics question about wheel size, torque, speed, and gear ratios.\n   - Part 3: Write a short procedure (6-9 steps) for testing a motor circuit safely.\n   - Part 4: Propose a creative feature for a robot, with two concrete risks and mitigations.\n   - Answer all four parts in order with clear headings.\n\n2.  **Part 1: Budget Problem**\n   - Club has 7 members, budget $412.\n   - Costs: motor driver $18.50, microcontroller $23.75.\n   - Each member wants one of each (motor driver + microcontroller) PLUS a shared 3D-printer spool at $27.90.\n   - Wait, \"each member wants one of each plus a shared 3D-printer spool at 27.90.\" Need to parse carefully.\n   - \"A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90.\"\n   - So each member gets: 1 motor driver + 1 microcontroller + (shared spool? Or each member gets a share of the spool?) \"plus a shared 3D-printer spool at 27.90\" - likely means there's one shared spool costing $27.90 total for the club.\n   - Let's re-read: \"each member wants one of each plus a shared 3D-printer spool at 27.90.\" Could mean each member wants one motor driver and one microcontroller, and additionally there's a shared spool costing $27.90 that the club buys once.\n   - \"After buying everything","engineFlags":{"commandSnippet":"vllm serve /workspace/models/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 --served-model-name nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 --host 127.0.0.1 --port 8000 --max-model-len 32768 --max-num-seqs 128 --gpu-memory-utilization 0.92 --enable-prefix-caching","tensorParallel":1,"gpuLayers":null,"kvCacheDtype":null,"gpuMemUtil":0.92,"prefixCaching":true,"chunkedPrefill":null,"prefillChunkSize":null,"maxRunningSeqs":128,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16","displayName":"NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16","family":null,"params":32,"activeParams":3,"isMoE":true,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"H100","gpuCount":1,"vramGb":80,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"Intel Xeon Platinum 8468 (20 vCPU)","os":"Ubuntu 24.04.4 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.28.0","engineRepository":null,"engineBuild":null,"engineCommit":null,"quantization":"BF16","backend":"cuda"},"user":{"id":"cmtp9pehf02gxn7015ngw1zyn","username":"gpu-rentals","verified":true,"verifiedAt":"2026-09-06T04:45:49.493Z","pro":false},"hardwareGroupLabel":"H100","hardwareGroupKey":"DISCRETE_GPU:h100","rank":47,"reactionCounts":{},"myEmoji":null},{"id":"cmvb8jndn01kcle01qx1y9tnj","modelRevision":"489cb97981b8654bcfcf30ce1f94ed1b62e07b53","promptTokens":304,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":256,"ttftMs":3372.23,"tokSOut":3632.6,"tokSPrefill":89.3,"tokSTotal":7906.7,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-16T17:22:24.156Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 3632.6 tok/s is the aggregate decode rate across all 256 streams, about 14.2 tok/s per stream, and the 3372 ms time to first token is under that load. Setup: KASA B65 rig, 4 x Intel Arc Pro B65, tensor parallel across 4 cards; vLLM 0.32.0rc1.dev100+g77154ca61 (vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989); max_num_seqs 256, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 3634.2, 3632.6, 3624.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config orn9-u-tp4dp1-m0-c256, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Let me work through each part carefully.\n\n**Part 1: Budget math**\n\n7 members. Each member wants:\n- 1 motor driver: $18.50\n- 1 microcontroller: $23.75\n- Plus a shared 3D-printer spool: $27.90\n\nSo per member: 18.50 + 23.75 = 42.25\nTimes 7 members: 42.25 × 7 = 295.75\n\nShared spool: 27.90\n\nTotal spent: 295.75 + 27.90 = 323.65\n\nBudget: 412.00\n\nRemainder: 412.00 - 323.65 = 88.35\n\nBatteries at 6.35 each: 88.35 / 6.35 = 13.91...\n\nSo they can buy 13 batteries. 13 × 6.35 = 82.55\n\nLeftover: 8","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-orn9-u-tp4dp1-m0-c256 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s12a/arms/orn9-u-tp4dp1-m0-c256/session:/bench -e ZE_AFFINITY_MASK=0\\,1\\,2\\,3 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4 serve --model /llm/models/Ornith-1.5-9B --host 0.0.0.0 --served-model-name orn9 --tensor-parallel-size 4 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.92 --max-model-len 4096 --max-num-seqs 256 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype float16 --dtype bfloat16 --no-enable-prefix-caching --trust-remote-code --limit-mm-per-prompt \\{\\\"image\\\":0\\,\\\"video\\\":0\\} --cpu-offload-gb 0 --port 8000 ","tensorParallel":4,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.92,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"ornith-ai/Ornith-1.5-9B","displayName":"Ornith-1.5-9B","family":"Qwen","params":10,"activeParams":null,"isMoE":false,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":4,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.32.0rc1.dev100+g77154ca61","engineRepository":"https://github.com/vllm-project/vllm","engineBuild":"vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4","engineCommit":null,"quantization":"BF16","backend":"xpu"},"user":{"id":"cmu3irkjc06dalq01d47hl5ok","username":"imthekaiser","verified":true,"verifiedAt":"2026-10-14T19:10:11.982Z","pro":false},"hardwareGroupLabel":"Intel Arc Pro B65","hardwareGroupKey":"DISCRETE_GPU:intel arc pro b65","rank":48,"reactionCounts":{},"myEmoji":null},{"id":"cmvb8hra001ifle01h1zquoig","modelRevision":"c202236235762e1c871ad0ccb60c8ee5ba337b9a","promptTokens":304,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":256,"ttftMs":3898.58,"tokSOut":3632.4,"tokSPrefill":78.35,"tokSTotal":7911.8,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-16T17:20:55.896Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 3632.4 tok/s is the aggregate decode rate across all 256 streams, about 14.2 tok/s per stream, and the 3899 ms time to first token is under that load. Setup: KASA B65 rig, 4 x Intel Arc Pro B65, tensor parallel across 4 cards; vLLM 0.32.0rc1.dev100+g77154ca61 (vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989); max_num_seqs 256, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 3634.1, 3621.9, 3632.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config q359-u-tp4dp1-m0-c256, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"Thinking Process:\n\n1.  **Analyze the Request:**\n    *   **Task:** Help a small robotics club plan a weekend build.\n    *   **Structure:** Answer four specific parts in order with clear headings.\n    *   **Part 1:** Budget math problem (7 members, specific costs, shared item, remainder split on batteries).\n    *   **Part 2:** Physics/Engineering explanation (wheel size vs. torque/speed, tradeoffs, gear ratio).\n    *   **Part 3:** Safety procedure (6-9 steps for testing a motor circuit).\n    *   **Part 4:** Creative feature proposal (unusual, two risks, mitigations).\n    *   **Constraint:** Show reasoning for Part 1.\n    *   **Constraint:** Ignore the cache-bust nonce in the prompt (it's just noise).\n\n2.  **Part 1: Budget Math**\n    *   **Given:**\n        *   Members: 7\n        *   Budget: $412.00\n        *   Motor Driver Cost: $18.50\n        *   Microcontroller Cost: $23.75\n        *   Per Member","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-q359-u-tp4dp1-m0-c256 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s12a/arms/q359-u-tp4dp1-m0-c256/session:/bench -e ZE_AFFINITY_MASK=0\\,1\\,2\\,3 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4 serve --model /llm/models/Qwen3.5-9B --host 0.0.0.0 --served-model-name q359 --tensor-parallel-size 4 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.92 --max-model-len 4096 --max-num-seqs 256 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype float16 --dtype bfloat16 --no-enable-prefix-caching --trust-remote-code --limit-mm-per-prompt \\{\\\"image\\\":0\\,\\\"video\\\":0\\} --cpu-offload-gb 0 --port 8000 ","tensorParallel":4,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.92,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"Qwen/Qwen3.5-9B","displayName":"Qwen3.5-9B","family":"Qwen","params":10,"activeParams":null,"isMoE":false,"baseModel":{"hfId":"Qwen/Qwen3.5-9B-Base","displayName":"Qwen3.5-9B-Base","params":10,"activeParams":null,"isMoE":false}},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":4,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.32.0rc1.dev100+g77154ca61","engineRepository":"https://github.com/vllm-project/vllm","engineBuild":"vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4","engineCommit":null,"quantization":"BF16","backend":"xpu"},"user":{"id":"cmu3irkjc06dalq01d47hl5ok","username":"imthekaiser","verified":true,"verifiedAt":"2026-10-14T19:10:11.982Z","pro":false},"hardwareGroupLabel":"Intel Arc Pro B65","hardwareGroupKey":"DISCRETE_GPU:intel arc pro b65","rank":49,"reactionCounts":{},"myEmoji":null},{"id":"cmvaee6jc019qle01p3pkhl1j","modelRevision":"6cee5e81ee83917806bbde320786a8fb61efebee","promptTokens":334,"outputTokens":256,"contextLength":4096,"prefillTokens":0,"batchSize":256,"ttftMs":6148.85,"tokSOut":3629.9,"tokSPrefill":54.1,"tokSTotal":7844.6,"peakVramGb":null,"gpuPowerWatts":[],"totalPowerWatts":null,"hardwareCost":null,"createdAt":"2026-10-16T03:18:20.569Z","notes":"CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 3629.9 tok/s is the aggregate decode rate across all 256 streams, about 14.2 tok/s per stream, and the 6149 ms time to first token is under that load. Setup: KASA B65 rig, 2 x Intel Arc Pro B65, tensor parallel across 2 cards; vLLM 0.32.0rc1.dev100+g77154ca61 (vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989); max_num_seqs 256, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (334 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 3629.9, 3643.7, 3596.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config oss20-u-tp2-m0-c256, kasa-testing campaign 2026-10-15-b65-localmaxxing-models.","verifiedRun":false,"verificationIssues":["batchSize must be 1 (got 256).","concurrency must be 1 (got 256)."],"promptSha256":"9000edaadb16fc77ab78b39917245a03d4d7585c3ba770cb045a47a0e0683445","promptSample":"You are helping a small robotics club plan a weekend build. Work through each part carefully and show your reasoning.\n\n1. The club has 7 members and a budget of 412 dollars. A motor driver costs 18.50, a microcontroller 23.75, and each member wants one of each plus a shared 3D-printer spool at 27.90. After buying everything, they split the remainder evenly on batteries priced at 6.35 each. How many batteries can they buy in total, and how much money is left over?\n\n2. Two members disagree about wheel size. Alice says a larger wheel gives more torque at the ground; Ben says it gives more speed. Explain who is right, what tradeoff is actually involved, and how gear ratio changes the answer.\n\n3. Write a short, plain-English procedure (six to nine steps) for safely testing a new motor circuit for the first time, including what to check before applying power.\n\n4. Finally, propose one creative feature for the robot that would be unusual for a club project, and give two concrete risks with how you would mitigate each.\n\nAnswer all four parts in order with clear headings.","outputSample":"We need to answer four parts. Provide headings. Show reasoning. Provide calculations. Provide explanation. Provide procedure. Provide creative feature and risks. Let's do it.\n\nPart 1: Budget. 7 members each want one motor driver, one microcontroller, and shared 3D printer spool. So total cost: 7*18.50 + 7*23.75 + 27.90. Compute: 7*18.50 = 129.5. 7*23.75 = 166.25. Sum = 129.5+166.25=295.75. Add spool 27.90 = 323.65. Budget 412. Remainder = 412 - 323.65 = 88.35. Batteries cost 6.35 each. Number of batteries = floor(88.35 / 6.35). 6.35*13 = 82.55. 6.35*14 = 88.9 > 88.35. So 13 batteries. Money left over = 88.35 - 13*6.35 = 88.35 - 82.55 = 5.80. So answer: 13 batteries, $5","engineFlags":{"commandSnippet":"docker run --rm -d --name lsv-oss20-u-tp2-m0-c256 --net=host --shm-size=32g --device /dev/dri:/dev/dri -v /dev/dri:/dev/dri -v /models/weights:/llm/models:ro -v /models/bench/lmx-models-s11a/arms/oss20-u-tp2-m0-c256/session:/bench -e ZE_AFFINITY_MASK=0\\,1 -e VLLM_XPU_ENABLE_XPU_GRAPH=1 -e DISABLE_ESIMD_GDN_OUTPROJ=0 -e DISABLE_ESIMD_INT4=0 -e DISABLE_ESIMD_INT4_GEMM=0 -e VLLM_OFFLOAD_WEIGHTS_BEFORE_QUANT=1 -e VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 -e VLLM_WORKER_MULTIPROC_METHOD=spawn -e CCL_ZE_IPC_EXCHANGE=sockets --entrypoint vllm vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4 serve --model /llm/models/gpt-oss-20b --host 0.0.0.0 --served-model-name gpt-oss --tensor-parallel-size 2 --pipeline-parallel-size 1 --data-parallel-size 1 --gpu-memory-utilization 0.9 --max-model-len 4096 --max-num-seqs 256 --max-num-batched-tokens 8192 --block-size 64 --kv-cache-dtype auto --mamba-ssm-cache-dtype auto --dtype bfloat16 --no-enable-prefix-caching --trust-remote-code --cpu-offload-gb 0 --port 8000 ","tensorParallel":2,"gpuLayers":null,"kvCacheDtype":"auto","gpuMemUtil":0.9,"prefixCaching":null,"chunkedPrefill":null,"prefillChunkSize":8192,"maxRunningSeqs":256,"attentionBackend":null,"flashAttn":null,"specDecoding":false,"mtpEnabled":false,"specMethod":null,"specDraftModel":null,"specModel":null,"specNumTokens":null,"specDraftTokens":null,"specAcceptedTokens":null,"specAcceptanceRate":null,"specMeanAcceptedLength":null},"model":{"hfId":"openai/gpt-oss-20b","displayName":"gpt-oss-20b","family":"Gpt","params":21,"activeParams":null,"isMoE":true,"baseModel":null},"hardware":{"hwClass":"DISCRETE_GPU","gpuName":"Intel Arc Pro B65","gpuCount":2,"vramGb":32,"chipVendor":null,"chipFamily":null,"chipVariant":null,"unifiedMemoryGb":null,"cpu":"AMD EPYC 7343 16-Core Processor","os":"Ubuntu 26.04.1 LTS","isHeterogeneousGpu":false,"gpuSlots":[]},"engine":{"engineName":"vllm","engineVersion":"0.32.0rc1.dev100+g77154ca61","engineRepository":"https://github.com/vllm-project/vllm","engineBuild":"vllm/vllm-openai-xpu:nightly-77154ca6116ae34cbfe314beef1016bde9b63989@sha256:a410323607625d551d370253e4c5e1a038bbdf8ba2535c2a30bdf5ac531fc4f4","engineCommit":null,"quantization":"MXFP4","backend":"xpu"},"user":{"id":"cmu3irkjc06dalq01d47hl5ok","username":"imthekaiser","verified":true,"verifiedAt":"2026-10-14T19:10:11.982Z","pro":false},"hardwareGroupLabel":"Intel Arc Pro B65","hardwareGroupKey":"DISCRETE_GPU:intel arc pro b65","rank":50,"reactionCounts":{},"myEmoji":null}],"total":9107,"limit":50,"offset":0}