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Mainly llama.cpp 46 approved runsLocalMaxxing

Submissions

46

46 approved public runs

Models

19

unique models

Avg output

88.9tok/s

Best 303 tok/s

Avg prefill

1,458tok/s

Best 9,243 tok/s

Avg total

226tok/s

Best 1,744 tok/s

Avg TTFT

1,001ms

Best 29.8 ms

Hardware

4

distinct setups

Matrix runs

8

speed test matrices

Reactions

3

across all runs

Benchmarks run

Benchmarks run94

Approved evaluation benchmark results.

ARC Challenge

Evaluation · Qwen3.8-27B-TT-Mixed-BFP4-BFP8-P150 · BFP4

Tenstorrent P150 32GB · 9/28/2026

283/289 correct

Result

Accuracy: 97.9%
GSM8K

Reasoning · Qwen3.8-27B-TT-Mixed-BFP4-BFP8-P150 · BFP4

Tenstorrent P150 32GB · 9/28/2026

99/101 correct

Result

Accuracy: 98.0%
Terminal-Bench 4.0

Agentic · Qwen3.8-27B · INT4

RTX 3090 48GB ×2 · 8/29/2026

0/7 correct

Result

Accuracy: 0.0%
Terminal-Bench 4.0

Agentic · Qwen3.8-27B · INT4

RTX 3090 48GB ×2 · 8/29/2026

0/6 correct

Result

Accuracy: 0.0%
Terminal-Bench 4.0

Agentic · Qwen3.8-27B · INT4

RTX 3090 48GB ×2 · 8/29/2026

0/7 correct

Result

Accuracy: 0.0%
Terminal-Bench 4.0

Agentic · Qwen3.8-27B · INT4

RTX 3090 · 8/29/2026

0/7 correct

Result

Accuracy: 0.0%
Terminal-Bench 4.0

Agentic · Qwen3.8-27B · INT4

RTX 3090 · 8/29/2026

1/6 correct

Result

Accuracy: 16.7%
Terminal-Bench 4.0

Agentic · Qwen3.8-27B · INT4

RTX 3090 · 8/29/2026

0/6 correct

Result

Accuracy: 0.0%
Terminal-Bench 4.0

Agentic · Qwen3.8-27B · INT4

RTX 3090 · 8/29/2026

0/7 correct

Result

Accuracy: 0.0%
Terminal-Bench 4.0

Agentic · Qwen3.8-27B · INT4

RTX 3090 · 8/29/2026

0/7 correct

Result

Accuracy: 0.0%
Terminal-Bench 4.0

Agentic · Qwen3.8-27B · INT4

RTX 3090 · 8/29/2026

0/6 correct

Result

Accuracy: 0.0%
Terminal-Bench 4.0

Agentic · Qwen3.8-27B · INT4

RTX 3090 · 8/29/2026

0/7 correct

Result

Accuracy: 0.0%
Terminal-Bench 2.1

Agentic · Qwen3.8-27B-GPTQ-4bit · GPTQ-4bit

RTX 3090 · 8/26/2026

5/9 correct

Result

Accuracy: 55.6%
Terminal-Bench 2.1

Agentic · Qwen3.8-27B-GPTQ-4bit · GPTQ-4bit

RTX 3090 · 8/26/2026

8/9 correct

Result

Accuracy: 88.9%
Terminal-Bench 2.1

Agentic · Qwen3.8-27B-GPTQ-4bit · GPTQ-4bit

RTX 3090 · 8/26/2026

5/9 correct

Result

Accuracy: 55.6%
Terminal-Bench 2.1

Agentic · Qwen3.8-27B-GPTQ-4bit · GPTQ-4bit

RTX 3090 · 8/26/2026

7/9 correct

Result

Accuracy: 77.8%
Terminal-Bench 2.1

Agentic · Qwen3.8-27B-GPTQ-4bit · GPTQ-4bit

RTX 3090 · 8/26/2026

6/9 correct

Result

Accuracy: 66.7%
Terminal-Bench 2.1

Agentic · Qwen3.8-27B-GPTQ-4bit · GPTQ-4bit

RTX 3090 · 8/26/2026

7/9 correct

Result

Accuracy: 77.8%
Terminal-Bench 2.1

Agentic · Qwen3.8-27B-GPTQ-4bit · GPTQ-4bit

RTX 3090 · 8/26/2026

5/9 correct

Result

Accuracy: 55.6%
Terminal-Bench 2.1

Agentic · Qwen3.8-27B-GPTQ-4bit · GPTQ-4bit

RTX 3090 · 8/26/2026

5/9 correct

Result

Accuracy: 55.6%
Terminal-Bench 2.1

Agentic · Qwen3.8-27B-GPTQ-4bit · GPTQ-4bit

RTX 3090 · 8/26/2026

8/9 correct

Result

Accuracy: 88.9%
Terminal-Bench 2.1

Agentic · Qwen3.8-27B-GPTQ-4bit · GPTQ-4bit

RTX 3090 · 8/26/2026

6/8 correct

Result

Accuracy: 75.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL-retry-assisted

RTX 3090 24GB ×2 · 8/15/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL-retry-assisted

RTX 3090 24GB ×2 · 8/15/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL-retry-assisted

RTX 3090 24GB ×2 · 8/15/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL-retry-assisted

RTX 3090 24GB ×2 · 8/15/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL-retry-assisted

RTX 3090 24GB ×2 · 8/15/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL

RTX 3090 24GB ×2 · 8/15/2026

1/1 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL

RTX 3090 24GB ×2 · 8/15/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL

RTX 3090 24GB ×2 · 8/15/2026

1/1 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL

RTX 3090 24GB ×2 · 8/15/2026

1/1 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL

RTX 3090 24GB ×2 · 8/15/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL

RTX 3090 24GB ×2 · 8/15/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL

RTX 3090 24GB ×2 · 8/15/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL

RTX 3090 24GB ×2 · 8/15/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL

RTX 3090 24GB ×2 · 8/15/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL

RTX 3090 24GB ×2 · 8/15/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Qwen3.8-27B-GGUF · Q4_K_XL

RTX 3090 24GB ×2 · 8/15/2026

7/8 correct

Result

Accuracy: 87.5%
Terminal-Bench 2.1

Agentic · Qwen3.5-9B · Q8_K_XL

RTX 3090 · 8/14/2026

1/9 correct

Result

Accuracy: 11.1%
Terminal-Bench 2.1

Agentic · Qwen3.5-9B · Q8_K_XL

RTX 3090 · 8/14/2026

1/9 correct

Result

Accuracy: 11.1%
Terminal-Bench 2.1

Agentic · Qwen3.5-9B · Q8_K_XL

RTX 3090 · 8/14/2026

2/9 correct

Result

Accuracy: 22.2%
Terminal-Bench 2.1

Agentic · Qwen3.5-9B · Q8_K_XL

RTX 3090 · 8/14/2026

4/9 correct

Result

Accuracy: 44.4%
Terminal-Bench 2.1

Agentic · Qwen3.5-9B · Q8_K_XL

RTX 3090 · 8/14/2026

3/9 correct

Result

Accuracy: 33.3%
Terminal-Bench 2.1

Agentic · Qwen3.5-9B · Q8_K_XL

RTX 3090 · 8/14/2026

3/9 correct

Result

Accuracy: 33.3%
Terminal-Bench 2.1

Agentic · Qwen3.5-9B · Q8_K_XL

RTX 3090 · 8/14/2026

2/9 correct

Result

Accuracy: 22.2%
Terminal-Bench 2.1

Agentic · Qwen3.5-9B · Q8_K_XL

RTX 3090 · 8/14/2026

1/9 correct

Result

Accuracy: 11.1%
Terminal-Bench 2.1

Agentic · Qwen3.5-9B · Q8_K_XL

RTX 3090 · 8/14/2026

4/9 correct

Result

Accuracy: 44.4%
Terminal-Bench 2.1

Agentic · Qwen3.5-9B · Q8_K_XL

RTX 3090 · 8/14/2026

1/8 correct

Result

Accuracy: 12.5%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

2/9 correct

Result

Accuracy: 22.2%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

0/9 correct

Result

Accuracy: 0.0%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

1/9 correct

Result

Accuracy: 11.1%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

2/9 correct

Result

Accuracy: 22.2%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

4/9 correct

Result

Accuracy: 44.4%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

4/9 correct

Result

Accuracy: 44.4%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

2/9 correct

Result

Accuracy: 22.2%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

1/9 correct

Result

Accuracy: 11.1%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

2/9 correct

Result

Accuracy: 22.2%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

1/8 correct

Result

Accuracy: 12.5%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

2/9 correct

Result

Accuracy: 22.2%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

0/9 correct

Result

Accuracy: 0.0%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

1/9 correct

Result

Accuracy: 11.1%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

1/9 correct

Result

Accuracy: 11.1%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

3/9 correct

Result

Accuracy: 33.3%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

4/9 correct

Result

Accuracy: 44.4%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

2/9 correct

Result

Accuracy: 22.2%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

1/9 correct

Result

Accuracy: 11.1%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

1/9 correct

Result

Accuracy: 11.1%
Terminal-Bench 2.1

Agentic · NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 · NVFP4

RTX 3090 · 8/13/2026

1/8 correct

Result

Accuracy: 12.5%
CRUD-Bench

Agentic · Muse-Glimmer-30B-GGUF · Q6_K_XL

RTX 3090 24GB ×2 · 8/12/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Muse-Glimmer-30B-GGUF · Q6_K_XL

RTX 3090 24GB ×2 · 8/12/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Muse-Glimmer-30B-GGUF · Q6_K_XL

RTX 3090 24GB ×2 · 8/12/2026

8/8 correct

Result

Accuracy: 100.0%
CRUD-Bench

Agentic · Muse-Glimmer-30B-GGUF · Q6_K_XL

RTX 3090 24GB ×2 · 8/12/2026

7/8 correct

Result

Accuracy: 87.5%
CRUD-Bench

Agentic · Muse-Glimmer-30B-GGUF · Q6_K_XL

RTX 3090 24GB ×2 · 8/12/2026

7/8 correct

Result

Accuracy: 87.5%
GSM8K

Reasoning · DeepSeek-V4-Pro-Qwen3.5-9B-MTP-GGUF · Q8_0

RTX 3090 24GB · 8/9/2026

2/2 correct

Result

Accuracy: 100.0%
Terminal-Bench 2.1

Agentic · Qwopus3.6-27B-Coder-MTP-GGUF · Q5_K_M

RTX 3090 · 7/22/2026

3/9 correct

Result

Accuracy: 33.3%
Terminal-Bench 2.1

Agentic · Qwopus3.6-27B-Coder-MTP-GGUF · Q5_K_M

RTX 3090 · 7/21/2026

2/8 correct

Result

Accuracy: 25.0%
Terminal-Bench 2.1

Agentic · Qwen3.6-27B-GGUF · Q4_K_XL

RTX 3090 · 7/17/2026

3/8 correct

Result

Accuracy: 37.5%
MBPP+

Coding · Qwopus3.6-27B-Coder-MTP-GGUF · Q5_K_M

RTX 3090 · 6/29/2026

297/378 correct

Result

Accuracy: 78.6%
HumanEval+

Coding · Qwopus3.6-27B-Coder-MTP-GGUF · Q5_K_M

RTX 3090 · 6/29/2026

149/164 correct

Result

Accuracy: 90.9%
ARC Challenge

Evaluation · Qwopus3.6-27B-Coder-MTP-GGUF · Q5_K_M

RTX 3090 · 6/29/2026

779/800 correct

Result

Accuracy: 97.4%
HellaSwag

Reasoning · Qwopus3.6-27B-Coder-MTP-GGUF · Q5_K_M

RTX 3090 · 6/29/2026

717/772 correct

Result

Accuracy: 92.9%
GSM8K

Reasoning · Qwopus3.6-27B-Coder-MTP-GGUF · Q5_K_M

RTX 3090 · 6/29/2026

100/101 correct

Result

Accuracy: 99.0%
MBPP+

Coding · gemma-4-26B-A4B-it-qat-GGUF · Q4_K_XL

RTX 3090 · 6/25/2026

76/100 correct

Result

Accuracy: 76.0%
HumanEval+

Coding · gemma-4-26B-A4B-it-qat-GGUF · Q4_K_XL

RTX 3090 · 6/25/2026

93/100 correct

Result

Accuracy: 93.0%
HellaSwag

Reasoning · gemma-4-26B-A4B-it-qat-GGUF · Q4_K_XL

RTX 3090 · 6/25/2026

52/100 correct

Result

Accuracy: 52.0%
GSM8K

Reasoning · gemma-4-26B-A4B-it-qat-GGUF · Q4_K_XL

RTX 3090 · 6/25/2026

97/100 correct

Result

Accuracy: 97.0%
ARC Challenge

Evaluation · gemma-4-26B-A4B-it-qat-GGUF · Q4_K_XL

RTX 3090 · 6/25/2026

100/100 correct

Result

Accuracy: 100.0%
GSM8K

Reasoning · gemma-4-12b-it-GGUF · Q8_K_XL

RTX 3090 · 6/24/2026

95/100 correct

Result

Accuracy: 95.0%
HellaSwag

Reasoning · gemma-4-12b-it-GGUF · Q8_K_XL

RTX 3090 · 6/24/2026

54/100 correct

Result

Accuracy: 54.0%
ARC Challenge

Evaluation · gemma-4-12b-it-GGUF · Q8_K_XL

RTX 3090 · 6/24/2026

96/100 correct

Result

Accuracy: 96.0%
HumanEval+

Coding · gemma-4-12b-it-GGUF · Q8_K_XL

RTX 3090 · 6/24/2026

90/100 correct

Result

Accuracy: 90.0%
MBPP+

Coding · gemma-4-12b-it-GGUF · Q8_K_XL

RTX 3090 · 6/24/2026

74/100 correct

Result

Accuracy: 74.0%
Matrix runs

Matrix runs 8

A matrix run measures prompt reading (prefill) and generation (decode) speed in one go, across several context lengths and numbers of simultaneous requests.
  • Qwen3.6-35B-A3B-TT-Mixed-BFP4-BFP8-P150Tenstorrent P150 32GB · vllm 0.1.dev14187+g03fa3af2e · BFP46 contexts × 5 concurrency levels
    Peak decode
    628.5 tok/s
    Peak prefill
    2,014 tok/s
  • Qwen3.6-35B-A3B-TT-Mixed-BFP4-BFP8-P150Tenstorrent P150 32GB · vllm 0.1.dev14187+g03fa3af2e · BFP46 contexts × 1 concurrency level
    Peak decode
    130.4 tok/s
    Peak prefill
    2,055 tok/s
  • Qwen3.6-35B-A3B-TT-Mixed-BFP4-BFP8-P150Tenstorrent P150 32GB · vllm 0.1.dev14187+g03fa3af2e · BFP44 contexts × 5 concurrency levels
    Peak decode
    626.2 tok/s
    Peak prefill
    2,016 tok/s
  • LFM2.5-24B-A2BTenstorrent P150 32GB · vllm 0.1.dev14187+g03fa3af2e · BFP46 contexts × 5 concurrency levels
    Peak decode
    805.5 tok/s
    Peak prefill
    10.6K tok/s
  • NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4RTX 3090 24GB · vllm 0.26.0 · NVFP43 contexts × 3 concurrency levels
    Peak decode
    1,092.4 tok/s
    Peak prefill
    6,659 tok/s
  • Qwen3.8-27B-GPTQ-4bitRTX 3090 24GB · vllm 0.26.0 · GPTQ3 contexts × 3 concurrency levels
    Peak decode
    80.6 tok/s
    Peak prefill
    1,292 tok/s
  • LFM2.5-2.6BRTX 3090 24GB · vllm 0.26.0 · BF164 contexts × 5 concurrency levels
    Peak decode
    1,553.7 tok/s
    Peak prefill
    11K tok/s
  • Qwen3-0.6BRTX 3090 24GB · vllm 0.26.0 · BF164 contexts × 5 concurrency levels
    Peak decode
    1,635.7 tok/s
    Peak prefill
    30.8K tok/s
Personal bests

Personal bests19

Fastest approved result for each model.

1B MoE4 runs
Output303 tok/s
Prefill—
TTFT144 ms
Depth0k
RTX 3090 48GB ×2

llama.cpp · Q8_0 · 5mo ago

8B MoE1 run
Output233 tok/s
Prefill9,243 tok/s
TTFT47.8 ms
Depth—
RTX 3090 24GB ×2

llama.cpp · Q8_0 · 2mo ago

27B3 runs
Output193 tok/s
Prefill1,639 tok/s
TTFT677 ms
Depth—
RTX 3090 48GB ×2

sglang · W4A16-AutoRound · 2mo ago

9B4 runs
Output152 tok/s
Prefill435 tok/s
TTFT252 ms
Depth—
RTX 3090 24GB

vllm · Q8_0 · 2mo ago

3B MoE2 runs
Output138 tok/s
Prefill2,008 tok/s
TTFT1,019 ms
Depth0k
Tenstorrent P150 32GB

vllm · BFP4 · 2w ago

27B1 run
Output96.6 tok/s
Prefill1,311 tok/s
TTFT796 ms
Depth—
RTX 3090 24GB ×2

vllm · GPTQ-4bit · 2mo ago

9B1 run
Output76.2 tok/s
Prefill—
TTFT209 ms
Depth0k
Tenstorrent P150 32GB

vllm · TT-BFP4-BF16-mixed · 1mo ago

27B1 run
Output73.0 tok/s
Prefill888 tok/s
TTFT1,175 ms
Depth1k
RTX 3090 24GB ×2

bunn-llama · Q4_K_XL · 2mo ago

27B7 runs
Output70.8 tok/s
Prefill2,677 tok/s
TTFT374 ms
Depth—
RTX 3090 48GB ×2

llama.cpp · Q4_K_XL · 5mo ago

9B1 run
Output70.6 tok/s
Prefill4,425 tok/s
TTFT1,051 ms
Depth5k
RTX 3090 48GB ×2

llama.cpp · Q8_K_XL · 4mo ago

27B3 runs
Output56.4 tok/s
Prefill1,212 tok/s
TTFT1,689 ms
Depth0k
Tenstorrent P150 32GB

vllm · BFP4 · 1mo ago

12B3 runs
Output53.9 tok/s
Prefill3,063 tok/s
TTFT651 ms
Depth0k
Tenstorrent P150 32GB

vllm · BFP8 · 3w ago

12B3 runs
Output53.0 tok/s
Prefill2,862 tok/s
TTFT349 ms
Depth—
RTX 3090 48GB ×2

llama.cpp · Q8_K_XL · 4mo ago

118B MoE3 runs
Output46.5 tok/s
Prefill493 tok/s
TTFT4,151 ms
Depth0k
RTX 3090 48GB ×2

llama.cpp · IQ4_NL · 2mo ago

27B4 runs
Output45.2 tok/s
Prefill1,211 tok/s
TTFT210 ms
Depth—
RTX 3090 48GB ×2

vllm · Q4_K_M · 5mo ago

9B1 run
Output38.6 tok/s
Prefill796 tok/s
TTFT676 ms
Depth0k
Tenstorrent P150 32GB

vllm · bfp_bf4 · 1mo ago

30B1 run
Output31.7 tok/s
Prefill1,604 tok/s
TTFT176 ms
Depth—
RTX 3090 24GB ×2

llama.cpp · Unsloth-Dynamic-Q6_K_XL · 2mo ago

180B MoE2 runs
Output27.5 tok/s
Prefill160 tok/s
TTFT2,181 ms
Depth0k
Tenstorrent P150 32GB

strata · IQ3_S · 2d ago

7B1 run🔥
Output19.9 tok/s
Prefill1,821 tok/s
TTFT316 ms
Depth0k
Tenstorrent P150 32GB

vllm · bfp_bf8 · 1mo ago

Hardware breakdown

Hardware breakdown4

Distinct rigs used across approved submissions.

RTX 3090 48GB ×2

24 runs · best 303 tok/s

Tenstorrent P150 32GB

13 runs · best 138 tok/s

RTX 3090 24GB ×2

5 runs · best 233 tok/s

RTX 3090 24GB

4 runs · best 152 tok/s

Engines used

Engines used6

Runtime mix and top quantizations.

llama.cpp24Q8_0 · Unsloth-Dynamic-Q6_K_XL · IQ4_NL
vllm18BFP4 · BFP8 · TT-BFP4-BF16-mixed
strata1IQ3_S
custom1IQ3_S
sglang1W4A16-AutoRound
bunn-llama1Q4_K_XL
All runs

All runs46

Full approved speed test history · page 2 of 2.

43.7

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · IQ4_NL

TTFT

1,162 ms

Context

2k · 2mo ago

Show all run details

Model

unsloth/Laguna-S-2.1-GGUF

Display name

Laguna-S-2.1-GGUF

Base model

Laguna-S-2.1

Revision

main

Family

—

Parameters

118B

Active params

—

MoE

yes

Output

43.7 tok/s

Prefill

441 tok/s

Total

157 tok/s

TTFT

1,162 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

512

Output tokens

128

Prefill tokens

—

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

IQ4_NL

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

—

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

—

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

/home/lotto/llama.cpp-laguna/build/bin/llama-bench -m /home/lotto/models/Laguna-S-2.1-GGUF/UD-IQ4_NL/Laguna-S-2.1-UD-IQ4_NL-00001-of-00003.gguf -fa on -sm layer -fitt 1024 -r 3 -o json

Extra flags

--sm layer --fitt 1024 --r 3 --o json

Notes

—

Reactions

—

Submitted

Aug 4, 2026, 3:43 PM

Last edited

—

43.4

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · IQ4_NL

TTFT

118 ms

Context

2k · 2mo ago

Show all run details

Model

unsloth/Laguna-S-2.1-GGUF

Display name

Laguna-S-2.1-GGUF

Base model

Laguna-S-2.1

Revision

main

Family

—

Parameters

118B

Active params

—

MoE

yes

Output

43.4 tok/s

Prefill

535 tok/s

Total

62.7 tok/s

TTFT

118 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

63

Output tokens

128

Prefill tokens

—

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

IQ4_NL

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

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

—

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://127.0.0.1:8080 servedModel: /home/lotto/models/Laguna-S-2.1-GGUF/UD-IQ4_NL/Laguna-S-2.1-UD-IQ4_NL-00001-of-00003.gguf

Extra flags

—

Notes

—

Reactions

—

Submitted

Aug 4, 2026, 3:41 PM

Last edited

—

48.7

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q8_K_XL

TTFT

122 ms

Context

2k · 4mo ago

Show all run details

Model

unsloth/gemma-4-12b-it-GGUF

Display name

gemma-4-12b-it-GGUF

Base model

gemma-4-12B-it

Revision

main

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

48.7 tok/s

Prefill

278 tok/s

Total

84.6 tok/s

TTFT

122 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

34

Output tokens

32

Prefill tokens

—

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q8_K_XL

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

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

—

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://127.0.0.1:8011 servedModel: gemma-4-12b-it

Extra flags

—

Notes

—

Reactions

—

Submitted

Jun 14, 2026, 9:52 PM

Last edited

—

70.6

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q8_K_XL

TTFT

1,051 ms

Context

262k · 4mo ago

Show all run details

Model

unsloth/Qwen3.5-9B-MTP-GGUF

Display name

Qwen3.5-9B-MTP-GGUF

Base model

Qwen3.5-9B

Revision

main

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

70.6 tok/s

Prefill

4,425 tok/s

Total

—

TTFT

1,051 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

4650

Output tokens

64

Prefill tokens

4650

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

b1-8ad8aef

Quantization

Q8_K_XL

Backend

cuda

Tensor parallel

1

Pipeline parallel

—

GPU layers

999

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

—

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

llama-server -m Qwen3.5-9B-UD-Q8_K_XL.gguf --ctx-size 262144 -ngl 999 -c 262144 --host 0.0.0.0 --port 8080

Extra flags

—

Notes

MTP-capable GGUF but MTP is disabled in llama.cpp launch (no --mtp / speculative decoding flags). Model emits reasoning_content only; content field is always empty. TTFT ≈ prefill_ms from llama.cpp timings. Decode speed measured at ~4.6K prompt tokens. KV cache stress tested up to 23.7K tokens with graceful ~25% decode degradation (70.6 → 54.6 tps).

Reactions

—

Submitted

Jun 4, 2026, 5:41 AM

Last edited

—

48.0

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q8_K_XL

TTFT

1,035 ms

Context

131k · 4mo ago

Show all run details

Model

unsloth/gemma-4-12b-it-GGUF

Display name

gemma-4-12b-it-GGUF

Base model

gemma-4-12B-it

Revision

main

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

48.0 tok/s

Prefill

—

Total

40.9 tok/s

TTFT

1,035 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

100000

Output tokens

285

Prefill tokens

100000

Context length

131072

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

b1-8ad8aef

Quantization

Q8_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

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

llama-server -m gemma-4-12b-it-UD-Q8_K_XL.gguf -ngl 999 -fa1 -c 0

Extra flags

--fa1

Notes

100K context tokens in KV cache. AMD Ryzen 9 9950X, 96GB DDR5. Gemma-4-12B-it (11.9B params). Q8_K_XL quant. Standard llama-server launch, no MTP. Prefill TPS not measured accurately.

Reactions

—

Submitted

Jun 4, 2026, 12:43 AM

Last edited

—

53.0

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q8_K_XL

TTFT

349 ms

Context

131k · 4mo ago

Show all run details

Model

unsloth/gemma-4-12b-it-GGUF

Display name

gemma-4-12b-it-GGUF

Base model

gemma-4-12B-it

Revision

main

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

53.0 tok/s

Prefill

2,862 tok/s

Total

49.7 tok/s

TTFT

349 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

1000

Output tokens

284

Prefill tokens

—

Context length

131072

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

b1-8ad8aef

Quantization

Q8_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

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

llama-server -m gemma-4-12b-it-UD-Q8_K_XL.gguf -ngl 999 -fa1 -c 0

Extra flags

--fa1

Notes

AMD Ryzen 9 9950X, 96GB DDR5. Gemma-4-12B-it (11.9B params). Reasoning model with 262K vocab. Q8_K_XL quant. Standard llama-server launch, no MTP.

Reactions

—

Submitted

Jun 3, 2026, 10:45 PM

Last edited

—

LFM2.5-8B-A1B-GGUF

1B MoE · Llama

227

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q8_0

TTFT

458 ms

Context

128k · 5mo ago

Show all run details

Model

LiquidAI/LFM2.5-8B-A1B-GGUF

Display name

LFM2.5-8B-A1B-GGUF

Base model

LFM2.5-8B-A1B

Revision

main

Family

Llama

Parameters

8B

Active params

1B

MoE

yes

Output

227 tok/s

Prefill

—

Total

195 tok/s

TTFT

458 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

100000

Output tokens

512

Prefill tokens

100000

Context length

128000

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q8_0

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

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

llama-server -m LFM2.5-8B-A1B-Q8_0.gguf -ngl 999 -fa1 -c 0

Extra flags

--fa1

Notes

100,000 tokens in KV cache (near max 128K context). 351 output tokens.

Reactions

—

Submitted

May 29, 2026, 6:14 AM

Last edited

—

LFM2.5-8B-A1B-GGUF

1B MoE · Llama

259

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q8_0

TTFT

382 ms

Context

128k · 5mo ago

Show all run details

Model

LiquidAI/LFM2.5-8B-A1B-GGUF

Display name

LFM2.5-8B-A1B-GGUF

Base model

LFM2.5-8B-A1B

Revision

main

Family

Llama

Parameters

8B

Active params

1B

MoE

yes

Output

259 tok/s

Prefill

—

Total

190 tok/s

TTFT

382 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

50000

Output tokens

512

Prefill tokens

50000

Context length

128000

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q8_0

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

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

llama-server -m LFM2.5-8B-A1B-Q8_0.gguf -ngl 999 -fa1 -c 0

Extra flags

--fa1

Notes

50,000 tokens in KV cache. 269 output tokens.

Reactions

—

Submitted

May 29, 2026, 6:14 AM

Last edited

—

LFM2.5-8B-A1B-GGUF

1B MoE · Llama

293

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q8_0

TTFT

251 ms

Context

128k · 5mo ago

Show all run details

Model

LiquidAI/LFM2.5-8B-A1B-GGUF

Display name

LFM2.5-8B-A1B-GGUF

Base model

LFM2.5-8B-A1B

Revision

main

Family

Llama

Parameters

8B

Active params

1B

MoE

yes

Output

293 tok/s

Prefill

—

Total

256 tok/s

TTFT

251 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

10000

Output tokens

512

Prefill tokens

10000

Context length

128000

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q8_0

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

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

llama-server -m LFM2.5-8B-A1B-Q8_0.gguf -ngl 999 -fa1 -c 0

Extra flags

--fa1

Notes

10,000 tokens in KV cache. 512 output tokens.

Reactions

—

Submitted

May 29, 2026, 6:14 AM

Last edited

—

LFM2.5-8B-A1B-GGUF

1B MoE · Llama

303

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q8_0

TTFT

144 ms

Context

128k · 5mo ago

Show all run details

Model

LiquidAI/LFM2.5-8B-A1B-GGUF

Display name

LFM2.5-8B-A1B-GGUF

Base model

LFM2.5-8B-A1B

Revision

main

Family

Llama

Parameters

8B

Active params

1B

MoE

yes

Output

303 tok/s

Prefill

—

Total

279 tok/s

TTFT

144 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

0

Output tokens

512

Prefill tokens

0

Context length

128000

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q8_0

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

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

llama-server -m LFM2.5-8B-A1B-Q8_0.gguf -ngl 999 -fa1 -c 0

Extra flags

--fa1

Notes

Cold start (no KV cache). 512 output tokens.

Reactions

—

Submitted

May 29, 2026, 6:14 AM

Last edited

—

48.0

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q4_K_XL

TTFT

897 ms

Context

262k · 5mo ago

Show all run details

Model

unsloth/Qwen3.6-27B-MTP-GGUF

Display name

Qwen3.6-27B-MTP-GGUF

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

48.0 tok/s

Prefill

—

Total

44.3 tok/s

TTFT

897 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

200000

Output tokens

512

Prefill tokens

200000

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q4_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

Spec decoding

yes

Spec method

draft-mtp

Spec model

—

Spec draft model

—

Spec tokens

2

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

llama-server -m Qwen3.6-27B-UD-Q4_K_XL.gguf -ngl 999 -fa1 -c 0 --spec-type draft-mtp --spec-draft-n-max 2

Extra flags

--fa1 --spec-type draft-mtp --spec-draft-n-max 2

Notes

KV cache stress test. 200,000 tokens in cache before generation. ~26% decode penalty vs cold start.

Reactions

—

Submitted

May 29, 2026, 5:49 AM

Last edited

—

59.3

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q4_K_XL

TTFT

503 ms

Context

262k · 5mo ago

Show all run details

Model

unsloth/Qwen3.6-27B-MTP-GGUF

Display name

Qwen3.6-27B-MTP-GGUF

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

59.3 tok/s

Prefill

—

Total

56.1 tok/s

TTFT

503 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

50000

Output tokens

512

Prefill tokens

50000

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q4_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

Spec decoding

yes

Spec method

draft-mtp

Spec model

—

Spec draft model

—

Spec tokens

2

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

llama-server -m Qwen3.6-27B-UD-Q4_K_XL.gguf -ngl 999 -fa1 -c 0 --spec-type draft-mtp --spec-draft-n-max 2

Extra flags

--fa1 --spec-type draft-mtp --spec-draft-n-max 2

Notes

KV cache stress test. 50,000 tokens in cache before generation.

Reactions

—

Submitted

May 29, 2026, 5:49 AM

Last edited

—

65.8

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q4_K_XL

TTFT

320 ms

Context

262k · 5mo ago

Show all run details

Model

unsloth/Qwen3.6-27B-MTP-GGUF

Display name

Qwen3.6-27B-MTP-GGUF

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

65.8 tok/s

Prefill

—

Total

63.2 tok/s

TTFT

320 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

10000

Output tokens

512

Prefill tokens

10000

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q4_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

Spec decoding

yes

Spec method

draft-mtp

Spec model

—

Spec draft model

—

Spec tokens

2

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

llama-server -m Qwen3.6-27B-UD-Q4_K_XL.gguf -ngl 999 -fa1 -c 0 --spec-type draft-mtp --spec-draft-n-max 2

Extra flags

--fa1 --spec-type draft-mtp --spec-draft-n-max 2

Notes

KV cache stress test. 10,000 tokens in cache before generation.

Reactions

—

Submitted

May 29, 2026, 5:49 AM

Last edited

—

65.2

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q4_K_XL

TTFT

567 ms

Context

262k · 5mo ago

Show all run details

Model

unsloth/Qwen3.6-27B-MTP-GGUF

Display name

Qwen3.6-27B-MTP-GGUF

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

65.2 tok/s

Prefill

—

Total

60.8 tok/s

TTFT

567 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

0

Output tokens

512

Prefill tokens

0

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q4_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

Spec decoding

yes

Spec method

draft-mtp

Spec model

—

Spec draft model

—

Spec tokens

2

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

llama-server -m Qwen3.6-27B-UD-Q4_K_XL.gguf -ngl 999 -fa1 -c 0 --spec-type draft-mtp --spec-draft-n-max 2

Extra flags

--fa1 --spec-type draft-mtp --spec-draft-n-max 2

Notes

KV cache stress test. 0 tokens in cache (cold start).

Reactions

—

Submitted

May 29, 2026, 5:48 AM

Last edited

—

64.9

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q4_K_XL

TTFT

497 ms

Context

262k · 5mo ago

Show all run details

Model

unsloth/Qwen3.6-27B-MTP-GGUF

Display name

Qwen3.6-27B-MTP-GGUF

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

64.9 tok/s

Prefill

603 tok/s

Total

61.2 tok/s

TTFT

497 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

300

Output tokens

544

Prefill tokens

—

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q4_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

Spec decoding

yes

Spec method

draft-mtp

Spec model

—

Spec draft model

—

Spec tokens

2

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

llama-server -m Qwen3.6-27B-UD-Q4_K_XL.gguf -ngl 999 -fa1 -c 0 --spec-type draft-mtp --spec-draft-n-max 2

Extra flags

--fa1 --spec-type draft-mtp --spec-draft-n-max 2

Notes

Unsloth Dynamic quantization. tokS includes reasoning_content + visible content. MTP speculative decoding (2 draft tokens). -fa1 flash attention. -c 0 = full 262K context.

Reactions

—

Submitted

May 29, 2026, 5:26 AM

Last edited

—

65.3

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q4_K_XL

TTFT

336 ms

Context

262k · 5mo ago

Show all run details

Model

unsloth/Qwen3.6-27B-MTP-GGUF

Display name

Qwen3.6-27B-MTP-GGUF

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

65.3 tok/s

Prefill

89.2 tok/s

Total

60.7 tok/s

TTFT

336 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

30

Output tokens

288

Prefill tokens

—

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q4_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

Spec decoding

yes

Spec method

draft-mtp

Spec model

—

Spec draft model

—

Spec tokens

2

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

llama-server -m Qwen3.6-27B-UD-Q4_K_XL.gguf -ngl 999 -fa1 -c 0 --spec-type draft-mtp --spec-draft-n-max 2

Extra flags

--fa1 --spec-type draft-mtp --spec-draft-n-max 2

Notes

Unsloth Dynamic quantization. tokS includes reasoning_content + visible content. MTP speculative decoding (2 draft tokens). -fa1 flash attention. -c 0 = full 262K context.

Reactions

—

Submitted

May 29, 2026, 5:25 AM

Last edited

—

70.8

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · Q4_K_XL

TTFT

374 ms

Context

262k · 5mo ago

Show all run details

Model

unsloth/Qwen3.6-27B-MTP-GGUF

Display name

Qwen3.6-27B-MTP-GGUF

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

70.8 tok/s

Prefill

2,677 tok/s

Total

69.1 tok/s

TTFT

374 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

1000

Output tokens

1056

Prefill tokens

—

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q4_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

Spec decoding

yes

Spec method

draft-mtp

Spec model

—

Spec draft model

—

Spec tokens

2

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

llama-server -m Qwen3.6-27B-UD-Q4_K_XL.gguf -ngl 999 -fa1 -c 0 --spec-type draft-mtp --spec-draft-n-max 2

Extra flags

--fa1 --spec-type draft-mtp --spec-draft-n-max 2

Notes

Unsloth Dynamic quantization. tokS includes reasoning_content + visible content. MTP speculative decoding (2 draft tokens). -fa1 flash attention. -c 0 = full 262K context.

Reactions

—

Submitted

May 29, 2026, 5:25 AM

Last edited

—

Qwen3.6-27B

27B · Qwen

45.2

tok/s

Hardware

RTX 3090 48GB ×2

Engine

vllm · Q4_K_M

TTFT

210 ms

Context

8k · 5mo ago

Show all run details

Model

unsloth/Qwen3.6-27B

Display name

Qwen3.6-27B

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

45.2 tok/s

Prefill

1,211 tok/s

Total

42.8 tok/s

TTFT

210 ms

Peak VRAM

24.3GB

Power draw

—

Hardware cost

—

Prompt tokens

512

Output tokens

256

Prefill tokens

—

Context length

8192

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

vllm

Engine version

0.8.4

Quantization

Q4_K_M

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

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

—

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

Extra flags

—

Notes

—

Reactions

—

Submitted

May 23, 2026, 2:15 PM

Last edited

—

Qwen3.6-27B

27B · Qwen

40.2

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · IQ4_NL

TTFT

475 ms

Context

132k · 6mo ago

Show all run details

Model

unsloth/Qwen3.6-27B

Display name

Qwen3.6-27B

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

40.2 tok/s

Prefill

—

Total

—

TTFT

475 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

30

Output tokens

256

Prefill tokens

—

Context length

131712

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD 7790

RAM

96GB

OS

windows 11

Power

—

Engine

llama.cpp

Engine version

—

Quantization

IQ4_NL

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

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

—

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

Extra flags

—

Notes

LM Studio on Win11, 96GB RAM, 2x RTX 3090. Flash attention enabled.

Reactions

—

Submitted

Apr 28, 2026, 7:49 PM

Last edited

—

Qwen3.6-27B

27B · Qwen

40.4

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · IQ4_NL

TTFT

463 ms

Context

132k · 6mo ago

Show all run details

Model

unsloth/Qwen3.6-27B

Display name

Qwen3.6-27B

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

40.4 tok/s

Prefill

—

Total

—

TTFT

463 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

30

Output tokens

256

Prefill tokens

—

Context length

131712

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD 7790

RAM

96GB

OS

windows 11

Power

—

Engine

llama.cpp

Engine version

—

Quantization

IQ4_NL

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

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

—

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

Extra flags

—

Notes

Test submission - LM Studio on Win11, 96GB RAM, 2x RTX 3090. Flash attention enabled.

Reactions

—

Submitted

Apr 28, 2026, 5:02 AM

Last edited

—

Qwen3.6-27B

27B · Qwen

42.1

tok/s

Hardware

RTX 3090 48GB ×2

Engine

llama.cpp · IQ4_NL

TTFT

229 ms

Context

132k · 6mo ago

Show all run details

Model

unsloth/Qwen3.6-27B

Display name

Qwen3.6-27B

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

42.1 tok/s

Prefill

—

Total

—

TTFT

229 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

25

Output tokens

256

Prefill tokens

—

Context length

131712

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD 7790

RAM

96GB

OS

windows 11

Power

—

Engine

llama.cpp

Engine version

—

Quantization

IQ4_NL

Backend

—

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

—

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

Extra flags

—

Notes

LM Studio on Win11, 96GB RAM, dual RTX 3090 (48GB VRAM), flash_attention=true, parallel=2 instances

Reactions

—

Submitted

Apr 25, 2026, 3:56 AM

Last edited

—

ModelHardwareEnginetok/s outprefilltok/s totalTTFTDepthShare
Laguna-S-2.1-GGUF

118B MoE

RTX 3090 48GB ×2
llama.cppIQ4_NL43.74411571,162 ms—
Show all details for Laguna-S-2.1-GGUF

Model

unsloth/Laguna-S-2.1-GGUF

Display name

Laguna-S-2.1-GGUF

Base model

Laguna-S-2.1

Revision

main

Family

—

Parameters

118B

Active params

—

MoE

yes

Output

43.7 tok/s

Prefill

441 tok/s

Total

157 tok/s

TTFT

1,162 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

512

Output tokens

128

Prefill tokens

—

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

IQ4_NL

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

—

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

—

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

/home/lotto/llama.cpp-laguna/build/bin/llama-bench -m /home/lotto/models/Laguna-S-2.1-GGUF/UD-IQ4_NL/Laguna-S-2.1-UD-IQ4_NL-00001-of-00003.gguf -fa on -sm layer -fitt 1024 -r 3 -o json

Extra flags

--sm layer --fitt 1024 --r 3 --o json

Notes

—

Reactions

—

Submitted

Aug 4, 2026, 3:43 PM

Last edited

—

Laguna-S-2.1-GGUF

118B MoE

RTX 3090 48GB ×2
llama.cppIQ4_NL43.453562.7118 ms—
Show all details for Laguna-S-2.1-GGUF

Model

unsloth/Laguna-S-2.1-GGUF

Display name

Laguna-S-2.1-GGUF

Base model

Laguna-S-2.1

Revision

main

Family

—

Parameters

118B

Active params

—

MoE

yes

Output

43.4 tok/s

Prefill

535 tok/s

Total

62.7 tok/s

TTFT

118 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

63

Output tokens

128

Prefill tokens

—

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

IQ4_NL

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

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

—

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://127.0.0.1:8080 servedModel: /home/lotto/models/Laguna-S-2.1-GGUF/UD-IQ4_NL/Laguna-S-2.1-UD-IQ4_NL-00001-of-00003.gguf

Extra flags

—

Notes

—

Reactions

—

Submitted

Aug 4, 2026, 3:41 PM

Last edited

—

gemma-4-12b-it-GGUF

12B · Gemma

RTX 3090 48GB ×2
llama.cppQ8_K_XL48.727884.6122 ms—
Show all details for gemma-4-12b-it-GGUF

Model

unsloth/gemma-4-12b-it-GGUF

Display name

gemma-4-12b-it-GGUF

Base model

gemma-4-12B-it

Revision

main

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

48.7 tok/s

Prefill

278 tok/s

Total

84.6 tok/s

TTFT

122 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

34

Output tokens

32

Prefill tokens

—

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q8_K_XL

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

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

—

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://127.0.0.1:8011 servedModel: gemma-4-12b-it

Extra flags

—

Notes

—

Reactions

—

Submitted

Jun 14, 2026, 9:52 PM

Last edited

—

Qwen3.5-9B-MTP-GGUF

9B · Qwen

RTX 3090 48GB ×2
llama.cppQ8_K_XL70.64,425—1,051 ms5k
Show all details for Qwen3.5-9B-MTP-GGUF

Model

unsloth/Qwen3.5-9B-MTP-GGUF

Display name

Qwen3.5-9B-MTP-GGUF

Base model

Qwen3.5-9B

Revision

main

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

70.6 tok/s

Prefill

4,425 tok/s

Total

—

TTFT

1,051 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

4650

Output tokens

64

Prefill tokens

4650

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

b1-8ad8aef

Quantization

Q8_K_XL

Backend

cuda

Tensor parallel

1

Pipeline parallel

—

GPU layers

999

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

—

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

llama-server -m Qwen3.5-9B-UD-Q8_K_XL.gguf --ctx-size 262144 -ngl 999 -c 262144 --host 0.0.0.0 --port 8080

Extra flags

—

Notes

MTP-capable GGUF but MTP is disabled in llama.cpp launch (no --mtp / speculative decoding flags). Model emits reasoning_content only; content field is always empty. TTFT ≈ prefill_ms from llama.cpp timings. Decode speed measured at ~4.6K prompt tokens. KV cache stress tested up to 23.7K tokens with graceful ~25% decode degradation (70.6 → 54.6 tps).

Reactions

—

Submitted

Jun 4, 2026, 5:41 AM

Last edited

—

gemma-4-12b-it-GGUF

12B · Gemma

RTX 3090 48GB ×2
llama.cppQ8_K_XL48.0—40.91,035 ms100k
Show all details for gemma-4-12b-it-GGUF

Model

unsloth/gemma-4-12b-it-GGUF

Display name

gemma-4-12b-it-GGUF

Base model

gemma-4-12B-it

Revision

main

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

48.0 tok/s

Prefill

—

Total

40.9 tok/s

TTFT

1,035 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

100000

Output tokens

285

Prefill tokens

100000

Context length

131072

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

b1-8ad8aef

Quantization

Q8_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

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

llama-server -m gemma-4-12b-it-UD-Q8_K_XL.gguf -ngl 999 -fa1 -c 0

Extra flags

--fa1

Notes

100K context tokens in KV cache. AMD Ryzen 9 9950X, 96GB DDR5. Gemma-4-12B-it (11.9B params). Q8_K_XL quant. Standard llama-server launch, no MTP. Prefill TPS not measured accurately.

Reactions

—

Submitted

Jun 4, 2026, 12:43 AM

Last edited

—

gemma-4-12b-it-GGUF

12B · Gemma

RTX 3090 48GB ×2
llama.cppQ8_K_XL53.02,86249.7349 ms—
Show all details for gemma-4-12b-it-GGUF

Model

unsloth/gemma-4-12b-it-GGUF

Display name

gemma-4-12b-it-GGUF

Base model

gemma-4-12B-it

Revision

main

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

53.0 tok/s

Prefill

2,862 tok/s

Total

49.7 tok/s

TTFT

349 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

1000

Output tokens

284

Prefill tokens

—

Context length

131072

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

b1-8ad8aef

Quantization

Q8_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

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

llama-server -m gemma-4-12b-it-UD-Q8_K_XL.gguf -ngl 999 -fa1 -c 0

Extra flags

--fa1

Notes

AMD Ryzen 9 9950X, 96GB DDR5. Gemma-4-12B-it (11.9B params). Reasoning model with 262K vocab. Q8_K_XL quant. Standard llama-server launch, no MTP.

Reactions

—

Submitted

Jun 3, 2026, 10:45 PM

Last edited

—

LFM2.5-8B-A1B-GGUF

1B MoE · Llama

RTX 3090 48GB ×2
llama.cppQ8_0227—195458 ms100k
Show all details for LFM2.5-8B-A1B-GGUF

Model

LiquidAI/LFM2.5-8B-A1B-GGUF

Display name

LFM2.5-8B-A1B-GGUF

Base model

LFM2.5-8B-A1B

Revision

main

Family

Llama

Parameters

8B

Active params

1B

MoE

yes

Output

227 tok/s

Prefill

—

Total

195 tok/s

TTFT

458 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

100000

Output tokens

512

Prefill tokens

100000

Context length

128000

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q8_0

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

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

llama-server -m LFM2.5-8B-A1B-Q8_0.gguf -ngl 999 -fa1 -c 0

Extra flags

--fa1

Notes

100,000 tokens in KV cache (near max 128K context). 351 output tokens.

Reactions

—

Submitted

May 29, 2026, 6:14 AM

Last edited

—

LFM2.5-8B-A1B-GGUF

1B MoE · Llama

RTX 3090 48GB ×2
llama.cppQ8_0259—190382 ms50k
Show all details for LFM2.5-8B-A1B-GGUF

Model

LiquidAI/LFM2.5-8B-A1B-GGUF

Display name

LFM2.5-8B-A1B-GGUF

Base model

LFM2.5-8B-A1B

Revision

main

Family

Llama

Parameters

8B

Active params

1B

MoE

yes

Output

259 tok/s

Prefill

—

Total

190 tok/s

TTFT

382 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

50000

Output tokens

512

Prefill tokens

50000

Context length

128000

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q8_0

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

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

llama-server -m LFM2.5-8B-A1B-Q8_0.gguf -ngl 999 -fa1 -c 0

Extra flags

--fa1

Notes

50,000 tokens in KV cache. 269 output tokens.

Reactions

—

Submitted

May 29, 2026, 6:14 AM

Last edited

—

LFM2.5-8B-A1B-GGUF

1B MoE · Llama

RTX 3090 48GB ×2
llama.cppQ8_0293—256251 ms10k
Show all details for LFM2.5-8B-A1B-GGUF

Model

LiquidAI/LFM2.5-8B-A1B-GGUF

Display name

LFM2.5-8B-A1B-GGUF

Base model

LFM2.5-8B-A1B

Revision

main

Family

Llama

Parameters

8B

Active params

1B

MoE

yes

Output

293 tok/s

Prefill

—

Total

256 tok/s

TTFT

251 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

10000

Output tokens

512

Prefill tokens

10000

Context length

128000

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q8_0

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

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

llama-server -m LFM2.5-8B-A1B-Q8_0.gguf -ngl 999 -fa1 -c 0

Extra flags

--fa1

Notes

10,000 tokens in KV cache. 512 output tokens.

Reactions

—

Submitted

May 29, 2026, 6:14 AM

Last edited

—

LFM2.5-8B-A1B-GGUF

1B MoE · Llama

RTX 3090 48GB ×2
llama.cppQ8_0303—279144 ms0k
Show all details for LFM2.5-8B-A1B-GGUF

Model

LiquidAI/LFM2.5-8B-A1B-GGUF

Display name

LFM2.5-8B-A1B-GGUF

Base model

LFM2.5-8B-A1B

Revision

main

Family

Llama

Parameters

8B

Active params

1B

MoE

yes

Output

303 tok/s

Prefill

—

Total

279 tok/s

TTFT

144 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

0

Output tokens

512

Prefill tokens

0

Context length

128000

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q8_0

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

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

llama-server -m LFM2.5-8B-A1B-Q8_0.gguf -ngl 999 -fa1 -c 0

Extra flags

--fa1

Notes

Cold start (no KV cache). 512 output tokens.

Reactions

—

Submitted

May 29, 2026, 6:14 AM

Last edited

—

Qwen3.6-27B-MTP-GGUF

27B · Qwen

RTX 3090 48GB ×2
llama.cppQ4_K_XL48.0—44.3897 ms200k
Show all details for Qwen3.6-27B-MTP-GGUF

Model

unsloth/Qwen3.6-27B-MTP-GGUF

Display name

Qwen3.6-27B-MTP-GGUF

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

48.0 tok/s

Prefill

—

Total

44.3 tok/s

TTFT

897 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

200000

Output tokens

512

Prefill tokens

200000

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q4_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

Spec decoding

yes

Spec method

draft-mtp

Spec model

—

Spec draft model

—

Spec tokens

2

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

llama-server -m Qwen3.6-27B-UD-Q4_K_XL.gguf -ngl 999 -fa1 -c 0 --spec-type draft-mtp --spec-draft-n-max 2

Extra flags

--fa1 --spec-type draft-mtp --spec-draft-n-max 2

Notes

KV cache stress test. 200,000 tokens in cache before generation. ~26% decode penalty vs cold start.

Reactions

—

Submitted

May 29, 2026, 5:49 AM

Last edited

—

Qwen3.6-27B-MTP-GGUF

27B · Qwen

RTX 3090 48GB ×2
llama.cppQ4_K_XL59.3—56.1503 ms50k
Show all details for Qwen3.6-27B-MTP-GGUF

Model

unsloth/Qwen3.6-27B-MTP-GGUF

Display name

Qwen3.6-27B-MTP-GGUF

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

59.3 tok/s

Prefill

—

Total

56.1 tok/s

TTFT

503 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

50000

Output tokens

512

Prefill tokens

50000

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q4_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

Spec decoding

yes

Spec method

draft-mtp

Spec model

—

Spec draft model

—

Spec tokens

2

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

llama-server -m Qwen3.6-27B-UD-Q4_K_XL.gguf -ngl 999 -fa1 -c 0 --spec-type draft-mtp --spec-draft-n-max 2

Extra flags

--fa1 --spec-type draft-mtp --spec-draft-n-max 2

Notes

KV cache stress test. 50,000 tokens in cache before generation.

Reactions

—

Submitted

May 29, 2026, 5:49 AM

Last edited

—

Qwen3.6-27B-MTP-GGUF

27B · Qwen

RTX 3090 48GB ×2
llama.cppQ4_K_XL65.8—63.2320 ms10k
Show all details for Qwen3.6-27B-MTP-GGUF

Model

unsloth/Qwen3.6-27B-MTP-GGUF

Display name

Qwen3.6-27B-MTP-GGUF

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

65.8 tok/s

Prefill

—

Total

63.2 tok/s

TTFT

320 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

10000

Output tokens

512

Prefill tokens

10000

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q4_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

Spec decoding

yes

Spec method

draft-mtp

Spec model

—

Spec draft model

—

Spec tokens

2

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

llama-server -m Qwen3.6-27B-UD-Q4_K_XL.gguf -ngl 999 -fa1 -c 0 --spec-type draft-mtp --spec-draft-n-max 2

Extra flags

--fa1 --spec-type draft-mtp --spec-draft-n-max 2

Notes

KV cache stress test. 10,000 tokens in cache before generation.

Reactions

—

Submitted

May 29, 2026, 5:49 AM

Last edited

—

Qwen3.6-27B-MTP-GGUF

27B · Qwen

RTX 3090 48GB ×2
llama.cppQ4_K_XL65.2—60.8567 ms0k
Show all details for Qwen3.6-27B-MTP-GGUF

Model

unsloth/Qwen3.6-27B-MTP-GGUF

Display name

Qwen3.6-27B-MTP-GGUF

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

65.2 tok/s

Prefill

—

Total

60.8 tok/s

TTFT

567 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

0

Output tokens

512

Prefill tokens

0

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q4_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

Spec decoding

yes

Spec method

draft-mtp

Spec model

—

Spec draft model

—

Spec tokens

2

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

llama-server -m Qwen3.6-27B-UD-Q4_K_XL.gguf -ngl 999 -fa1 -c 0 --spec-type draft-mtp --spec-draft-n-max 2

Extra flags

--fa1 --spec-type draft-mtp --spec-draft-n-max 2

Notes

KV cache stress test. 0 tokens in cache (cold start).

Reactions

—

Submitted

May 29, 2026, 5:48 AM

Last edited

—

Qwen3.6-27B-MTP-GGUF

27B · Qwen

RTX 3090 48GB ×2
llama.cppQ4_K_XL64.960361.2497 ms—
Show all details for Qwen3.6-27B-MTP-GGUF

Model

unsloth/Qwen3.6-27B-MTP-GGUF

Display name

Qwen3.6-27B-MTP-GGUF

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

64.9 tok/s

Prefill

603 tok/s

Total

61.2 tok/s

TTFT

497 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

300

Output tokens

544

Prefill tokens

—

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q4_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

Spec decoding

yes

Spec method

draft-mtp

Spec model

—

Spec draft model

—

Spec tokens

2

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

llama-server -m Qwen3.6-27B-UD-Q4_K_XL.gguf -ngl 999 -fa1 -c 0 --spec-type draft-mtp --spec-draft-n-max 2

Extra flags

--fa1 --spec-type draft-mtp --spec-draft-n-max 2

Notes

Unsloth Dynamic quantization. tokS includes reasoning_content + visible content. MTP speculative decoding (2 draft tokens). -fa1 flash attention. -c 0 = full 262K context.

Reactions

—

Submitted

May 29, 2026, 5:26 AM

Last edited

—

Qwen3.6-27B-MTP-GGUF

27B · Qwen

RTX 3090 48GB ×2
llama.cppQ4_K_XL65.389.260.7336 ms—
Show all details for Qwen3.6-27B-MTP-GGUF

Model

unsloth/Qwen3.6-27B-MTP-GGUF

Display name

Qwen3.6-27B-MTP-GGUF

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

65.3 tok/s

Prefill

89.2 tok/s

Total

60.7 tok/s

TTFT

336 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

30

Output tokens

288

Prefill tokens

—

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q4_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

Spec decoding

yes

Spec method

draft-mtp

Spec model

—

Spec draft model

—

Spec tokens

2

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

llama-server -m Qwen3.6-27B-UD-Q4_K_XL.gguf -ngl 999 -fa1 -c 0 --spec-type draft-mtp --spec-draft-n-max 2

Extra flags

--fa1 --spec-type draft-mtp --spec-draft-n-max 2

Notes

Unsloth Dynamic quantization. tokS includes reasoning_content + visible content. MTP speculative decoding (2 draft tokens). -fa1 flash attention. -c 0 = full 262K context.

Reactions

—

Submitted

May 29, 2026, 5:25 AM

Last edited

—

Qwen3.6-27B-MTP-GGUF

27B · Qwen

RTX 3090 48GB ×2
llama.cppQ4_K_XL70.82,67769.1374 ms—
Show all details for Qwen3.6-27B-MTP-GGUF

Model

unsloth/Qwen3.6-27B-MTP-GGUF

Display name

Qwen3.6-27B-MTP-GGUF

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

70.8 tok/s

Prefill

2,677 tok/s

Total

69.1 tok/s

TTFT

374 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

1000

Output tokens

1056

Prefill tokens

—

Context length

262144

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

llama.cpp

Engine version

—

Quantization

Q4_K_XL

Backend

—

Tensor parallel

—

Pipeline parallel

—

GPU layers

999

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

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

—

Spec decoding

yes

Spec method

draft-mtp

Spec model

—

Spec draft model

—

Spec tokens

2

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

llama-server -m Qwen3.6-27B-UD-Q4_K_XL.gguf -ngl 999 -fa1 -c 0 --spec-type draft-mtp --spec-draft-n-max 2

Extra flags

--fa1 --spec-type draft-mtp --spec-draft-n-max 2

Notes

Unsloth Dynamic quantization. tokS includes reasoning_content + visible content. MTP speculative decoding (2 draft tokens). -fa1 flash attention. -c 0 = full 262K context.

Reactions

—

Submitted

May 29, 2026, 5:25 AM

Last edited

—

Qwen3.6-27B

27B · Qwen

RTX 3090 48GB ×2
vllmQ4_K_M45.21,21142.8210 ms—
Show all details for Qwen3.6-27B

Model

unsloth/Qwen3.6-27B

Display name

Qwen3.6-27B

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

45.2 tok/s

Prefill

1,211 tok/s

Total

42.8 tok/s

TTFT

210 ms

Peak VRAM

24.3GB

Power draw

—

Hardware cost

—

Prompt tokens

512

Output tokens

256

Prefill tokens

—

Context length

8192

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD Ryzen

RAM

64GB

OS

Ubuntu

Power

—

Engine

vllm

Engine version

0.8.4

Quantization

Q4_K_M

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

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

—

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

Extra flags

—

Notes

—

Reactions

—

Submitted

May 23, 2026, 2:15 PM

Last edited

—

Qwen3.6-27B

27B · Qwen

RTX 3090 48GB ×2
llama.cppIQ4_NL40.2——475 ms—
Show all details for Qwen3.6-27B

Model

unsloth/Qwen3.6-27B

Display name

Qwen3.6-27B

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

40.2 tok/s

Prefill

—

Total

—

TTFT

475 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

30

Output tokens

256

Prefill tokens

—

Context length

131712

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD 7790

RAM

96GB

OS

windows 11

Power

—

Engine

llama.cpp

Engine version

—

Quantization

IQ4_NL

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

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

—

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

Extra flags

—

Notes

LM Studio on Win11, 96GB RAM, 2x RTX 3090. Flash attention enabled.

Reactions

—

Submitted

Apr 28, 2026, 7:49 PM

Last edited

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Qwen3.6-27B

27B · Qwen

RTX 3090 48GB ×2
llama.cppIQ4_NL40.4——463 ms—
Show all details for Qwen3.6-27B

Model

unsloth/Qwen3.6-27B

Display name

Qwen3.6-27B

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

40.4 tok/s

Prefill

—

Total

—

TTFT

463 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

30

Output tokens

256

Prefill tokens

—

Context length

131712

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD 7790

RAM

96GB

OS

windows 11

Power

—

Engine

llama.cpp

Engine version

—

Quantization

IQ4_NL

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

—

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

—

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

Extra flags

—

Notes

Test submission - LM Studio on Win11, 96GB RAM, 2x RTX 3090. Flash attention enabled.

Reactions

—

Submitted

Apr 28, 2026, 5:02 AM

Last edited

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Qwen3.6-27B

27B · Qwen

RTX 3090 48GB ×2
llama.cppIQ4_NL42.1——229 ms—
Show all details for Qwen3.6-27B

Model

unsloth/Qwen3.6-27B

Display name

Qwen3.6-27B

Base model

Qwen3.6-27B

Revision

main

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

42.1 tok/s

Prefill

—

Total

—

TTFT

229 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

25

Output tokens

256

Prefill tokens

—

Context length

131712

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX 3090 48GB ×2

GPU slots

—

GPU count

2

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD 7790

RAM

96GB

OS

windows 11

Power

—

Engine

llama.cpp

Engine version

—

Quantization

IQ4_NL

Backend

—

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

—

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

Extra flags

—

Notes

LM Studio on Win11, 96GB RAM, dual RTX 3090 (48GB VRAM), flash_attention=true, parallel=2 instances

Reactions

—

Submitted

Apr 25, 2026, 3:56 AM

Last edited

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