llamaperf

Local model performance on your hardware

Find which open-weight LLMs fit in your GPU or Mac and compare the speeds people report on setups like yours.

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Pick your GPU or Mac, then read the speeds people reported on it, or estimate which models fit and how fast they run.

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Local model performance reports from the community

These are individual setups, not a controlled benchmark. Compare GPU count, quantization, context and offloading before comparing speeds. How to read a report →

GPU: NVIDIA B300 288GB
Compare setup details (1 active)

Exact recorded values. Context may be a configured limit; matching filters does not establish identical prompts, offloading or concurrency.

NVIDIA hardware
generation:
92.0 tokens/s
quant:
MXFP4 (MXFP4)

User reports Kimi K3 (2.8T parameters) at 92 tok/s decode on 8x B300 via Modal. Setup is vLLM with MXFP4 weights, tensor parallel 8, cold boot about 27 min for a 1.56 TB load. TTFT is 0.92 to 1.02 s and average decode over 4 prompts is 83 tok/s. Cost is $56.79 per hour, $190 per million output tokens, about $36 per run, or $1,363 a day left warm. User also ran Unsloth's Dynamic GGUF 1-bit UD-IQ1_S (594 GB) on 8x A100-80GB via llama.cpp at about 9 tok/s with TTFT 7 to 60 s, $19.99 per hour and about $620 per million tokens, 3.3x more expensive per token. Quality at 1-bit was fine.

Sep 27, 2026
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Community benchmarks snapshot

Records by GPU

AMD Strix Halo 128GB322NVIDIA RTX 3090219NVIDIA RTX 5090171NVIDIA DGX Spark91NVIDIA RTX 5060 Ti 16GB72NVIDIA RTX Pro 6000 Blackwell71M5 Max 48GB67NVIDIA RTX 3060 12GB65AMD Radeon AI PRO R9700 32GB63NVIDIA RTX 409057

Records by model

2718 total
Qwen3.81285
Qwen3.6285
Gemma 4168
DeepSeek V4 Flash149
Qwen3.5121
Qwen375
other635

Records by engine

1992 total
llama.cpp1014
vLLM207
Strata151
MLX107
Ollama84
other429

Use cases

coding 741agentic 470long-context 344tool-use 209vision 143summarization 61math 58creative-writing 46multilingual 30rp 13text-generation 9reasoning 3
coding741agentic470long-context344tool-use209vision143summarization61math58creative-writing46

Median generation t/s by GPU

On Qwen3.8 27B at 4-bit, plain single-GPU runs. Full ranking

RTX 509080RTX Pro 600067RTX 5070 Ti55M5 Ultra 256GB50RX 7900 XTX43RTX 409040RTX 309038RX 7800 XT 16GB35RTX 5090 Laptop 24GB34M3 Ultra 512GB32

Prompt processing, same model

M5 Ultra 256GB1800RX 7900 XTX640RTX 40901949RTX 3090561M3 Ultra 512GB391V100 32GB941Radeon AI PRO R9700 32GB1287RTX 3080 20GB997Arc Pro B70512M1 Ultra 128GB153

Reports by model size

Qwen3.8 27B715Qwen3.8 125B · 6B active506Qwen3.6 35B · 3B active194DeepSeek V4 Flash 284B · 13B active132Qwen3.6 27B80Gemma 4 26B · 4B active59DeepSeek V4.1 Flash 552B · 16B active50GLM-5.3 320B · 18B active45

Quants

Q4_K_M343UD-Q4_K_XL158NVFP4149Q8_01074-bit93IQ4_XS76Q4_K_XL75MXFP466IQ3_XXS66IQ3_S59