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imthekaiser

imthekaiser

Verified

@imthekaiser · Member since 2026

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Verified 1w ago Mainly llama.cpp 200 approved runs

Submissions

1,114

200 approved public runs

Models

24

unique models

Avg output

367tok/s

Best 3,216 tok/s

Avg prefill

376tok/s

Best 3,953 tok/s

Avg total

763tok/s

Best 6,953 tok/s

Avg TTFT

5,093ms

Best 77.2 ms

Hardware

2

distinct setups

Matrix runs

36

speed test matrices

Matrix runs

Matrix runs 36

A matrix run measures prompt reading (prefill) and generation (decode) speed in one go, across several context lengths and numbers of simultaneous requests. Showing the newest 10.
  • Qwen3.8-Flash-Next-W4A16Intel Arc Pro B65 32GB ×4 · vllm 0.30.0 · INT4_W4A166 contexts × 3 concurrency levels
    Peak decode
    242.6 tok/s
    Peak prefill
    3,961 tok/s
  • Qwen3.8-Flash-Next-W4A16Intel Arc Pro B65 32GB ×4 · vllm 0.30.0 · INT4_W4A166 contexts × 3 concurrency levels
    Peak decode
    158.3 tok/s
    Peak prefill
    3,856 tok/s
  • Qwen3.8-Flash-Next-W4A16Intel Arc Pro B65 32GB ×4 · vllm 0.30.0 · INT4_W4A166 contexts × 3 concurrency levels
    Peak decode
    246.3 tok/s
    Peak prefill
    5,206 tok/s
  • Qwen3.8-Flash-Next-W4A16Intel Arc Pro B65 32GB ×4 · vllm 0.30.0 · INT4_W4A166 contexts × 3 concurrency levels
    Peak decode
    158.6 tok/s
    Peak prefill
    5,306 tok/s
  • gemma-4-31B-itIntel Arc Pro B65 32GB · vllm 0.31.0+xpu.llmscaler · INT41 context × 5 concurrency levels
    Peak decode
    207.3 tok/s
    Peak prefill
    —
  • gemma-4-26B-A4B-itIntel Arc Pro B65 32GB · vllm 0.32.0 · FP81 context × 5 concurrency levels
    Peak decode
    425.3 tok/s
    Peak prefill
    —
  • gemma-4-12B-itIntel Arc Pro B65 32GB · vllm 0.32.0 · BF161 context × 5 concurrency levels
    Peak decode
    262.4 tok/s
    Peak prefill
    —
  • gemma-4-31B-itIntel Arc Pro B65 32GB ×4 · vllm 0.32.0 · BF163 contexts × 5 concurrency levels
    Peak decode
    284.5 tok/s
    Peak prefill
    592.0 tok/s
  • gemma-4-12B-itIntel Arc Pro B65 32GB ×4 · vllm 0.32.0 · BF164 contexts × 5 concurrency levels
    Peak decode
    648.0 tok/s
    Peak prefill
    1,407 tok/s
  • gemma-4-31B-itIntel Arc Pro B65 32GB ×4 · vllm 0.31.0+xpu.llmscaler · FP84 contexts × 5 concurrency levels
    Peak decode
    396.8 tok/s
    Peak prefill
    1,123 tok/s
See all 36 matrix runs
Personal bests

Personal bests24

Fastest approved result for each model.

20B MoE6 runs
Output3,216 tok/s
Prefill185 tok/s
TTFT1,794 ms
Depth0k
RTX A6000 48GB

vllm · MXFP4 · today

4B15 runs
Output2,566 tok/s
Prefill76.2 tok/s
TTFT4,026 ms
Depth0k
RTX A6000 48GB

vllm · BF16 · today

3B MoE6 runs
Output1,820 tok/s
Prefill167 tok/s
TTFT1,769 ms
Depth0k
RTX A6000 48GB

vllm · FP8 · today

9B17 runs
Output1,746 tok/s
Prefill44.6 tok/s
TTFT6,884 ms
Depth0k
RTX A6000 48GB

vllm · BF16 · today

3B MoE6 runs
Output1,745 tok/s
Prefill244 tok/s
TTFT1,214 ms
Depth0k
RTX A6000 48GB

vllm · FP8 · today

4B MoE7 runs
Output675 tok/s
Prefill68.7 tok/s
TTFT4,471 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-IQ4_XS · today

31B MoE7 runs
Output533 tok/s
Prefill115 tok/s
TTFT2,428 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q4_K_XL · today

80B MoE11 runs
Output406 tok/s
Prefill127 tok/s
TTFT2,335 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q3_K_XL · today

27B8 runs
Output364 tok/s
Prefill58.7 tok/s
TTFT5,893 ms
Depth0k
Intel Arc Pro B65 32GB

vllm · W4A16 · today

31B5 runs
Output264 tok/s
Prefill18.7 tok/s
TTFT16,397 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q4_K_XL · today

122B MoE6 runs
Output233 tok/s
Prefill54.7 tok/s
TTFT5,549 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · today

180B27 runs
Output231 tok/s
Prefill5.8 tok/s
TTFT59,930 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · today

10B MoE27 runs
Output224 tok/s
Prefill58.8 tok/s
TTFT5,156 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · today

70B5 runs
Output222 tok/s
Prefill47.5 tok/s
TTFT6,360 ms
Depth0k
RTX A6000 48GB

vllm · AWQ · today

72B6 runs
Output178 tok/s
Prefill68.7 tok/s
TTFT4,960 ms
Depth0k
RTX A6000 48GB

vllm · AWQ · today

17B MoE12 runs
Output165 tok/s
Prefill137 tok/s
TTFT2,007 ms
Depth0k
RTX A6000 48GB

llama.cpp · Q2_K · today

70B2 runs
Output116 tok/s
Prefill99.0 tok/s
TTFT3,062 ms
Depth0k
RTX A6000 48GB

vllm · AWQ · today

120B MoE3 runs
Output115 tok/s
Prefill20.7 tok/s
TTFT16,236 ms
Depth0k
RTX A6000 48GB

llama.cpp · MXFP4 · today

304B MoE7 runs
Output54.7 tok/s
Prefill8.5 tok/s
TTFT32,583 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · today

27B4 runs
Output47.5 tok/s
Prefill29.9 tok/s
TTFT11,560 ms
Depth0k
Intel Arc Pro B65 32GB

llama.cpp · UD-IQ4_XS · today

199B6 runs
Output27.0 tok/s
Prefill88.7 tok/s
TTFT3,168 ms
Depth0k
RTX A6000 48GB

llama.cpp · Q4_K · today

108B MoE4 runs
Output16.4 tok/s
Prefill95.1 tok/s
TTFT2,945 ms
Depth0k
RTX A6000 48GB

llama.cpp · Q8_0 · today

321B2 runs
Output16.0 tok/s
Prefill11.7 tok/s
TTFT24,401 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · today

311B MoE1 run
Output12.5 tok/s
Prefill9.2 tok/s
TTFT32,129 ms
Depth0k
RTX A6000 48GB

llama.cpp · Q2_K · today

Hardware breakdown

Hardware breakdown2

Distinct rigs used across approved submissions.

RTX A6000 48GB

179 runs · best 3,216 tok/s

Intel Arc Pro B65 32GB

21 runs · best 364 tok/s

Engines used

Engines used3

Runtime mix and top quantizations.

llama.cpp121UD-Q4_K_XL · UD-IQ4_XS · UD-Q2_K_XL
vllm71FP8 · MXFP4 · AWQ
strata8IQ3_S
All runs

All runs200

Full approved speed test history · page 2 of 8.

1,570

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

988 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,570 tok/s

Prefill

301 tok/s

Total

3,320 tok/s

TTFT

988 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

48

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

48

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 48 requests sent at once (batch 48, lmx --concurrency 48); 1569.6 tok/s is the aggregate decode rate across all 48 streams, about 32.7 tok/s per stream, and the 988 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (297 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 48: 1569.6, 1572, 1555.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 8:46 PM

Last edited

—

8.7

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q4_K

TTFT

11,488 ms

Context

2k · today

Show all run details

Model

stepfun-ai/Step-3.5-Flash

Display name

Step-3.5-Flash

Base model

Step-3.5-Flash

Revision

c439ad64d667632f3e1507757cbe225b6a46e6b8

Family

—

Parameters

199B

Active params

—

MoE

no

Output

8.7 tok/s

Prefill

24.5 tok/s

Total

17.7 tok/s

TTFT

11,488 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

281

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

Q4_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-step35f-q4k-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Step-3.5-Flash/Step-3.5-Flash-Q4_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-step35f-q4k-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 8.7 tok/s is the aggregate decode rate across all 12 streams, about 0.7 tok/s per stream, and the 11488 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 118 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47518 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF ggml-org/Step-3.5-Flash-GGUF @ c439ad64d667632f3e1507757cbe225b6a46e6b8, file Step-3.5-Flash-Q4_K.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 8.7, 13.5, 6.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 8:46 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

38.2

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

strata · IQ3_S

TTFT

2,690 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

38.2 tok/s

Prefill

127 tok/s

Total

63.8 tok/s

TTFT

2,690 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

342

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: VRAM_RESERVE_MIB=1024. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 38.2, 40.7, 35.4 tok/s; this is the median (tie to the earlier). Campaign ai/vm/b65-vm/2026-10-27-b65-vm-localmaxxing. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

—

Submitted

Oct 27, 2026, 8:44 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

40.7

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

strata · IQ3_S

TTFT

2,850 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

40.7 tok/s

Prefill

120 tok/s

Total

65.6 tok/s

TTFT

2,850 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

342

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: SPEC_MIN_P=0.8. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 32.6, 40.7, 48.3 tok/s; this is the median (tie to the earlier). Campaign ai/vm/b65-vm/2026-10-27-b65-vm-localmaxxing. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

—

Submitted

Oct 27, 2026, 8:44 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

44.0

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

strata · IQ3_S

TTFT

3,478 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

44.0 tok/s

Prefill

99.5 tok/s

Total

64.9 tok/s

TTFT

3,478 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: SPEC_MIN_P=0.6. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 37.2, 44, 50.2 tok/s; this is the median (tie to the earlier). Campaign ai/vm/b65-vm/2026-10-27-b65-vm-localmaxxing. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

—

Submitted

Oct 27, 2026, 8:44 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

38.1

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

strata · IQ3_S

TTFT

3,101 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

38.1 tok/s

Prefill

111 tok/s

Total

61.3 tok/s

TTFT

3,101 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: PCIE_FRAC=0.9. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 27.8, 41.3, 38.1 tok/s; this is the median (tie to the earlier). Campaign ai/vm/b65-vm/2026-10-27-b65-vm-localmaxxing. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

—

Submitted

Oct 27, 2026, 8:41 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

34.4

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

strata · IQ3_S

TTFT

2,481 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

34.4 tok/s

Prefill

139 tok/s

Total

60.8 tok/s

TTFT

2,481 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: PCIE_FRAC=0.5. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 34.6, 34.4, 29.7 tok/s; this is the median (tie to the earlier). Campaign ai/vm/b65-vm/2026-10-27-b65-vm-localmaxxing. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

—

Submitted

Oct 27, 2026, 8:41 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

30.0

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

strata · IQ3_S

TTFT

16,343 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

30.0 tok/s

Prefill

22.9 tok/s

Total

64.3 tok/s

TTFT

16,343 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

4

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 30 tok/s is the aggregate decode rate across all 4 streams, about 7.5 tok/s per stream. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: recipe defaults. Canonical reasoning-v1 prompt with a unique nonce; one discarded warm-up batch, then 3 measured batches: 29.4, 30, 30.9 tok/s; this is the median. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

—

Submitted

Oct 27, 2026, 8:41 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

37.1

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

strata · IQ3_S

TTFT

6,281 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

37.1 tok/s

Prefill

92.3 tok/s

Total

74.8 tok/s

TTFT

6,281 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 37.1 tok/s is the aggregate decode rate across all 2 streams, about 18.6 tok/s per stream. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: recipe defaults. Canonical reasoning-v1 prompt with a unique nonce; one discarded warm-up batch, then 3 measured batches: 40.1, 43.4, 37.1 tok/s; this is the median. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

—

Submitted

Oct 27, 2026, 8:41 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

40.4

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

strata · IQ3_S

TTFT

2,871 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

40.4 tok/s

Prefill

121 tok/s

Total

65.8 tok/s

TTFT

2,871 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

348

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: recipe defaults. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 34.3, 40.4, 43.2 tok/s; this is the median (tie to the earlier). Campaign ai/vm/b65-vm/2026-10-27-b65-vm-localmaxxing. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

—

Submitted

Oct 27, 2026, 8:40 PM

Last edited

—

275

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

143 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

275 tok/s

Prefill

2,070 tok/s

Total

579 tok/s

TTFT

143 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

3

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

3

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 3 requests sent at once (batch 3, lmx --concurrency 3); 275.2 tok/s is the aggregate decode rate across all 3 streams, about 91.7 tok/s per stream, and the 143 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (297 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 3: 268.1, 292.9, 275.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 8:36 PM

Last edited

—

1,124

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

515 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,124 tok/s

Prefill

571 tok/s

Total

2,378 tok/s

TTFT

515 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

24

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

24

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 24 requests sent at once (batch 24, lmx --concurrency 24); 1123.7 tok/s is the aggregate decode rate across all 24 streams, about 46.8 tok/s per stream, and the 515 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 24: 1099.4, 1123.7, 1138.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 8:36 PM

Last edited

—

693

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

286 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

693 tok/s

Prefill

1,031 tok/s

Total

1,464 tok/s

TTFT

286 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (batch 12, lmx --concurrency 12); 692.9 tok/s is the aggregate decode rate across all 12 streams, about 57.7 tok/s per stream, and the 286 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 12: 687.4, 720.1, 692.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 8:36 PM

Last edited

—

1,820

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

1,769 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,820 tok/s

Prefill

167 tok/s

Total

3,894 tok/s

TTFT

1,769 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

96

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

96

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 96 requests sent at once (batch 96, lmx --concurrency 96); 1820.2 tok/s is the aggregate decode rate across all 96 streams, about 19.0 tok/s per stream, and the 1769 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 96: 1820.2, 1811.3, 1827.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 8:36 PM

Last edited

—

451

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

232 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

451 tok/s

Prefill

1,275 tok/s

Total

945 tok/s

TTFT

232 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

295

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

6

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

6

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 6 requests sent at once (batch 6, lmx --concurrency 6); 450.5 tok/s is the aggregate decode rate across all 6 streams, about 75.1 tok/s per stream, and the 232 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (295 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 6: 486.8, 467.1, 450.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 8:36 PM

Last edited

—

1,442

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

961 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,442 tok/s

Prefill

308 tok/s

Total

3,065 tok/s

TTFT

961 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

48

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

48

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 48 requests sent at once (batch 48, lmx --concurrency 48); 1442.3 tok/s is the aggregate decode rate across all 48 streams, about 30.0 tok/s per stream, and the 961 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 48: 1447.6, 1452, 1442.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 8:26 PM

Last edited

—

283

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

143 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

283 tok/s

Prefill

2,071 tok/s

Total

589 tok/s

TTFT

143 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

295

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

3

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

3

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 3 requests sent at once (batch 3, lmx --concurrency 3); 282.6 tok/s is the aggregate decode rate across all 3 streams, about 94.2 tok/s per stream, and the 143 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (295 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 3: 284.6, 282.6, 270.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 8:26 PM

Last edited

—

1,013

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

502 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,013 tok/s

Prefill

588 tok/s

Total

2,150 tok/s

TTFT

502 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

24

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

24

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 24 requests sent at once (batch 24, lmx --concurrency 24); 1012.7 tok/s is the aggregate decode rate across all 24 streams, about 42.2 tok/s per stream, and the 502 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 24: 1090.6, 1062, 1012.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 8:26 PM

Last edited

—

706

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

291 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

706 tok/s

Prefill

1,003 tok/s

Total

1,481 tok/s

TTFT

291 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (batch 12, lmx --concurrency 12); 706.4 tok/s is the aggregate decode rate across all 12 streams, about 58.9 tok/s per stream, and the 291 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (297 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 12: 706.4, 711.1, 697.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 8:25 PM

Last edited

—

7.5

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q4_K

TTFT

10,404 ms

Context

2k · today

Show all run details

Model

stepfun-ai/Step-3.5-Flash

Display name

Step-3.5-Flash

Base model

Step-3.5-Flash

Revision

c439ad64d667632f3e1507757cbe225b6a46e6b8

Family

—

Parameters

199B

Active params

—

MoE

no

Output

7.5 tok/s

Prefill

27.1 tok/s

Total

15.2 tok/s

TTFT

10,404 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

282

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

Q4_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

8

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-step35f-q4k-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Step-3.5-Flash/Step-3.5-Flash-Q4_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-step35f-q4k-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 7.5 tok/s is the aggregate decode rate across all 8 streams, about 0.9 tok/s per stream, and the 10404 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 118 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47748 MiB of VRAM in use after load; a model download was running on the VM during this run, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF ggml-org/Step-3.5-Flash-GGUF @ c439ad64d667632f3e1507757cbe225b6a46e6b8, file Step-3.5-Flash-Q4_K.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 4.2, 7.5, 13.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 8:25 PM

Last edited

—

gpt-oss-20b

20B MoE · Gpt

3,216

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · MXFP4

TTFT

1,794 ms

Context

33k · today

Show all run details

Model

openai/gpt-oss-20b

Display name

gpt-oss-20b

Base model

—

Revision

6cee5e81ee83917806bbde320786a8fb61efebee

Family

Gpt

Parameters

20B

Active params

—

MoE

yes

Output

3,216 tok/s

Prefill

185 tok/s

Total

6,953 tok/s

TTFT

1,794 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

334

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

96

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

MXFP4

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

96

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-voss20-mxfp4 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gpt-oss-20b --served-model-name gpt-oss-20b --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-voss20-mxfp4 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 96 requests sent at once (batch 96, lmx --concurrency 96); 3215.7 tok/s is the aggregate decode rate across all 96 streams, about 33.5 tok/s per stream, and the 1794 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 96: 3251.2, 3215.7, 3186.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config voss20-mxfp4, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 8:15 PM

Last edited

—

gpt-oss-20b

20B MoE · Gpt

622

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · MXFP4

TTFT

198 ms

Context

33k · today

Show all run details

Model

openai/gpt-oss-20b

Display name

gpt-oss-20b

Base model

—

Revision

6cee5e81ee83917806bbde320786a8fb61efebee

Family

Gpt

Parameters

20B

Active params

—

MoE

yes

Output

622 tok/s

Prefill

1,692 tok/s

Total

1,331 tok/s

TTFT

198 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

334

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

6

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

MXFP4

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

6

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-voss20-mxfp4 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gpt-oss-20b --served-model-name gpt-oss-20b --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-voss20-mxfp4 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 6 requests sent at once (batch 6, lmx --concurrency 6); 622 tok/s is the aggregate decode rate across all 6 streams, about 103.7 tok/s per stream, and the 198 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 6: 622, 599.7, 546 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config voss20-mxfp4, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 8:15 PM

Last edited

—

gpt-oss-20b

20B MoE · Gpt

2,343

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · MXFP4

TTFT

1,171 ms

Context

33k · today

Show all run details

Model

openai/gpt-oss-20b

Display name

gpt-oss-20b

Base model

—

Revision

6cee5e81ee83917806bbde320786a8fb61efebee

Family

Gpt

Parameters

20B

Active params

—

MoE

yes

Output

2,343 tok/s

Prefill

288 tok/s

Total

4,887 tok/s

TTFT

1,171 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

334

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

48

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

MXFP4

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

48

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-voss20-mxfp4 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gpt-oss-20b --served-model-name gpt-oss-20b --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-voss20-mxfp4 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 48 requests sent at once (batch 48, lmx --concurrency 48); 2343.2 tok/s is the aggregate decode rate across all 48 streams, about 48.8 tok/s per stream, and the 1171 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 48: 2343.2, 2296.3, 2332.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config voss20-mxfp4, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 8:15 PM

Last edited

—

gpt-oss-20b

20B MoE · Gpt

317

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · MXFP4

TTFT

131 ms

Context

33k · today

Show all run details

Model

openai/gpt-oss-20b

Display name

gpt-oss-20b

Base model

—

Revision

6cee5e81ee83917806bbde320786a8fb61efebee

Family

Gpt

Parameters

20B

Active params

—

MoE

yes

Output

317 tok/s

Prefill

2,558 tok/s

Total

694 tok/s

TTFT

131 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

333

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

3

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

MXFP4

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

3

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-voss20-mxfp4 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gpt-oss-20b --served-model-name gpt-oss-20b --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-voss20-mxfp4 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 3 requests sent at once (batch 3, lmx --concurrency 3); 316.6 tok/s is the aggregate decode rate across all 3 streams, about 105.5 tok/s per stream, and the 131 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (333 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 3: 316.6, 314.4, 388.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config voss20-mxfp4, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 8:15 PM

Last edited

—

gpt-oss-20b

20B MoE · Gpt

1,650

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · MXFP4

TTFT

624 ms

Context

33k · today

Show all run details

Model

openai/gpt-oss-20b

Display name

gpt-oss-20b

Base model

—

Revision

6cee5e81ee83917806bbde320786a8fb61efebee

Family

Gpt

Parameters

20B

Active params

—

MoE

yes

Output

1,650 tok/s

Prefill

537 tok/s

Total

3,279 tok/s

TTFT

624 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

335

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

24

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

MXFP4

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

24

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-voss20-mxfp4 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gpt-oss-20b --served-model-name gpt-oss-20b --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-voss20-mxfp4 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 24 requests sent at once (batch 24, lmx --concurrency 24); 1650.1 tok/s is the aggregate decode rate across all 24 streams, about 68.8 tok/s per stream, and the 624 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (335 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 24: 1573.6, 1650.1, 1705.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config voss20-mxfp4, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

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Submitted

Oct 27, 2026, 8:15 PM

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ModelHardwareEnginetok/s outprefilltok/s totalTTFTDepthShare
Qwen3-Coder-30B-A3B-Instruct-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP81,5703013,320988 ms0k
Show all details for Qwen3-Coder-30B-A3B-Instruct-FP8

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,570 tok/s

Prefill

301 tok/s

Total

3,320 tok/s

TTFT

988 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

48

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

48

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 48 requests sent at once (batch 48, lmx --concurrency 48); 1569.6 tok/s is the aggregate decode rate across all 48 streams, about 32.7 tok/s per stream, and the 988 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (297 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 48: 1569.6, 1572, 1555.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 8:46 PM

Last edited

—

Step-3.5-Flash

199B

RTX A6000 48GB
llama.cppQ4_K8.724.517.711,488 ms0k
Show all details for Step-3.5-Flash

Model

stepfun-ai/Step-3.5-Flash

Display name

Step-3.5-Flash

Base model

Step-3.5-Flash

Revision

c439ad64d667632f3e1507757cbe225b6a46e6b8

Family

—

Parameters

199B

Active params

—

MoE

no

Output

8.7 tok/s

Prefill

24.5 tok/s

Total

17.7 tok/s

TTFT

11,488 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

281

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

Q4_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-step35f-q4k-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Step-3.5-Flash/Step-3.5-Flash-Q4_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-step35f-q4k-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 8.7 tok/s is the aggregate decode rate across all 12 streams, about 0.7 tok/s per stream, and the 11488 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 118 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47518 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF ggml-org/Step-3.5-Flash-GGUF @ c439ad64d667632f3e1507757cbe225b6a46e6b8, file Step-3.5-Flash-Q4_K.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 8.7, 13.5, 6.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 8:46 PM

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Qwen3.8-Flash-Next

180B · Qwen

Intel Arc Pro B65 32GB
strataIQ3_S38.212763.82,690 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

38.2 tok/s

Prefill

127 tok/s

Total

63.8 tok/s

TTFT

2,690 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

342

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: VRAM_RESERVE_MIB=1024. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 38.2, 40.7, 35.4 tok/s; this is the median (tie to the earlier). Campaign ai/vm/b65-vm/2026-10-27-b65-vm-localmaxxing. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

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Submitted

Oct 27, 2026, 8:44 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

Intel Arc Pro B65 32GB
strataIQ3_S40.712065.62,850 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

40.7 tok/s

Prefill

120 tok/s

Total

65.6 tok/s

TTFT

2,850 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

342

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: SPEC_MIN_P=0.8. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 32.6, 40.7, 48.3 tok/s; this is the median (tie to the earlier). Campaign ai/vm/b65-vm/2026-10-27-b65-vm-localmaxxing. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

—

Submitted

Oct 27, 2026, 8:44 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

Intel Arc Pro B65 32GB
strataIQ3_S44.099.564.93,478 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

44.0 tok/s

Prefill

99.5 tok/s

Total

64.9 tok/s

TTFT

3,478 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: SPEC_MIN_P=0.6. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 37.2, 44, 50.2 tok/s; this is the median (tie to the earlier). Campaign ai/vm/b65-vm/2026-10-27-b65-vm-localmaxxing. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

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Submitted

Oct 27, 2026, 8:44 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

Intel Arc Pro B65 32GB
strataIQ3_S38.111161.33,101 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

38.1 tok/s

Prefill

111 tok/s

Total

61.3 tok/s

TTFT

3,101 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: PCIE_FRAC=0.9. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 27.8, 41.3, 38.1 tok/s; this is the median (tie to the earlier). Campaign ai/vm/b65-vm/2026-10-27-b65-vm-localmaxxing. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

—

Submitted

Oct 27, 2026, 8:41 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

Intel Arc Pro B65 32GB
strataIQ3_S34.413960.82,481 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

34.4 tok/s

Prefill

139 tok/s

Total

60.8 tok/s

TTFT

2,481 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: PCIE_FRAC=0.5. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 34.6, 34.4, 29.7 tok/s; this is the median (tie to the earlier). Campaign ai/vm/b65-vm/2026-10-27-b65-vm-localmaxxing. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

—

Submitted

Oct 27, 2026, 8:41 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

Intel Arc Pro B65 32GB
strataIQ3_S30.022.964.316,343 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

30.0 tok/s

Prefill

22.9 tok/s

Total

64.3 tok/s

TTFT

16,343 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

4

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 30 tok/s is the aggregate decode rate across all 4 streams, about 7.5 tok/s per stream. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: recipe defaults. Canonical reasoning-v1 prompt with a unique nonce; one discarded warm-up batch, then 3 measured batches: 29.4, 30, 30.9 tok/s; this is the median. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

—

Submitted

Oct 27, 2026, 8:41 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

Intel Arc Pro B65 32GB
strataIQ3_S37.192.374.86,281 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

37.1 tok/s

Prefill

92.3 tok/s

Total

74.8 tok/s

TTFT

6,281 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 37.1 tok/s is the aggregate decode rate across all 2 streams, about 18.6 tok/s per stream. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: recipe defaults. Canonical reasoning-v1 prompt with a unique nonce; one discarded warm-up batch, then 3 measured batches: 40.1, 43.4, 37.1 tok/s; this is the median. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

—

Submitted

Oct 27, 2026, 8:41 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

Intel Arc Pro B65 32GB
strataIQ3_S40.412165.82,871 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

main

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

40.4 tok/s

Prefill

121 tok/s

Total

65.8 tok/s

TTFT

2,871 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

348

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

strata

Engine version

xe0.1.40.2.2

Quantization

IQ3_S

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

# Remote endpoint: http://127.0.0.1:8000 servedModel: qwen38-flash-next

Extra flags

—

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Engine: XeStrata xe0.1.40.2.2 (MistVVK/XeStrata, the Intel fork of Strata; LGPL-3.0) in the chriscorbell/intel-arc-pro-b70-qwen38-flash-next recipe image built from commit 6aad3506 (local image local/xestrata-qwen38:xe0.1.40.2.2, sha256:6f03dde70323). Weights: ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S @ ed59f920; the GPU holds the dense layers and a cache of hot experts, the CPU computes the rest of the experts from system RAM at the same time. MTP speculative decoding (4 tokens), int8 KV cache. Settings: recipe defaults. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 34.3, 40.4, 43.2 tok/s; this is the median (tie to the earlier). Campaign ai/vm/b65-vm/2026-10-27-b65-vm-localmaxxing. Prompt/output samples were added from the lmx run file; engineTimingsRaw is XeStrata's own per-request log (decode tokens and tok/s, expert-cache hit rate), because lmx does not capture it for this engine.

Reactions

—

Submitted

Oct 27, 2026, 8:40 PM

Last edited

—

Qwen3-Coder-30B-A3B-Instruct-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP82752,070579143 ms0k
Show all details for Qwen3-Coder-30B-A3B-Instruct-FP8

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

275 tok/s

Prefill

2,070 tok/s

Total

579 tok/s

TTFT

143 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

3

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

3

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 3 requests sent at once (batch 3, lmx --concurrency 3); 275.2 tok/s is the aggregate decode rate across all 3 streams, about 91.7 tok/s per stream, and the 143 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (297 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 3: 268.1, 292.9, 275.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 8:36 PM

Last edited

—

Qwen3-Coder-30B-A3B-Instruct-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP81,1245712,378515 ms0k
Show all details for Qwen3-Coder-30B-A3B-Instruct-FP8

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,124 tok/s

Prefill

571 tok/s

Total

2,378 tok/s

TTFT

515 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

24

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

24

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 24 requests sent at once (batch 24, lmx --concurrency 24); 1123.7 tok/s is the aggregate decode rate across all 24 streams, about 46.8 tok/s per stream, and the 515 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 24: 1099.4, 1123.7, 1138.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 8:36 PM

Last edited

—

Qwen3-Coder-30B-A3B-Instruct-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP86931,0311,464286 ms0k
Show all details for Qwen3-Coder-30B-A3B-Instruct-FP8

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

693 tok/s

Prefill

1,031 tok/s

Total

1,464 tok/s

TTFT

286 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (batch 12, lmx --concurrency 12); 692.9 tok/s is the aggregate decode rate across all 12 streams, about 57.7 tok/s per stream, and the 286 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 12: 687.4, 720.1, 692.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 8:36 PM

Last edited

—

Qwen3-30B-A3B-Instruct-2507-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP81,8201673,8941,769 ms0k
Show all details for Qwen3-30B-A3B-Instruct-2507-FP8

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,820 tok/s

Prefill

167 tok/s

Total

3,894 tok/s

TTFT

1,769 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

96

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

96

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 96 requests sent at once (batch 96, lmx --concurrency 96); 1820.2 tok/s is the aggregate decode rate across all 96 streams, about 19.0 tok/s per stream, and the 1769 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 96: 1820.2, 1811.3, 1827.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 8:36 PM

Last edited

—

Qwen3-30B-A3B-Instruct-2507-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP84511,275945232 ms0k
Show all details for Qwen3-30B-A3B-Instruct-2507-FP8

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

451 tok/s

Prefill

1,275 tok/s

Total

945 tok/s

TTFT

232 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

295

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

6

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

6

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 6 requests sent at once (batch 6, lmx --concurrency 6); 450.5 tok/s is the aggregate decode rate across all 6 streams, about 75.1 tok/s per stream, and the 232 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (295 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 6: 486.8, 467.1, 450.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 8:36 PM

Last edited

—

Qwen3-30B-A3B-Instruct-2507-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP81,4423083,065961 ms0k
Show all details for Qwen3-30B-A3B-Instruct-2507-FP8

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,442 tok/s

Prefill

308 tok/s

Total

3,065 tok/s

TTFT

961 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

48

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

48

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 48 requests sent at once (batch 48, lmx --concurrency 48); 1442.3 tok/s is the aggregate decode rate across all 48 streams, about 30.0 tok/s per stream, and the 961 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 48: 1447.6, 1452, 1442.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 8:26 PM

Last edited

—

Qwen3-30B-A3B-Instruct-2507-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP82832,071589143 ms0k
Show all details for Qwen3-30B-A3B-Instruct-2507-FP8

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

283 tok/s

Prefill

2,071 tok/s

Total

589 tok/s

TTFT

143 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

295

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

3

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

3

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 3 requests sent at once (batch 3, lmx --concurrency 3); 282.6 tok/s is the aggregate decode rate across all 3 streams, about 94.2 tok/s per stream, and the 143 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (295 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 3: 284.6, 282.6, 270.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 8:26 PM

Last edited

—

Qwen3-30B-A3B-Instruct-2507-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP81,0135882,150502 ms0k
Show all details for Qwen3-30B-A3B-Instruct-2507-FP8

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,013 tok/s

Prefill

588 tok/s

Total

2,150 tok/s

TTFT

502 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

24

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

24

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 24 requests sent at once (batch 24, lmx --concurrency 24); 1012.7 tok/s is the aggregate decode rate across all 24 streams, about 42.2 tok/s per stream, and the 502 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 24: 1090.6, 1062, 1012.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 8:26 PM

Last edited

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Qwen3-30B-A3B-Instruct-2507-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP87061,0031,481291 ms0k
Show all details for Qwen3-30B-A3B-Instruct-2507-FP8

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

706 tok/s

Prefill

1,003 tok/s

Total

1,481 tok/s

TTFT

291 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (batch 12, lmx --concurrency 12); 706.4 tok/s is the aggregate decode rate across all 12 streams, about 58.9 tok/s per stream, and the 291 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (297 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 12: 706.4, 711.1, 697.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 8:25 PM

Last edited

—

Step-3.5-Flash

199B

RTX A6000 48GB
llama.cppQ4_K7.527.115.210,404 ms0k
Show all details for Step-3.5-Flash

Model

stepfun-ai/Step-3.5-Flash

Display name

Step-3.5-Flash

Base model

Step-3.5-Flash

Revision

c439ad64d667632f3e1507757cbe225b6a46e6b8

Family

—

Parameters

199B

Active params

—

MoE

no

Output

7.5 tok/s

Prefill

27.1 tok/s

Total

15.2 tok/s

TTFT

10,404 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

282

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

Q4_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

8

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-step35f-q4k-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Step-3.5-Flash/Step-3.5-Flash-Q4_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-step35f-q4k-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 7.5 tok/s is the aggregate decode rate across all 8 streams, about 0.9 tok/s per stream, and the 10404 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 118 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47748 MiB of VRAM in use after load; a model download was running on the VM during this run, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF ggml-org/Step-3.5-Flash-GGUF @ c439ad64d667632f3e1507757cbe225b6a46e6b8, file Step-3.5-Flash-Q4_K.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 4.2, 7.5, 13.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 8:25 PM

Last edited

—

gpt-oss-20b

20B MoE · Gpt

RTX A6000 48GB
vllmMXFP43,2161856,9531,794 ms0k
Show all details for gpt-oss-20b

Model

openai/gpt-oss-20b

Display name

gpt-oss-20b

Base model

—

Revision

6cee5e81ee83917806bbde320786a8fb61efebee

Family

Gpt

Parameters

20B

Active params

—

MoE

yes

Output

3,216 tok/s

Prefill

185 tok/s

Total

6,953 tok/s

TTFT

1,794 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

334

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

96

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

MXFP4

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

96

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-voss20-mxfp4 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gpt-oss-20b --served-model-name gpt-oss-20b --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-voss20-mxfp4 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 96 requests sent at once (batch 96, lmx --concurrency 96); 3215.7 tok/s is the aggregate decode rate across all 96 streams, about 33.5 tok/s per stream, and the 1794 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 96: 3251.2, 3215.7, 3186.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config voss20-mxfp4, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 8:15 PM

Last edited

—

gpt-oss-20b

20B MoE · Gpt

RTX A6000 48GB
vllmMXFP46221,6921,331198 ms0k
Show all details for gpt-oss-20b

Model

openai/gpt-oss-20b

Display name

gpt-oss-20b

Base model

—

Revision

6cee5e81ee83917806bbde320786a8fb61efebee

Family

Gpt

Parameters

20B

Active params

—

MoE

yes

Output

622 tok/s

Prefill

1,692 tok/s

Total

1,331 tok/s

TTFT

198 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

334

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

6

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

MXFP4

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

6

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-voss20-mxfp4 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gpt-oss-20b --served-model-name gpt-oss-20b --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-voss20-mxfp4 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 6 requests sent at once (batch 6, lmx --concurrency 6); 622 tok/s is the aggregate decode rate across all 6 streams, about 103.7 tok/s per stream, and the 198 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 6: 622, 599.7, 546 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config voss20-mxfp4, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 8:15 PM

Last edited

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gpt-oss-20b

20B MoE · Gpt

RTX A6000 48GB
vllmMXFP42,3432884,8871,171 ms0k
Show all details for gpt-oss-20b

Model

openai/gpt-oss-20b

Display name

gpt-oss-20b

Base model

—

Revision

6cee5e81ee83917806bbde320786a8fb61efebee

Family

Gpt

Parameters

20B

Active params

—

MoE

yes

Output

2,343 tok/s

Prefill

288 tok/s

Total

4,887 tok/s

TTFT

1,171 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

334

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

48

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

MXFP4

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

48

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-voss20-mxfp4 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gpt-oss-20b --served-model-name gpt-oss-20b --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-voss20-mxfp4 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 48 requests sent at once (batch 48, lmx --concurrency 48); 2343.2 tok/s is the aggregate decode rate across all 48 streams, about 48.8 tok/s per stream, and the 1171 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; 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 48: 2343.2, 2296.3, 2332.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config voss20-mxfp4, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 8:15 PM

Last edited

—

gpt-oss-20b

20B MoE · Gpt

RTX A6000 48GB
vllmMXFP43172,558694131 ms0k
Show all details for gpt-oss-20b

Model

openai/gpt-oss-20b

Display name

gpt-oss-20b

Base model

—

Revision

6cee5e81ee83917806bbde320786a8fb61efebee

Family

Gpt

Parameters

20B

Active params

—

MoE

yes

Output

317 tok/s

Prefill

2,558 tok/s

Total

694 tok/s

TTFT

131 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

333

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

3

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

MXFP4

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

3

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-voss20-mxfp4 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gpt-oss-20b --served-model-name gpt-oss-20b --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-voss20-mxfp4 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 3 requests sent at once (batch 3, lmx --concurrency 3); 316.6 tok/s is the aggregate decode rate across all 3 streams, about 105.5 tok/s per stream, and the 131 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (333 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 3: 316.6, 314.4, 388.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config voss20-mxfp4, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 8:15 PM

Last edited

—

gpt-oss-20b

20B MoE · Gpt

RTX A6000 48GB
vllmMXFP41,6505373,279624 ms0k
Show all details for gpt-oss-20b

Model

openai/gpt-oss-20b

Display name

gpt-oss-20b

Base model

—

Revision

6cee5e81ee83917806bbde320786a8fb61efebee

Family

Gpt

Parameters

20B

Active params

—

MoE

yes

Output

1,650 tok/s

Prefill

537 tok/s

Total

3,279 tok/s

TTFT

624 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

335

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

24

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

MXFP4

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

24

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-voss20-mxfp4 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gpt-oss-20b --served-model-name gpt-oss-20b --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-voss20-mxfp4 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 24 requests sent at once (batch 24, lmx --concurrency 24); 1650.1 tok/s is the aggregate decode rate across all 24 streams, about 68.8 tok/s per stream, and the 624 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (335 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 24: 1573.6, 1650.1, 1705.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config voss20-mxfp4, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 8:15 PM

Last edited

—