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imthekaiser

imthekaiser

Verified

@imthekaiser · Member since 2026

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Verified 1w ago Mainly llama.cpp 200 approved runs

Submissions

1,123

200 approved public runs

Models

24

unique models

Avg output

333tok/s

Best 3,216 tok/s

Avg prefill

372tok/s

Best 3,953 tok/s

Avg total

688tok/s

Best 6,953 tok/s

Avg TTFT

5,197ms

Best 77.2 ms

Hardware

2

distinct setups

Matrix runs

36

speed test matrices

Matrix runs

Matrix runs 36

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

Personal bests24

Fastest approved result for each model.

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

vllm · MXFP4 · today

4B11 runs
Output2,506 tok/s
Prefill136 tok/s
TTFT2,240 ms
Depth0k
RTX A6000 48GB

vllm · BF16 · today

3B MoE6 runs
Output1,901 tok/s
Prefill163 tok/s
TTFT1,823 ms
Depth0k
RTX A6000 48GB

vllm · FP8 · today

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

vllm · FP8 · today

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

vllm · BF16 · today

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

llama.cpp · UD-IQ4_XS · today

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

llama.cpp · UD-Q4_K_XL · today

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

llama.cpp · UD-Q3_K_XL · today

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

vllm · W4A16 · today

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

llama.cpp · UD-Q4_K_XL · today

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

llama.cpp · UD-Q2_K_XL · today

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

llama.cpp · UD-Q2_K_XL · today

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

llama.cpp · UD-Q2_K_XL · today

70B4 runs
Output222 tok/s
Prefill47.5 tok/s
TTFT6,360 ms
Depth0k
RTX A6000 48GB

vllm · AWQ · today

72B10 runs
Output178 tok/s
Prefill68.7 tok/s
TTFT4,960 ms
Depth0k
RTX A6000 48GB

vllm · AWQ · today

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

llama.cpp · Q2_K · today

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

llama.cpp · MXFP4 · today

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

llama.cpp · UD-Q2_K_XL · today

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

llama.cpp · UD-IQ4_XS · today

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

llama.cpp · Q4_K · today

12B MoE3 runs
Output26.8 tok/s
Prefill133 tok/s
TTFT2,301 ms
Depth0k
RTX A6000 48GB

llama.cpp · Q4_0 · today

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

llama.cpp · Q8_0 · today

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

llama.cpp · UD-Q2_K_XL · today

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

llama.cpp · Q2_K · today

Hardware breakdown

Hardware breakdown2

Distinct rigs used across approved submissions.

RTX A6000 48GB

179 runs · best 3,216 tok/s

Intel Arc Pro B65 32GB

21 runs · best 364 tok/s

Engines used

Engines used3

Runtime mix and top quantizations.

llama.cpp124Q4_0 · UD-Q4_K_XL · UD-IQ4_XS
vllm68AWQ · FP8 · MXFP4
strata8IQ3_S
All runs

All runs200

Full approved speed test history · page 6 of 8.

Qwen3.5-9B

9B · Qwen

288

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

243 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

288 tok/s

Prefill

1,258 tok/s

Total

609 tok/s

TTFT

243 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

4

Spec decoding

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16-mtp2 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq9-bf16-mtp2 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (batch 4, lmx --concurrency 4); 288.1 tok/s is the aggregate decode rate across all 4 streams, about 72.0 tok/s per stream, and the 243 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (305 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 4: 288.1, 297.7, 302.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:48 AM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

52.8

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ4_XS

TTFT

1,170 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

52.8 tok/s

Prefill

296 tok/s

Total

100 tok/s

TTFT

1,170 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ4_XS

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

1

Concurrency

1

Spec decoding

yes

Spec method

MTP

Spec model

/mnt/models/gguf/Qwen3.8-Flash-Next/mtp-Qwen3.8-Flash-Next-Q8_0.gguf

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-iq4xs-mtp2-np1 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next/UD-IQ4_XS/Qwen3.8-Flash-Next-UD-IQ4_XS-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 1 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none --spec-type draft-mtp -md /mnt/models/gguf/Qwen3.8-Flash-Next/mtp-Qwen3.8-Flash-Next-Q8_0.gguf --spec-draft-n-max 2

Extra flags

--d --name lsv-fn-iq4xs-mtp2-np1 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 93 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47476 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 1 slot, context 32768. MTP speculative decoding from mtp-Qwen3.8-Flash-Next-Q8_0.gguf, up to 2 draft tokens. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-IQ4_XS-00001-of-00003.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 7.9, 52.8, 55.1 tok/s; this is the median (tie to the earlier). Prompt and output samples were added from the lmx run file, engineTimingsRaw from exact llama-server /metrics deltas around the request.

Reactions

—

Submitted

Oct 27, 2026, 9:48 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

9.5

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ2_M

TTFT

4,499 ms

Context

2k · today

Show all run details

Model

deepseek-ai/DeepSeek-V4-Flash-0731

Display name

DeepSeek-V4-Flash-0731

Base model

—

Revision

fbbb5b93fb787c21338159b0af3318bb3f4d9768

Family

Deepseek

Parameters

304B

Active params

—

MoE

yes

Output

9.5 tok/s

Prefill

60.7 tok/s

Total

19.3 tok/s

TTFT

4,499 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

273

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ2_M

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

8

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/DeepSeek-V4-Flash-IQ2M/UD-IQ2_M/DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 9.5 tok/s is the aggregate decode rate across all 8 streams, about 1.2 tok/s per stream, and the 4499 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 90 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 10.2, 9.5, 8.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:48 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

1,237

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

1,096 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

1,237 tok/s

Prefill

278 tok/s

Total

2,643 tok/s

TTFT

1,096 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

32

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

32

Spec decoding

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16-mtp2 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq9-bf16-mtp2 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 32 requests sent at once (batch 32, lmx --concurrency 32); 1236.8 tok/s is the aggregate decode rate across all 32 streams, about 38.6 tok/s per stream, and the 1096 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 32: 1240.5, 1228.2, 1236.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 9:38 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

1,549

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

18,549 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

1,549 tok/s

Prefill

16.4 tok/s

Total

3,376 tok/s

TTFT

18,549 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

256

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

256

Spec decoding

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16-mtp2 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq9-bf16-mtp2 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 1548.6 tok/s is the aggregate decode rate across all 256 streams, about 6.0 tok/s per stream, and the 18549 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 1548.6, 1561.6, 1547.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 9:38 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

158

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

128 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

158 tok/s

Prefill

2,429 tok/s

Total

334 tok/s

TTFT

128 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

2

Spec decoding

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16-mtp2 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq9-bf16-mtp2 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (batch 2, lmx --concurrency 2); 158.1 tok/s is the aggregate decode rate across all 2 streams, about 79.0 tok/s per stream, and the 128 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (303 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 2: 160.4, 158.1, 147.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:38 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

814

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

726 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

814 tok/s

Prefill

423 tok/s

Total

1,736 tok/s

TTFT

726 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

16

Spec decoding

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16-mtp2 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq9-bf16-mtp2 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (batch 16, lmx --concurrency 16); 814.2 tok/s is the aggregate decode rate across all 16 streams, about 50.9 tok/s per stream, and the 726 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 16: 814.2, 825.5, 803.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:38 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

1,435

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

3,819 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

1,435 tok/s

Prefill

80.2 tok/s

Total

3,118 tok/s

TTFT

3,819 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

128

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

128

Spec decoding

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16-mtp2 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq9-bf16-mtp2 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 128 requests sent at once (batch 128, lmx --concurrency 128); 1434.6 tok/s is the aggregate decode rate across all 128 streams, about 11.2 tok/s per stream, and the 3819 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 128: 1434.6, 1429.7, 1436.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 9:38 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

293

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

364 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

293 tok/s

Prefill

838 tok/s

Total

633 tok/s

TTFT

364 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

8

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (batch 8, lmx --concurrency 8); 292.6 tok/s is the aggregate decode rate across all 8 streams, about 36.6 tok/s per stream, and the 364 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (305 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 8: 292.6, 293, 292.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 9:28 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

1,242

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

1,867 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

1,242 tok/s

Prefill

164 tok/s

Total

2,688 tok/s

TTFT

1,867 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

64

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

64

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 64 requests sent at once (batch 64, lmx --concurrency 64); 1241.9 tok/s is the aggregate decode rate across all 64 streams, about 19.4 tok/s per stream, and the 1867 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (305 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 64: 1241.9, 1241, 1242.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 9:28 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

152

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

231 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

152 tok/s

Prefill

1,322 tok/s

Total

329 tok/s

TTFT

231 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

4

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (batch 4, lmx --concurrency 4); 152 tok/s is the aggregate decode rate across all 4 streams, about 38.0 tok/s per stream, and the 231 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (305 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 4: 151.9, 152, 152.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:28 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

6.5

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ2_M

TTFT

5,300 ms

Context

2k · today

Show all run details

Model

deepseek-ai/DeepSeek-V4-Flash-0731

Display name

DeepSeek-V4-Flash-0731

Base model

—

Revision

fbbb5b93fb787c21338159b0af3318bb3f4d9768

Family

Deepseek

Parameters

304B

Active params

—

MoE

yes

Output

6.5 tok/s

Prefill

51.7 tok/s

Total

13.1 tok/s

TTFT

5,300 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

274

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ2_M

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

4

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/DeepSeek-V4-Flash-IQ2M/UD-IQ2_M/DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 6.5 tok/s is the aggregate decode rate across all 4 streams, about 1.6 tok/s per stream, and the 5300 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 90 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 10.1, 6.5, 6.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:28 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

5.9

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ2_M

TTFT

3,550 ms

Context

2k · today

Show all run details

Model

deepseek-ai/DeepSeek-V4-Flash-0731

Display name

DeepSeek-V4-Flash-0731

Base model

—

Revision

fbbb5b93fb787c21338159b0af3318bb3f4d9768

Family

Deepseek

Parameters

304B

Active params

—

MoE

yes

Output

5.9 tok/s

Prefill

76.3 tok/s

Total

11.6 tok/s

TTFT

3,550 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

271

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ2_M

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/DeepSeek-V4-Flash-IQ2M/UD-IQ2_M/DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 5.9 tok/s is the aggregate decode rate across all 2 streams, about 3.0 tok/s per stream, and the 3550 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 90 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 5.9, 6.3, 11.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:28 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

845

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

961 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

845 tok/s

Prefill

316 tok/s

Total

1,826 tok/s

TTFT

961 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

32

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

32

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 32 requests sent at once (batch 32, lmx --concurrency 32); 845 tok/s is the aggregate decode rate across all 32 streams, about 26.4 tok/s per stream, and the 961 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 32: 844.8, 846.4, 845 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 9:18 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

1,746

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

6,884 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

1,746 tok/s

Prefill

44.6 tok/s

Total

3,808 tok/s

TTFT

6,884 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

256

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

256

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 1745.8 tok/s is the aggregate decode rate across all 256 streams, about 6.8 tok/s per stream, and the 6884 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 1747, 1744.3, 1745.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 9:18 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

77.9

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

110 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

77.9 tok/s

Prefill

2,995 tok/s

Total

168 tok/s

TTFT

110 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (batch 2, lmx --concurrency 2); 77.9 tok/s is the aggregate decode rate across all 2 streams, about 39.0 tok/s per stream, and the 110 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (303 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 2: 77.9, 77.9, 78 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:18 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

26.1

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · MXFP4_MOE

TTFT

4,686 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

26.1 tok/s

Prefill

65.1 tok/s

Total

53.9 tok/s

TTFT

4,686 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

MXFP4_MOE

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

8

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-mxfp4-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-MXFP4/MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-mxfp4-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 26.1 tok/s is the aggregate decode rate across all 8 streams, about 3.3 tok/s per stream, and the 4686 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 74 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 27.4, 27, 26.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:17 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

17.9

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ2_M

TTFT

1,885 ms

Context

2k · today

Show all run details

Model

deepseek-ai/DeepSeek-V4-Flash-0731

Display name

DeepSeek-V4-Flash-0731

Base model

—

Revision

fbbb5b93fb787c21338159b0af3318bb3f4d9768

Family

Deepseek

Parameters

304B

Active params

—

MoE

yes

Output

17.9 tok/s

Prefill

146 tok/s

Total

33.0 tok/s

TTFT

1,885 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

275

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ2_M

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/DeepSeek-V4-Flash-IQ2M/UD-IQ2_M/DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 90 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 17.9, 17.5, 19.5 tok/s; this is the median (tie to the earlier). Prompt and output samples were added from the lmx run file, engineTimingsRaw from exact llama-server /metrics deltas around the request.

Reactions

—

Submitted

Oct 27, 2026, 9:17 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

523

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

668 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

523 tok/s

Prefill

456 tok/s

Total

1,127 tok/s

TTFT

668 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (batch 16, lmx --concurrency 16); 522.6 tok/s is the aggregate decode rate across all 16 streams, about 32.7 tok/s per stream, and the 668 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 16: 523.4, 523.2, 522.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:07 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

1,650

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

3,363 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

1,650 tok/s

Prefill

90.7 tok/s

Total

3,583 tok/s

TTFT

3,363 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

128

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

128

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 128 requests sent at once (batch 128, lmx --concurrency 128); 1650.3 tok/s is the aggregate decode rate across all 128 streams, about 12.9 tok/s per stream, and the 3363 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 128: 1650.3, 1647.4, 1651.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 9:07 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

14.7

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · MXFP4_MOE

TTFT

2,990 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

14.7 tok/s

Prefill

102 tok/s

Total

30.9 tok/s

TTFT

2,990 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

MXFP4_MOE

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

4

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-mxfp4-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-MXFP4/MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-mxfp4-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 14.7 tok/s is the aggregate decode rate across all 4 streams, about 3.7 tok/s per stream, and the 2990 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 74 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 14.8, 14.7, 14.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:07 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

7.8

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · MXFP4_MOE

TTFT

1,584 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

7.8 tok/s

Prefill

193 tok/s

Total

16.7 tok/s

TTFT

1,584 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

MXFP4_MOE

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-mxfp4-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-MXFP4/MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-mxfp4-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 7.8 tok/s is the aggregate decode rate across all 2 streams, about 3.9 tok/s per stream, and the 1584 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 74 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 8.5, 8.9, 7.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:07 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

37.8

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · MXFP4_MOE

TTFT

1,244 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

37.8 tok/s

Prefill

240 tok/s

Total

69.5 tok/s

TTFT

1,244 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

299

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

MXFP4_MOE

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-mxfp4-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-MXFP4/MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-mxfp4-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 74 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 37.5, 38.1, 37.8 tok/s; this is the median (tie to the earlier). Prompt and output samples were added from the lmx run file, engineTimingsRaw from exact llama-server /metrics deltas around the request.

Reactions

—

Submitted

Oct 27, 2026, 9:07 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

39.9

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

77.2 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

39.9 tok/s

Prefill

3,953 tok/s

Total

86.8 tok/s

TTFT

77.2 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Config vq9-bf16, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 40, 39.9, 39.9 tok/s; this is the median (declared rule, tie to the earlier). Prompt/output samples, engineTimingsRaw and MTP counts were added from the lmx run file and exact per-request vLLM /metrics deltas, because lmx does not send them.

Reactions

—

Submitted

Oct 27, 2026, 8:57 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

25.1

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q4_K_XL

TTFT

4,889 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

25.1 tok/s

Prefill

62.2 tok/s

Total

51.8 tok/s

TTFT

4,889 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q4_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

8

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-q4kxl-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-Q4/UD-Q4_K_XL/Qwen3.5-122B-A10B-UD-Q4_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-q4kxl-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 25.1 tok/s is the aggregate decode rate across all 8 streams, about 3.1 tok/s per stream, and the 4889 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 77 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47380 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-UD-Q4_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 33.6, 33.1, 25.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 8:57 AM

Last edited

—

ModelHardwareEnginetok/s outprefilltok/s totalTTFTDepthShare
Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF162881,258609243 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

288 tok/s

Prefill

1,258 tok/s

Total

609 tok/s

TTFT

243 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

4

Spec decoding

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16-mtp2 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq9-bf16-mtp2 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (batch 4, lmx --concurrency 4); 288.1 tok/s is the aggregate decode rate across all 4 streams, about 72.0 tok/s per stream, and the 243 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (305 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 4: 288.1, 297.7, 302.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

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Submitted

Oct 27, 2026, 9:48 AM

Last edited

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Qwen3.8-Flash-Next

180B · Qwen

RTX A6000 48GB
llama.cppUD-IQ4_XS52.82961001,170 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

52.8 tok/s

Prefill

296 tok/s

Total

100 tok/s

TTFT

1,170 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ4_XS

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

1

Concurrency

1

Spec decoding

yes

Spec method

MTP

Spec model

/mnt/models/gguf/Qwen3.8-Flash-Next/mtp-Qwen3.8-Flash-Next-Q8_0.gguf

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-iq4xs-mtp2-np1 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next/UD-IQ4_XS/Qwen3.8-Flash-Next-UD-IQ4_XS-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 1 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none --spec-type draft-mtp -md /mnt/models/gguf/Qwen3.8-Flash-Next/mtp-Qwen3.8-Flash-Next-Q8_0.gguf --spec-draft-n-max 2

Extra flags

--d --name lsv-fn-iq4xs-mtp2-np1 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 93 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47476 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 1 slot, context 32768. MTP speculative decoding from mtp-Qwen3.8-Flash-Next-Q8_0.gguf, up to 2 draft tokens. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-IQ4_XS-00001-of-00003.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 7.9, 52.8, 55.1 tok/s; this is the median (tie to the earlier). Prompt and output samples were added from the lmx run file, engineTimingsRaw from exact llama-server /metrics deltas around the request.

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Submitted

Oct 27, 2026, 9:48 AM

Last edited

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DeepSeek-V4-Flash-0731

304B MoE · Deepseek

RTX A6000 48GB
llama.cppUD-IQ2_M9.560.719.34,499 ms0k
Show all details for DeepSeek-V4-Flash-0731

Model

deepseek-ai/DeepSeek-V4-Flash-0731

Display name

DeepSeek-V4-Flash-0731

Base model

—

Revision

fbbb5b93fb787c21338159b0af3318bb3f4d9768

Family

Deepseek

Parameters

304B

Active params

—

MoE

yes

Output

9.5 tok/s

Prefill

60.7 tok/s

Total

19.3 tok/s

TTFT

4,499 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

273

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ2_M

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

8

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/DeepSeek-V4-Flash-IQ2M/UD-IQ2_M/DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 9.5 tok/s is the aggregate decode rate across all 8 streams, about 1.2 tok/s per stream, and the 4499 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 90 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 10.2, 9.5, 8.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 9:48 AM

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Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF161,2372782,6431,096 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

1,237 tok/s

Prefill

278 tok/s

Total

2,643 tok/s

TTFT

1,096 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

32

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

32

Spec decoding

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16-mtp2 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq9-bf16-mtp2 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 32 requests sent at once (batch 32, lmx --concurrency 32); 1236.8 tok/s is the aggregate decode rate across all 32 streams, about 38.6 tok/s per stream, and the 1096 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 32: 1240.5, 1228.2, 1236.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 9:38 AM

Last edited

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Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF161,54916.43,37618,549 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

1,549 tok/s

Prefill

16.4 tok/s

Total

3,376 tok/s

TTFT

18,549 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

256

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

256

Spec decoding

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16-mtp2 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq9-bf16-mtp2 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 1548.6 tok/s is the aggregate decode rate across all 256 streams, about 6.0 tok/s per stream, and the 18549 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 1548.6, 1561.6, 1547.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Oct 27, 2026, 9:38 AM

Last edited

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Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF161582,429334128 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

158 tok/s

Prefill

2,429 tok/s

Total

334 tok/s

TTFT

128 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

2

Spec decoding

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16-mtp2 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq9-bf16-mtp2 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (batch 2, lmx --concurrency 2); 158.1 tok/s is the aggregate decode rate across all 2 streams, about 79.0 tok/s per stream, and the 128 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (303 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 2: 160.4, 158.1, 147.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 9:38 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF168144231,736726 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

814 tok/s

Prefill

423 tok/s

Total

1,736 tok/s

TTFT

726 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

16

Spec decoding

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16-mtp2 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq9-bf16-mtp2 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (batch 16, lmx --concurrency 16); 814.2 tok/s is the aggregate decode rate across all 16 streams, about 50.9 tok/s per stream, and the 726 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 16: 814.2, 825.5, 803.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 9:38 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF161,43580.23,1183,819 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

1,435 tok/s

Prefill

80.2 tok/s

Total

3,118 tok/s

TTFT

3,819 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

128

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

128

Spec decoding

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16-mtp2 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq9-bf16-mtp2 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 128 requests sent at once (batch 128, lmx --concurrency 128); 1434.6 tok/s is the aggregate decode rate across all 128 streams, about 11.2 tok/s per stream, and the 3819 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 128: 1434.6, 1429.7, 1436.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 9:38 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF16293838633364 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

293 tok/s

Prefill

838 tok/s

Total

633 tok/s

TTFT

364 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

8

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (batch 8, lmx --concurrency 8); 292.6 tok/s is the aggregate decode rate across all 8 streams, about 36.6 tok/s per stream, and the 364 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (305 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 8: 292.6, 293, 292.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 9:28 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF161,2421642,6881,867 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

1,242 tok/s

Prefill

164 tok/s

Total

2,688 tok/s

TTFT

1,867 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

64

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

64

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 64 requests sent at once (batch 64, lmx --concurrency 64); 1241.9 tok/s is the aggregate decode rate across all 64 streams, about 19.4 tok/s per stream, and the 1867 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (305 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 64: 1241.9, 1241, 1242.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 9:28 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF161521,322329231 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

152 tok/s

Prefill

1,322 tok/s

Total

329 tok/s

TTFT

231 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

4

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (batch 4, lmx --concurrency 4); 152 tok/s is the aggregate decode rate across all 4 streams, about 38.0 tok/s per stream, and the 231 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (305 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 4: 151.9, 152, 152.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:28 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

RTX A6000 48GB
llama.cppUD-IQ2_M6.551.713.15,300 ms0k
Show all details for DeepSeek-V4-Flash-0731

Model

deepseek-ai/DeepSeek-V4-Flash-0731

Display name

DeepSeek-V4-Flash-0731

Base model

—

Revision

fbbb5b93fb787c21338159b0af3318bb3f4d9768

Family

Deepseek

Parameters

304B

Active params

—

MoE

yes

Output

6.5 tok/s

Prefill

51.7 tok/s

Total

13.1 tok/s

TTFT

5,300 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

274

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ2_M

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

4

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/DeepSeek-V4-Flash-IQ2M/UD-IQ2_M/DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 6.5 tok/s is the aggregate decode rate across all 4 streams, about 1.6 tok/s per stream, and the 5300 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 90 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 10.1, 6.5, 6.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

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Submitted

Oct 27, 2026, 9:28 AM

Last edited

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DeepSeek-V4-Flash-0731

304B MoE · Deepseek

RTX A6000 48GB
llama.cppUD-IQ2_M5.976.311.63,550 ms0k
Show all details for DeepSeek-V4-Flash-0731

Model

deepseek-ai/DeepSeek-V4-Flash-0731

Display name

DeepSeek-V4-Flash-0731

Base model

—

Revision

fbbb5b93fb787c21338159b0af3318bb3f4d9768

Family

Deepseek

Parameters

304B

Active params

—

MoE

yes

Output

5.9 tok/s

Prefill

76.3 tok/s

Total

11.6 tok/s

TTFT

3,550 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

271

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ2_M

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/DeepSeek-V4-Flash-IQ2M/UD-IQ2_M/DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 5.9 tok/s is the aggregate decode rate across all 2 streams, about 3.0 tok/s per stream, and the 3550 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 90 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 5.9, 6.3, 11.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 9:28 AM

Last edited

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Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF168453161,826961 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

845 tok/s

Prefill

316 tok/s

Total

1,826 tok/s

TTFT

961 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

32

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

32

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 32 requests sent at once (batch 32, lmx --concurrency 32); 845 tok/s is the aggregate decode rate across all 32 streams, about 26.4 tok/s per stream, and the 961 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 32: 844.8, 846.4, 845 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

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Submitted

Oct 27, 2026, 9:18 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF161,74644.63,8086,884 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

1,746 tok/s

Prefill

44.6 tok/s

Total

3,808 tok/s

TTFT

6,884 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

256

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

256

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 1745.8 tok/s is the aggregate decode rate across all 256 streams, about 6.8 tok/s per stream, and the 6884 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 1747, 1744.3, 1745.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 9:18 AM

Last edited

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Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF1677.92,995168110 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

77.9 tok/s

Prefill

2,995 tok/s

Total

168 tok/s

TTFT

110 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (batch 2, lmx --concurrency 2); 77.9 tok/s is the aggregate decode rate across all 2 streams, about 39.0 tok/s per stream, and the 110 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (303 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 2: 77.9, 77.9, 78 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 9:18 AM

Last edited

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Qwen3.5-122B-A10B

10B MoE · Qwen

RTX A6000 48GB
llama.cppMXFP4_MOE26.165.153.94,686 ms0k
Show all details for Qwen3.5-122B-A10B

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

26.1 tok/s

Prefill

65.1 tok/s

Total

53.9 tok/s

TTFT

4,686 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

MXFP4_MOE

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

8

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-mxfp4-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-MXFP4/MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-mxfp4-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 26.1 tok/s is the aggregate decode rate across all 8 streams, about 3.3 tok/s per stream, and the 4686 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 74 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 27.4, 27, 26.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 9:17 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

RTX A6000 48GB
llama.cppUD-IQ2_M17.914633.01,885 ms0k
Show all details for DeepSeek-V4-Flash-0731

Model

deepseek-ai/DeepSeek-V4-Flash-0731

Display name

DeepSeek-V4-Flash-0731

Base model

—

Revision

fbbb5b93fb787c21338159b0af3318bb3f4d9768

Family

Deepseek

Parameters

304B

Active params

—

MoE

yes

Output

17.9 tok/s

Prefill

146 tok/s

Total

33.0 tok/s

TTFT

1,885 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

275

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ2_M

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/DeepSeek-V4-Flash-IQ2M/UD-IQ2_M/DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-iq2m-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 90 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 17.9, 17.5, 19.5 tok/s; this is the median (tie to the earlier). Prompt and output samples were added from the lmx run file, engineTimingsRaw from exact llama-server /metrics deltas around the request.

Reactions

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Submitted

Oct 27, 2026, 9:17 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF165234561,127668 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

523 tok/s

Prefill

456 tok/s

Total

1,127 tok/s

TTFT

668 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (batch 16, lmx --concurrency 16); 522.6 tok/s is the aggregate decode rate across all 16 streams, about 32.7 tok/s per stream, and the 668 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 16: 523.4, 523.2, 522.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 9:07 AM

Last edited

—

Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF161,65090.73,5833,363 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

1,650 tok/s

Prefill

90.7 tok/s

Total

3,583 tok/s

TTFT

3,363 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

128

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

128

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 128 requests sent at once (batch 128, lmx --concurrency 128); 1650.3 tok/s is the aggregate decode rate across all 128 streams, about 12.9 tok/s per stream, and the 3363 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 128: 1650.3, 1647.4, 1651.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq9-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 9:07 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

RTX A6000 48GB
llama.cppMXFP4_MOE14.710230.92,990 ms0k
Show all details for Qwen3.5-122B-A10B

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

14.7 tok/s

Prefill

102 tok/s

Total

30.9 tok/s

TTFT

2,990 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

MXFP4_MOE

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

4

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-mxfp4-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-MXFP4/MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-mxfp4-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 14.7 tok/s is the aggregate decode rate across all 4 streams, about 3.7 tok/s per stream, and the 2990 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 74 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 14.8, 14.7, 14.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:07 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

RTX A6000 48GB
llama.cppMXFP4_MOE7.819316.71,584 ms0k
Show all details for Qwen3.5-122B-A10B

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

7.8 tok/s

Prefill

193 tok/s

Total

16.7 tok/s

TTFT

1,584 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

MXFP4_MOE

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-mxfp4-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-MXFP4/MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-mxfp4-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 7.8 tok/s is the aggregate decode rate across all 2 streams, about 3.9 tok/s per stream, and the 1584 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 74 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 8.5, 8.9, 7.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 9:07 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

RTX A6000 48GB
llama.cppMXFP4_MOE37.824069.51,244 ms0k
Show all details for Qwen3.5-122B-A10B

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

37.8 tok/s

Prefill

240 tok/s

Total

69.5 tok/s

TTFT

1,244 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

299

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

MXFP4_MOE

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-mxfp4-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-MXFP4/MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-mxfp4-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 74 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 37.5, 38.1, 37.8 tok/s; this is the median (tie to the earlier). Prompt and output samples were added from the lmx run file, engineTimingsRaw from exact llama-server /metrics deltas around the request.

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Submitted

Oct 27, 2026, 9:07 AM

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Qwen3.5-9B

9B · Qwen

RTX A6000 48GB
vllmBF1639.93,95386.877.2 ms0k
Show all details for Qwen3.5-9B

Model

Qwen/Qwen3.5-9B

Display name

Qwen3.5-9B

Base model

Qwen3.5-9B-Base

Revision

c202236235762e1c871ad0ccb60c8ee5ba337b9a

Family

Qwen

Parameters

9B

Active params

—

MoE

no

Output

39.9 tok/s

Prefill

3,953 tok/s

Total

86.8 tok/s

TTFT

77.2 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq9-bf16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3.5-9B --served-model-name Qwen3.5-9B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq9-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Config vq9-bf16, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 40, 39.9, 39.9 tok/s; this is the median (declared rule, tie to the earlier). Prompt/output samples, engineTimingsRaw and MTP counts were added from the lmx run file and exact per-request vLLM /metrics deltas, because lmx does not send them.

Reactions

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Submitted

Oct 27, 2026, 8:57 AM

Last edited

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Qwen3.5-122B-A10B

10B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q4_K_XL25.162.251.84,889 ms0k
Show all details for Qwen3.5-122B-A10B

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

25.1 tok/s

Prefill

62.2 tok/s

Total

51.8 tok/s

TTFT

4,889 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q4_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

8

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-q4kxl-np8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-Q4/UD-Q4_K_XL/Qwen3.5-122B-A10B-UD-Q4_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-q4kxl-np8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 25.1 tok/s is the aggregate decode rate across all 8 streams, about 3.1 tok/s per stream, and the 4889 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 77 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47380 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-UD-Q4_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 33.6, 33.1, 25.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

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Submitted

Oct 27, 2026, 8:57 AM

Last edited

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