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imthekaiser

imthekaiser

Verified

@imthekaiser · Member since 2026

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Verified 3w ago Mainly llama.cpp 200 approved runs

Submissions

2,318

200 approved public runs

Models

16

unique models

Avg output

24.0tok/s

Best 103 tok/s

Avg prefill

72.7tok/s

Best 281 tok/s

Avg total

43.4tok/s

Best 215 tok/s

Avg TTFT

10,220ms

Best 1,110 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 bests16

Fastest approved result for each model.

180B40 runs
Output103 tok/s
Prefill177 tok/s
TTFT1,948 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · 1w ago

80B MoE13 runs
Output68.8 tok/s
Prefill18.4 tok/s
TTFT16,014 ms
Depth0k
Intel Arc Pro B65 32GB

llama.cpp · UD-Q4_K_XL · 1w ago

120B MoE4 runs
Output54.6 tok/s
Prefill23.0 tok/s
TTFT14,546 ms
Depth0k
Intel Arc Pro B65 32GB

llama.cpp · MXFP4 · 1w ago

17B MoE14 runs
Output40.6 tok/s
Prefill18.2 tok/s
TTFT15,203 ms
Depth0k
Intel Arc Pro B65 32GB

llama.cpp · Q2_K · 1w ago

199B13 runs
Output36.0 tok/s
Prefill65.6 tok/s
TTFT4,297 ms
Depth0k
RTX A6000 48GB

llama.cpp · Q4_K · 1w ago

108B MoE10 runs
Output26.5 tok/s
Prefill10.4 tok/s
TTFT26,805 ms
Depth0k
Intel Arc Pro B65 32GB

llama.cpp · Q4_K_M · 1w ago

12B MoE2 runs
Output25.7 tok/s
Prefill40.2 tok/s
TTFT7,676 ms
Depth0k
RTX A6000 48GB

llama.cpp · Q4_0 · 1w ago

22B MoE6 runs
Output24.8 tok/s
Prefill56.0 tok/s
TTFT5,284 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · 1w ago

311B MoE12 runs
Output18.2 tok/s
Prefill87.5 tok/s
TTFT3,384 ms
Depth0k
RTX A6000 48GB

llama.cpp · Q2_K · 1w ago

304B MoE51 runs
Output16.4 tok/s
Prefill57.8 tok/s
TTFT4,726 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-IQ2_M · 1w ago

17B MoE5 runs
Output15.8 tok/s
Prefill101 tok/s
TTFT2,728 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-IQ1_S · 1w ago

17B MoE5 runs
Output10.5 tok/s
Prefill109 tok/s
TTFT2,774 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-IQ2_XXS · 1w ago

321B16 runs
Output10.0 tok/s
Prefill8.3 tok/s
TTFT34,358 ms
Depth0k
Intel Arc Pro B65 32GB

llama.cpp · UD-Q2_K_XL · 1w ago

10B MoE2 runs
Output7.7 tok/s
Prefill62.9 tok/s
TTFT4,815 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q4_K_XL · 1w ago

427B MoE4 runs
Output7.0 tok/s
Prefill53.0 tok/s
TTFT8,325 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-IQ1_M · 1w ago

358B MoE3 runs
Output5.1 tok/s
Prefill22.9 tok/s
TTFT12,149 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-IQ2_M · 1w ago

Hardware breakdown

Hardware breakdown2

Distinct rigs used across approved submissions.

Intel Arc Pro B65 32GB

149 runs · best 68.8 tok/s

RTX A6000 48GB

51 runs · best 103 tok/s

Engines used

Engines used2

Runtime mix and top quantizations.

llama.cpp164UD-IQ3_XXS · UD-IQ2_M · Q4_K
strata36UD-IQ4_XS · IQ3_S
All runs

All runs200

Full approved speed test history · page 5 of 8.

Qwen3.5-397B-A17B

17B MoE · Qwen

4.6

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ2_XXS

TTFT

6,651 ms

Context

2k · 1w ago

Show all run details

Model

Qwen/Qwen3.5-397B-A17B

Display name

Qwen3.5-397B-A17B

Base model

—

Revision

da33c16fa4440f831149fcf53b98a22bc07785e5

Family

Qwen

Parameters

397B

Active params

17B

MoE

yes

Output

4.6 tok/s

Prefill

45.7 tok/s

Total

9.9 tok/s

TTFT

6,651 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-IQ2_XXS

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q397-iq2xxs-v16 --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/Qwen3.5-397B-IQ2XXS/UD-IQ2_XXS/Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q397-iq2xxs-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 4.6 tok/s is the aggregate decode rate across all 8 streams, about 0.6 tok/s per stream, and the 6651 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 114 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; 47546 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-397B-A17B-GGUF @ da33c16fa4440f831149fcf53b98a22bc07785e5, file Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 4.8, 4.7, 4.6 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 30, 2026, 3:24 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

7.3

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

33,689 ms

Context

2k · 1w ago

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

7.3 tok/s

Prefill

8.2 tok/s

Total

13.5 tok/s

TTFT

33,689 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

275

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 7.3 tok/s is the aggregate decode rate across all 8 streams, about 0.9 tok/s per stream, and the 33689 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 7.3, 6.3, 8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 30, 2026, 3:12 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

6.8

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

15,955 ms

Context

2k · 1w ago

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

6.8 tok/s

Prefill

17.0 tok/s

Total

13.1 tok/s

TTFT

15,955 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

272

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

6

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

6

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 6 requests sent at once (lmx --concurrency 6); 6.8 tok/s is the aggregate decode rate across all 6 streams, about 1.1 tok/s per stream, and the 15955 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 7.3, 6.8, 6.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 30, 2026, 2:52 AM

Last edited

—

Qwen3.5-397B-A17B

17B MoE · Qwen

2.6

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ2_XXS

TTFT

8,894 ms

Context

2k · 1w ago

Show all run details

Model

Qwen/Qwen3.5-397B-A17B

Display name

Qwen3.5-397B-A17B

Base model

—

Revision

da33c16fa4440f831149fcf53b98a22bc07785e5

Family

Qwen

Parameters

397B

Active params

17B

MoE

yes

Output

2.6 tok/s

Prefill

34.2 tok/s

Total

5.6 tok/s

TTFT

8,894 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

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_XXS

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q397-iq2xxs-v16 --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/Qwen3.5-397B-IQ2XXS/UD-IQ2_XXS/Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q397-iq2xxs-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 2.6 tok/s is the aggregate decode rate across all 4 streams, about 0.7 tok/s per stream, and the 8894 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 114 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; 47546 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-397B-A17B-GGUF @ da33c16fa4440f831149fcf53b98a22bc07785e5, file Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.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, 4.5, 2.6 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 30, 2026, 2:24 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

8.1

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

7,962 ms

Context

2k · 1w ago

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

8.1 tok/s

Prefill

34.2 tok/s

Total

15.4 tok/s

TTFT

7,962 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

272

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

3

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

3

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 3 requests sent at once (lmx --concurrency 3); 8.1 tok/s is the aggregate decode rate across all 3 streams, about 2.7 tok/s per stream, and the 7962 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 5.2, 8.1, 8.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 30, 2026, 2:22 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

6.6

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

7,416 ms

Context

2k · 1w ago

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

6.6 tok/s

Prefill

37.0 tok/s

Total

12.5 tok/s

TTFT

7,416 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

274

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 6.6 tok/s is the aggregate decode rate across all 2 streams, about 3.3 tok/s per stream, and the 7416 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 7, 6.6, 7.7 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 30, 2026, 2:12 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

7.0

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

4,958 ms

Context

2k · 1w ago

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

7.0 tok/s

Prefill

55.1 tok/s

Total

12.9 tok/s

TTFT

4,958 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

273

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 7, 6.9, 7.1 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 30, 2026, 2:12 AM

Last edited

—

Qwen3.5-397B-A17B

17B MoE · Qwen

5.5

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ2_XXS

TTFT

4,291 ms

Context

2k · 1w ago

Show all run details

Model

Qwen/Qwen3.5-397B-A17B

Display name

Qwen3.5-397B-A17B

Base model

—

Revision

da33c16fa4440f831149fcf53b98a22bc07785e5

Family

Qwen

Parameters

397B

Active params

17B

MoE

yes

Output

5.5 tok/s

Prefill

71.1 tok/s

Total

11.7 tok/s

TTFT

4,291 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

3

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ2_XXS

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

3

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q397-iq2xxs-v16 --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/Qwen3.5-397B-IQ2XXS/UD-IQ2_XXS/Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q397-iq2xxs-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 3 requests sent at once (lmx --concurrency 3); 5.5 tok/s is the aggregate decode rate across all 3 streams, about 1.8 tok/s per stream, and the 4291 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 114 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; 47546 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-397B-A17B-GGUF @ da33c16fa4440f831149fcf53b98a22bc07785e5, file Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.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, 5.5, 2.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 30, 2026, 2:04 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

9.1

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

9,610 ms

Context

2k · 1w ago

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

9.1 tok/s

Prefill

28.4 tok/s

Total

17.4 tok/s

TTFT

9,610 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

273

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

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

4

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-np4-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 16384 -np 4 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-np4-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 9.1 tok/s is the aggregate decode rate across all 4 streams, about 2.3 tok/s per stream, and the 9610 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 4 server slots of 4096 tokens each, context 16384. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 9.1, 7.9, 6.7 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 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 30, 2026, 2:02 AM

Last edited

—

Qwen3.5-397B-A17B

17B MoE · Qwen

8.8

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ2_XXS

TTFT

3,691 ms

Context

2k · 1w ago

Show all run details

Model

Qwen/Qwen3.5-397B-A17B

Display name

Qwen3.5-397B-A17B

Base model

—

Revision

da33c16fa4440f831149fcf53b98a22bc07785e5

Family

Qwen

Parameters

397B

Active params

17B

MoE

yes

Output

8.8 tok/s

Prefill

82.9 tok/s

Total

18.1 tok/s

TTFT

3,691 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

306

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_XXS

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q397-iq2xxs-v16 --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/Qwen3.5-397B-IQ2XXS/UD-IQ2_XXS/Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q397-iq2xxs-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 8.8 tok/s is the aggregate decode rate across all 2 streams, about 4.4 tok/s per stream, and the 3691 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 114 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; 47546 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-397B-A17B-GGUF @ da33c16fa4440f831149fcf53b98a22bc07785e5, file Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.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: 9.9, 8.8, 11.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 30, 2026, 1:54 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

6.5

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

9,998 ms

Context

2k · 1w ago

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

27.2 tok/s

Total

12.3 tok/s

TTFT

9,998 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

272

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

3

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

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

4

Concurrency

3

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-np4-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 16384 -np 4 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-np4-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 3 requests sent at once (lmx --concurrency 3); 6.5 tok/s is the aggregate decode rate across all 3 streams, about 2.2 tok/s per stream, and the 9998 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 4 server slots of 4096 tokens each, context 16384. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 5.4, 8.3, 6.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 30, 2026, 1:52 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

9.2

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

5,597 ms

Context

2k · 1w ago

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

9.2 tok/s

Prefill

49.3 tok/s

Total

17.3 tok/s

TTFT

5,597 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

276

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

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

4

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-np4-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 16384 -np 4 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-np4-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 9.2 tok/s is the aggregate decode rate across all 2 streams, about 4.6 tok/s per stream, and the 5597 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 4 server slots of 4096 tokens each, context 16384. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 9.2, 9.3, 9.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 30, 2026, 1:52 AM

Last edited

—

Qwen3.5-397B-A17B

17B MoE · Qwen

10.5

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ2_XXS

TTFT

2,774 ms

Context

2k · 1w ago

Show all run details

Model

Qwen/Qwen3.5-397B-A17B

Display name

Qwen3.5-397B-A17B

Base model

—

Revision

da33c16fa4440f831149fcf53b98a22bc07785e5

Family

Qwen

Parameters

397B

Active params

17B

MoE

yes

Output

10.5 tok/s

Prefill

109 tok/s

Total

20.6 tok/s

TTFT

2,774 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

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_XXS

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q397-iq2xxs-v16 --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/Qwen3.5-397B-IQ2XXS/UD-IQ2_XXS/Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q397-iq2xxs-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 114 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; 47546 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-397B-A17B-GGUF @ da33c16fa4440f831149fcf53b98a22bc07785e5, file Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 10.2, 10.5, 10.5 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 30, 2026, 1:44 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

7.0

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

5,210 ms

Context

2k · 1w ago

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

7.0 tok/s

Prefill

52.6 tok/s

Total

12.7 tok/s

TTFT

5,210 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

274

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

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

4

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-np4-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 16384 -np 4 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-np4-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 4 server slots of 4096 tokens each, context 16384. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 7.1, 7, 6.9 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 30, 2026, 1:42 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

12.3

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

46,128 ms

Context

2k · 1w ago

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

12.3 tok/s

Prefill

5.9 tok/s

Total

22.3 tok/s

TTFT

46,128 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

273

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 12.3 tok/s is the aggregate decode rate across all 16 streams, about 0.8 tok/s per stream, and the 46128 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 12.7, 12.2, 12.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 30, 2026, 1:42 AM

Last edited

—

MiniMax-M3

427B MoE · Minimax

7.0

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ1_M

TTFT

8,325 ms

Context

2k · 1w ago

Show all run details

Model

MiniMaxAI/MiniMax-M3

Display name

MiniMax-M3

Base model

—

Revision

abdce34b5ab2e7dd9260130415c01a1f25561210

Family

Minimax

Parameters

427B

Active params

—

MoE

yes

Output

7.0 tok/s

Prefill

53.0 tok/s

Total

18.3 tok/s

TTFT

8,325 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

441

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

6

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ1_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

6

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-mmm3-iq1m-v16 --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/MiniMax-M3-IQ1M/UD-IQ1_M/MiniMax-M3-UD-IQ1_M-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mmm3-iq1m-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 6 requests sent at once (lmx --concurrency 6); 7 tok/s is the aggregate decode rate across all 6 streams, about 1.2 tok/s per stream, and the 8324 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 129 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; 46918 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/MiniMax-M3-GGUF @ abdce34b5ab2e7dd9260130415c01a1f25561210, file MiniMax-M3-UD-IQ1_M-00001-of-00004.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: 7, 7, 4.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 30, 2026, 1:24 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

8.6

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

54,654 ms

Context

2k · 1w ago

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

8.6 tok/s

Prefill

5.0 tok/s

Total

15.4 tok/s

TTFT

54,654 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

273

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 8.6 tok/s is the aggregate decode rate across all 12 streams, about 0.7 tok/s per stream, and the 54654 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 8.5, 9.2, 8.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 30, 2026, 1:12 AM

Last edited

—

MiniMax-M3

427B MoE · Minimax

5.0

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ1_M

TTFT

5,595 ms

Context

2k · 1w ago

Show all run details

Model

MiniMaxAI/MiniMax-M3

Display name

MiniMax-M3

Base model

—

Revision

abdce34b5ab2e7dd9260130415c01a1f25561210

Family

Minimax

Parameters

427B

Active params

—

MoE

yes

Output

5.0 tok/s

Prefill

78.6 tok/s

Total

13.2 tok/s

TTFT

5,595 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

440

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

3

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ1_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

3

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-mmm3-iq1m-v16 --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/MiniMax-M3-IQ1M/UD-IQ1_M/MiniMax-M3-UD-IQ1_M-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mmm3-iq1m-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 3 requests sent at once (lmx --concurrency 3); 5 tok/s is the aggregate decode rate across all 3 streams, about 1.7 tok/s per stream, and the 5595 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 129 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; 46918 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/MiniMax-M3-GGUF @ abdce34b5ab2e7dd9260130415c01a1f25561210, file MiniMax-M3-UD-IQ1_M-00001-of-00004.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 4.9, 6.4, 5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 30, 2026, 12:53 AM

Last edited

—

MiniMax-M3

427B MoE · Minimax

5.0

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ1_M

TTFT

4,120 ms

Context

2k · 1w ago

Show all run details

Model

MiniMaxAI/MiniMax-M3

Display name

MiniMax-M3

Base model

—

Revision

abdce34b5ab2e7dd9260130415c01a1f25561210

Family

Minimax

Parameters

427B

Active params

—

MoE

yes

Output

5.0 tok/s

Prefill

107 tok/s

Total

13.1 tok/s

TTFT

4,120 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

441

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-IQ1_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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-mmm3-iq1m-v16 --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/MiniMax-M3-IQ1M/UD-IQ1_M/MiniMax-M3-UD-IQ1_M-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mmm3-iq1m-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 5 tok/s is the aggregate decode rate across all 2 streams, about 2.5 tok/s per stream, and the 4120 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 129 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; 46918 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/MiniMax-M3-GGUF @ abdce34b5ab2e7dd9260130415c01a1f25561210, file MiniMax-M3-UD-IQ1_M-00001-of-00004.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, 5.2, 2.8 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 30, 2026, 12:43 AM

Last edited

—

MiniMax-M3

427B MoE · Minimax

6.6

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ1_M

TTFT

3,461 ms

Context

2k · 1w ago

Show all run details

Model

MiniMaxAI/MiniMax-M3

Display name

MiniMax-M3

Base model

—

Revision

abdce34b5ab2e7dd9260130415c01a1f25561210

Family

Minimax

Parameters

427B

Active params

—

MoE

yes

Output

6.6 tok/s

Prefill

127 tok/s

Total

16.6 tok/s

TTFT

3,461 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

441

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-IQ1_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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-mmm3-iq1m-v16 --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/MiniMax-M3-IQ1M/UD-IQ1_M/MiniMax-M3-UD-IQ1_M-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mmm3-iq1m-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 129 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; 46918 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/MiniMax-M3-GGUF @ abdce34b5ab2e7dd9260130415c01a1f25561210, file MiniMax-M3-UD-IQ1_M-00001-of-00004.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 6.6, 6.6, 6.5 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 30, 2026, 12:43 AM

Last edited

—

3.9

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q4_K

TTFT

8,125 ms

Context

2k · 1w ago

Show all run details

Model

stepfun-ai/Step-3.5-Flash

Display name

Step-3.5-Flash

Base model

Step-3.5-Flash

Revision

c439ad64d667632f3e1507757cbe225b6a46e6b8

Family

—

Parameters

199B

Active params

—

MoE

no

Output

3.9 tok/s

Prefill

34.9 tok/s

Total

8.0 tok/s

TTFT

8,125 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

283

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

Q4_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-step35f-q4k-v16-kv8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Step-3.5-Flash/Step-3.5-Flash-Q4_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-step35f-q4k-v16-kv8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 3.9 tok/s is the aggregate decode rate across all 4 streams, about 1.0 tok/s per stream, and the 8125 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 118 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47406 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768, KV cache q8_0 keys / q8_0 values (-ctk/-ctv). GGUF ggml-org/Step-3.5-Flash-GGUF @ c439ad64d667632f3e1507757cbe225b6a46e6b8, file Step-3.5-Flash-Q4_K.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 9.5, 4.8, 3.9 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 30, 2026, 12:33 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

6.6

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

15,719 ms

Context

2k · 1w ago

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

6.6 tok/s

Prefill

17.3 tok/s

Total

12.8 tok/s

TTFT

15,719 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

272

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

6

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

6

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 6 requests sent at once (lmx --concurrency 6); 6.6 tok/s is the aggregate decode rate across all 6 streams, about 1.1 tok/s per stream, and the 15719 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 9.6, 7.2, 6.6 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 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 30, 2026, 12:32 AM

Last edited

—

20.2

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q4_K

TTFT

2,753 ms

Context

2k · 1w ago

Show all run details

Model

stepfun-ai/Step-3.5-Flash

Display name

Step-3.5-Flash

Base model

Step-3.5-Flash

Revision

c439ad64d667632f3e1507757cbe225b6a46e6b8

Family

—

Parameters

199B

Active params

—

MoE

no

Output

20.2 tok/s

Prefill

102 tok/s

Total

34.9 tok/s

TTFT

2,753 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

282

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

Q4_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-step35f-q4k-v16-kv8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Step-3.5-Flash/Step-3.5-Flash-Q4_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-step35f-q4k-v16-kv8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 118 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47406 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768, KV cache q8_0 keys / q8_0 values (-ctk/-ctv). GGUF ggml-org/Step-3.5-Flash-GGUF @ c439ad64d667632f3e1507757cbe225b6a46e6b8, file Step-3.5-Flash-Q4_K.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 21.2, 20, 20.2 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 30, 2026, 12:23 AM

Last edited

—

7.3

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

8,434 ms

Context

2k · 1w ago

Show all run details

Model

zai-org/GLM-5.3-Flash

Display name

GLM-5.3-Flash

Base model

—

Revision

a38483c8cd5df544f53d70fb281afe97369d5ab6

Family

—

Parameters

321B

Active params

—

MoE

no

Output

7.3 tok/s

Prefill

33.8 tok/s

Total

14.5 tok/s

TTFT

8,434 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

285

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-Q2_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

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-glm53f-q2kxl-v16-kv8 --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/GLM-5.3-Flash/UD-Q2_K_XL/GLM-5.3-Flash-UD-Q2_K_XL-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-glm53f-q2kxl-v16-kv8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 7.3 tok/s is the aggregate decode rate across all 4 streams, about 1.8 tok/s per stream, and the 8434 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 108 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; 47800 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768, KV cache q8_0 keys / q8_0 values (-ctk/-ctv). GGUF unsloth/GLM-5.3-Flash-GGUF @ a38483c8cd5df544f53d70fb281afe97369d5ab6, file GLM-5.3-Flash-UD-Q2_K_XL-00001-of-00004.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: 6.7, 8.6, 7.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 30, 2026, 12:13 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

6.0

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

13,113 ms

Context

2k · 1w ago

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

6.0 tok/s

Prefill

20.7 tok/s

Total

11.4 tok/s

TTFT

13,113 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

271

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 6 tok/s is the aggregate decode rate across all 4 streams, about 1.5 tok/s per stream, and the 13113 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 8.3, 6, 8.5 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 30, 2026, 12:11 AM

Last edited

—

ModelHardwareEnginetok/s outprefilltok/s totalTTFTDepthShare
Qwen3.5-397B-A17B

17B MoE · Qwen

RTX A6000 48GB
llama.cppUD-IQ2_XXS4.645.79.96,651 ms0k
Show all details for Qwen3.5-397B-A17B

Model

Qwen/Qwen3.5-397B-A17B

Display name

Qwen3.5-397B-A17B

Base model

—

Revision

da33c16fa4440f831149fcf53b98a22bc07785e5

Family

Qwen

Parameters

397B

Active params

17B

MoE

yes

Output

4.6 tok/s

Prefill

45.7 tok/s

Total

9.9 tok/s

TTFT

6,651 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-IQ2_XXS

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q397-iq2xxs-v16 --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/Qwen3.5-397B-IQ2XXS/UD-IQ2_XXS/Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q397-iq2xxs-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 4.6 tok/s is the aggregate decode rate across all 8 streams, about 0.6 tok/s per stream, and the 6651 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 114 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; 47546 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-397B-A17B-GGUF @ da33c16fa4440f831149fcf53b98a22bc07785e5, file Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 4.8, 4.7, 4.6 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 30, 2026, 3:24 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

Intel Arc Pro B65 32GB
llama.cppUD-Q2_K_XL7.38.213.533,689 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

7.3 tok/s

Prefill

8.2 tok/s

Total

13.5 tok/s

TTFT

33,689 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

275

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 7.3 tok/s is the aggregate decode rate across all 8 streams, about 0.9 tok/s per stream, and the 33689 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 7.3, 6.3, 8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 30, 2026, 3:12 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

Intel Arc Pro B65 32GB
llama.cppUD-Q2_K_XL6.817.013.115,955 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

6.8 tok/s

Prefill

17.0 tok/s

Total

13.1 tok/s

TTFT

15,955 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

272

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

6

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

6

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 6 requests sent at once (lmx --concurrency 6); 6.8 tok/s is the aggregate decode rate across all 6 streams, about 1.1 tok/s per stream, and the 15955 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 7.3, 6.8, 6.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 30, 2026, 2:52 AM

Last edited

—

Qwen3.5-397B-A17B

17B MoE · Qwen

RTX A6000 48GB
llama.cppUD-IQ2_XXS2.634.25.68,894 ms0k
Show all details for Qwen3.5-397B-A17B

Model

Qwen/Qwen3.5-397B-A17B

Display name

Qwen3.5-397B-A17B

Base model

—

Revision

da33c16fa4440f831149fcf53b98a22bc07785e5

Family

Qwen

Parameters

397B

Active params

17B

MoE

yes

Output

2.6 tok/s

Prefill

34.2 tok/s

Total

5.6 tok/s

TTFT

8,894 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

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_XXS

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q397-iq2xxs-v16 --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/Qwen3.5-397B-IQ2XXS/UD-IQ2_XXS/Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q397-iq2xxs-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 2.6 tok/s is the aggregate decode rate across all 4 streams, about 0.7 tok/s per stream, and the 8894 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 114 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; 47546 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-397B-A17B-GGUF @ da33c16fa4440f831149fcf53b98a22bc07785e5, file Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.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, 4.5, 2.6 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 30, 2026, 2:24 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

Intel Arc Pro B65 32GB
llama.cppUD-Q2_K_XL8.134.215.47,962 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

8.1 tok/s

Prefill

34.2 tok/s

Total

15.4 tok/s

TTFT

7,962 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

272

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

3

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

3

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 3 requests sent at once (lmx --concurrency 3); 8.1 tok/s is the aggregate decode rate across all 3 streams, about 2.7 tok/s per stream, and the 7962 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 5.2, 8.1, 8.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 30, 2026, 2:22 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

Intel Arc Pro B65 32GB
llama.cppUD-Q2_K_XL6.637.012.57,416 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

6.6 tok/s

Prefill

37.0 tok/s

Total

12.5 tok/s

TTFT

7,416 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

274

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 6.6 tok/s is the aggregate decode rate across all 2 streams, about 3.3 tok/s per stream, and the 7416 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 7, 6.6, 7.7 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 30, 2026, 2:12 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

Intel Arc Pro B65 32GB
llama.cppUD-Q2_K_XL7.055.112.94,958 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

7.0 tok/s

Prefill

55.1 tok/s

Total

12.9 tok/s

TTFT

4,958 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

273

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-dsv4f-q2kxl-kv8-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 7, 6.9, 7.1 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 30, 2026, 2:12 AM

Last edited

—

Qwen3.5-397B-A17B

17B MoE · Qwen

RTX A6000 48GB
llama.cppUD-IQ2_XXS5.571.111.74,291 ms0k
Show all details for Qwen3.5-397B-A17B

Model

Qwen/Qwen3.5-397B-A17B

Display name

Qwen3.5-397B-A17B

Base model

—

Revision

da33c16fa4440f831149fcf53b98a22bc07785e5

Family

Qwen

Parameters

397B

Active params

17B

MoE

yes

Output

5.5 tok/s

Prefill

71.1 tok/s

Total

11.7 tok/s

TTFT

4,291 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

3

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ2_XXS

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

3

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q397-iq2xxs-v16 --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/Qwen3.5-397B-IQ2XXS/UD-IQ2_XXS/Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q397-iq2xxs-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 3 requests sent at once (lmx --concurrency 3); 5.5 tok/s is the aggregate decode rate across all 3 streams, about 1.8 tok/s per stream, and the 4291 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 114 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; 47546 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-397B-A17B-GGUF @ da33c16fa4440f831149fcf53b98a22bc07785e5, file Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.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, 5.5, 2.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 30, 2026, 2:04 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

Intel Arc Pro B65 32GB
llama.cppUD-Q2_K_XL9.128.417.49,610 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

9.1 tok/s

Prefill

28.4 tok/s

Total

17.4 tok/s

TTFT

9,610 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

273

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

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

4

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-np4-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 16384 -np 4 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-np4-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 9.1 tok/s is the aggregate decode rate across all 4 streams, about 2.3 tok/s per stream, and the 9610 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 4 server slots of 4096 tokens each, context 16384. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 9.1, 7.9, 6.7 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 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 30, 2026, 2:02 AM

Last edited

—

Qwen3.5-397B-A17B

17B MoE · Qwen

RTX A6000 48GB
llama.cppUD-IQ2_XXS8.882.918.13,691 ms0k
Show all details for Qwen3.5-397B-A17B

Model

Qwen/Qwen3.5-397B-A17B

Display name

Qwen3.5-397B-A17B

Base model

—

Revision

da33c16fa4440f831149fcf53b98a22bc07785e5

Family

Qwen

Parameters

397B

Active params

17B

MoE

yes

Output

8.8 tok/s

Prefill

82.9 tok/s

Total

18.1 tok/s

TTFT

3,691 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

306

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_XXS

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q397-iq2xxs-v16 --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/Qwen3.5-397B-IQ2XXS/UD-IQ2_XXS/Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q397-iq2xxs-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 8.8 tok/s is the aggregate decode rate across all 2 streams, about 4.4 tok/s per stream, and the 3691 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 114 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; 47546 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-397B-A17B-GGUF @ da33c16fa4440f831149fcf53b98a22bc07785e5, file Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.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: 9.9, 8.8, 11.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 30, 2026, 1:54 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

Intel Arc Pro B65 32GB
llama.cppUD-Q2_K_XL6.527.212.39,998 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

27.2 tok/s

Total

12.3 tok/s

TTFT

9,998 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

272

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

3

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

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

4

Concurrency

3

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-np4-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 16384 -np 4 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-np4-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 3 requests sent at once (lmx --concurrency 3); 6.5 tok/s is the aggregate decode rate across all 3 streams, about 2.2 tok/s per stream, and the 9998 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 4 server slots of 4096 tokens each, context 16384. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 5.4, 8.3, 6.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 30, 2026, 1:52 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

Intel Arc Pro B65 32GB
llama.cppUD-Q2_K_XL9.249.317.35,597 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

9.2 tok/s

Prefill

49.3 tok/s

Total

17.3 tok/s

TTFT

5,597 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

276

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

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

4

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-np4-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 16384 -np 4 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-np4-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 9.2 tok/s is the aggregate decode rate across all 2 streams, about 4.6 tok/s per stream, and the 5597 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 4 server slots of 4096 tokens each, context 16384. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 9.2, 9.3, 9.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 30, 2026, 1:52 AM

Last edited

—

Qwen3.5-397B-A17B

17B MoE · Qwen

RTX A6000 48GB
llama.cppUD-IQ2_XXS10.510920.62,774 ms0k
Show all details for Qwen3.5-397B-A17B

Model

Qwen/Qwen3.5-397B-A17B

Display name

Qwen3.5-397B-A17B

Base model

—

Revision

da33c16fa4440f831149fcf53b98a22bc07785e5

Family

Qwen

Parameters

397B

Active params

17B

MoE

yes

Output

10.5 tok/s

Prefill

109 tok/s

Total

20.6 tok/s

TTFT

2,774 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

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_XXS

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q397-iq2xxs-v16 --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/Qwen3.5-397B-IQ2XXS/UD-IQ2_XXS/Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q397-iq2xxs-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 114 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; 47546 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.5-397B-A17B-GGUF @ da33c16fa4440f831149fcf53b98a22bc07785e5, file Qwen3.5-397B-A17B-UD-IQ2_XXS-00001-of-00004.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 10.2, 10.5, 10.5 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 30, 2026, 1:44 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

Intel Arc Pro B65 32GB
llama.cppUD-Q2_K_XL7.052.612.75,210 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

7.0 tok/s

Prefill

52.6 tok/s

Total

12.7 tok/s

TTFT

5,210 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

274

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

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

4

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-np4-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 16384 -np 4 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-np4-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 4 server slots of 4096 tokens each, context 16384. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 7.1, 7, 6.9 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 30, 2026, 1:42 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

Intel Arc Pro B65 32GB
llama.cppUD-Q2_K_XL12.35.922.346,128 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

12.3 tok/s

Prefill

5.9 tok/s

Total

22.3 tok/s

TTFT

46,128 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

273

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 12.3 tok/s is the aggregate decode rate across all 16 streams, about 0.8 tok/s per stream, and the 46128 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 12.7, 12.2, 12.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 30, 2026, 1:42 AM

Last edited

—

MiniMax-M3

427B MoE · Minimax

RTX A6000 48GB
llama.cppUD-IQ1_M7.053.018.38,325 ms0k
Show all details for MiniMax-M3

Model

MiniMaxAI/MiniMax-M3

Display name

MiniMax-M3

Base model

—

Revision

abdce34b5ab2e7dd9260130415c01a1f25561210

Family

Minimax

Parameters

427B

Active params

—

MoE

yes

Output

7.0 tok/s

Prefill

53.0 tok/s

Total

18.3 tok/s

TTFT

8,325 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

441

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

6

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ1_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

6

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-mmm3-iq1m-v16 --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/MiniMax-M3-IQ1M/UD-IQ1_M/MiniMax-M3-UD-IQ1_M-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mmm3-iq1m-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 6 requests sent at once (lmx --concurrency 6); 7 tok/s is the aggregate decode rate across all 6 streams, about 1.2 tok/s per stream, and the 8324 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 129 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; 46918 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/MiniMax-M3-GGUF @ abdce34b5ab2e7dd9260130415c01a1f25561210, file MiniMax-M3-UD-IQ1_M-00001-of-00004.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: 7, 7, 4.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 30, 2026, 1:24 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

Intel Arc Pro B65 32GB
llama.cppUD-Q2_K_XL8.65.015.454,654 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

8.6 tok/s

Prefill

5.0 tok/s

Total

15.4 tok/s

TTFT

54,654 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

273

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 8.6 tok/s is the aggregate decode rate across all 12 streams, about 0.7 tok/s per stream, and the 54654 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 8.5, 9.2, 8.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 30, 2026, 1:12 AM

Last edited

—

MiniMax-M3

427B MoE · Minimax

RTX A6000 48GB
llama.cppUD-IQ1_M5.078.613.25,595 ms0k
Show all details for MiniMax-M3

Model

MiniMaxAI/MiniMax-M3

Display name

MiniMax-M3

Base model

—

Revision

abdce34b5ab2e7dd9260130415c01a1f25561210

Family

Minimax

Parameters

427B

Active params

—

MoE

yes

Output

5.0 tok/s

Prefill

78.6 tok/s

Total

13.2 tok/s

TTFT

5,595 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

440

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

3

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ1_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

3

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-mmm3-iq1m-v16 --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/MiniMax-M3-IQ1M/UD-IQ1_M/MiniMax-M3-UD-IQ1_M-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mmm3-iq1m-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 3 requests sent at once (lmx --concurrency 3); 5 tok/s is the aggregate decode rate across all 3 streams, about 1.7 tok/s per stream, and the 5595 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 129 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; 46918 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/MiniMax-M3-GGUF @ abdce34b5ab2e7dd9260130415c01a1f25561210, file MiniMax-M3-UD-IQ1_M-00001-of-00004.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 4.9, 6.4, 5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 30, 2026, 12:53 AM

Last edited

—

MiniMax-M3

427B MoE · Minimax

RTX A6000 48GB
llama.cppUD-IQ1_M5.010713.14,120 ms0k
Show all details for MiniMax-M3

Model

MiniMaxAI/MiniMax-M3

Display name

MiniMax-M3

Base model

—

Revision

abdce34b5ab2e7dd9260130415c01a1f25561210

Family

Minimax

Parameters

427B

Active params

—

MoE

yes

Output

5.0 tok/s

Prefill

107 tok/s

Total

13.1 tok/s

TTFT

4,120 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

441

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-IQ1_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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-mmm3-iq1m-v16 --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/MiniMax-M3-IQ1M/UD-IQ1_M/MiniMax-M3-UD-IQ1_M-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mmm3-iq1m-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 5 tok/s is the aggregate decode rate across all 2 streams, about 2.5 tok/s per stream, and the 4120 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 129 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; 46918 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/MiniMax-M3-GGUF @ abdce34b5ab2e7dd9260130415c01a1f25561210, file MiniMax-M3-UD-IQ1_M-00001-of-00004.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, 5.2, 2.8 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 30, 2026, 12:43 AM

Last edited

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MiniMax-M3

427B MoE · Minimax

RTX A6000 48GB
llama.cppUD-IQ1_M6.612716.63,461 ms0k
Show all details for MiniMax-M3

Model

MiniMaxAI/MiniMax-M3

Display name

MiniMax-M3

Base model

—

Revision

abdce34b5ab2e7dd9260130415c01a1f25561210

Family

Minimax

Parameters

427B

Active params

—

MoE

yes

Output

6.6 tok/s

Prefill

127 tok/s

Total

16.6 tok/s

TTFT

3,461 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

441

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-IQ1_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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-mmm3-iq1m-v16 --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/MiniMax-M3-IQ1M/UD-IQ1_M/MiniMax-M3-UD-IQ1_M-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mmm3-iq1m-v16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 129 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; 46918 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/MiniMax-M3-GGUF @ abdce34b5ab2e7dd9260130415c01a1f25561210, file MiniMax-M3-UD-IQ1_M-00001-of-00004.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 6.6, 6.6, 6.5 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 30, 2026, 12:43 AM

Last edited

—

Step-3.5-Flash

199B

RTX A6000 48GB
llama.cppQ4_K3.934.98.08,125 ms0k
Show all details for Step-3.5-Flash

Model

stepfun-ai/Step-3.5-Flash

Display name

Step-3.5-Flash

Base model

Step-3.5-Flash

Revision

c439ad64d667632f3e1507757cbe225b6a46e6b8

Family

—

Parameters

199B

Active params

—

MoE

no

Output

3.9 tok/s

Prefill

34.9 tok/s

Total

8.0 tok/s

TTFT

8,125 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

283

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

Q4_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-step35f-q4k-v16-kv8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Step-3.5-Flash/Step-3.5-Flash-Q4_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-step35f-q4k-v16-kv8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 3.9 tok/s is the aggregate decode rate across all 4 streams, about 1.0 tok/s per stream, and the 8125 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 118 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47406 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768, KV cache q8_0 keys / q8_0 values (-ctk/-ctv). GGUF ggml-org/Step-3.5-Flash-GGUF @ c439ad64d667632f3e1507757cbe225b6a46e6b8, file Step-3.5-Flash-Q4_K.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 9.5, 4.8, 3.9 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 30, 2026, 12:33 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

Intel Arc Pro B65 32GB
llama.cppUD-Q2_K_XL6.617.312.815,719 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

6.6 tok/s

Prefill

17.3 tok/s

Total

12.8 tok/s

TTFT

15,719 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

272

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

6

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

6

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 6 requests sent at once (lmx --concurrency 6); 6.6 tok/s is the aggregate decode rate across all 6 streams, about 1.1 tok/s per stream, and the 15719 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 9.6, 7.2, 6.6 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 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 30, 2026, 12:32 AM

Last edited

—

Step-3.5-Flash

199B

RTX A6000 48GB
llama.cppQ4_K20.210234.92,753 ms0k
Show all details for Step-3.5-Flash

Model

stepfun-ai/Step-3.5-Flash

Display name

Step-3.5-Flash

Base model

Step-3.5-Flash

Revision

c439ad64d667632f3e1507757cbe225b6a46e6b8

Family

—

Parameters

199B

Active params

—

MoE

no

Output

20.2 tok/s

Prefill

102 tok/s

Total

34.9 tok/s

TTFT

2,753 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

282

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

Q4_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-step35f-q4k-v16-kv8 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Step-3.5-Flash/Step-3.5-Flash-Q4_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-step35f-q4k-v16-kv8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 118 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47406 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768, KV cache q8_0 keys / q8_0 values (-ctk/-ctv). GGUF ggml-org/Step-3.5-Flash-GGUF @ c439ad64d667632f3e1507757cbe225b6a46e6b8, file Step-3.5-Flash-Q4_K.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 21.2, 20, 20.2 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 30, 2026, 12:23 AM

Last edited

—

GLM-5.3-Flash

321B

RTX A6000 48GB
llama.cppUD-Q2_K_XL7.333.814.58,434 ms0k
Show all details for GLM-5.3-Flash

Model

zai-org/GLM-5.3-Flash

Display name

GLM-5.3-Flash

Base model

—

Revision

a38483c8cd5df544f53d70fb281afe97369d5ab6

Family

—

Parameters

321B

Active params

—

MoE

no

Output

7.3 tok/s

Prefill

33.8 tok/s

Total

14.5 tok/s

TTFT

8,434 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

285

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-Q2_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

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-glm53f-q2kxl-v16-kv8 --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/GLM-5.3-Flash/UD-Q2_K_XL/GLM-5.3-Flash-UD-Q2_K_XL-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 8 -tb 16 --metrics -b 4096 -ub 2048 -lm none -ctk q8_0 -ctv q8_0

Extra flags

--d --name lsv-glm53f-q2kxl-v16-kv8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --b 4096 --lm none --ctk q8_0 --ctv q8_0

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 7.3 tok/s is the aggregate decode rate across all 4 streams, about 1.8 tok/s per stream, and the 8434 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 16 vCPU pinned to 8 of the 16 cores (16 threads) of an AMD EPYC 7343 (another VM holds the other 8 cores) 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 108 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; 47800 MiB of VRAM in use after load, 8 generation threads, 16 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768, KV cache q8_0 keys / q8_0 values (-ctk/-ctv). GGUF unsloth/GLM-5.3-Flash-GGUF @ a38483c8cd5df544f53d70fb281afe97369d5ab6, file GLM-5.3-Flash-UD-Q2_K_XL-00001-of-00004.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: 6.7, 8.6, 7.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

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Submitted

Oct 30, 2026, 12:13 AM

Last edited

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DeepSeek-V4-Flash-0731

304B MoE · Deepseek

Intel Arc Pro B65 32GB
llama.cppUD-Q2_K_XL6.020.711.413,113 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

6.0 tok/s

Prefill

20.7 tok/s

Total

11.4 tok/s

TTFT

13,113 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

271

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q2_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

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

8

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 8 -tb 16 --metrics -ctk q4_0 -ctv q4_0 -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-kv4-np16-t8-fit --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 16 --metrics --ctk q4_0 --ctv q4_0 --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 6 tok/s is the aggregate decode rate across all 4 streams, about 1.5 tok/s per stream, and the 13113 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 16 vCPU pinned to 8 cores (16 threads) of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 8 generation threads, 16 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_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: 8.3, 6, 8.5 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 30, 2026, 12:11 AM

Last edited

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