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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What runs on your hardware?

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: M3 Ultra 256GB
Compare setup details (1 active)

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

Tone: negativeApple hardware
generation:
25.0 tokens/s
quant:
4bit
agenticcodinglong-context

User reports Minimax-m2.1-4bit at 25 t/s generation on an M3 Ultra 256GB with 80-core GPU, compared against llama.cpp with flash attention at 32 t/s. Setup is MLX with 4bit weights at 30K context; the same model under llama.cpp with flash attention reached 32 t/s generation and 78s prompt processing. At 146K context MLX fell to 5.95 t/s generation versus 12.12 t/s for llama.cpp, and the user says MLX runs about 50% slower on long-context generation, impacting agentic coding.

Oct 4, 2026
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Community benchmarks snapshot

Records by GPU

AMD Strix Halo 128GB318NVIDIA RTX 3090219NVIDIA RTX 5090169NVIDIA DGX Spark91NVIDIA RTX 5060 Ti 16GB72NVIDIA RTX Pro 6000 Blackwell70M5 Max 48GB67NVIDIA RTX 3060 12GB65AMD Radeon AI PRO R9700 32GB63NVIDIA RTX 409057

Records by model

2687 total
Qwen3.81268
Qwen3.6282
Gemma 4168
DeepSeek V4 Flash149
Qwen3.5121
Qwen375
other624

Records by engine

1974 total
llama.cpp1007
vLLM205
Strata148
MLX106
Ollama83
other425

Use cases

coding 731agentic 465long-context 340tool-use 207vision 134summarization 61math 57creative-writing 46multilingual 29rp 13text-generation 9reasoning 3
coding731agentic465long-context340tool-use207vision134summarization61math57creative-writing46

Median generation t/s by GPU

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

RTX 509080RTX Pro 600067M5 Ultra 256GB50RX 7900 XTX43RTX 409040RTX 309038RX 7800 XT 16GB35RTX 5090 Laptop 24GB34M3 Ultra 512GB32V100 32GB31

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 27B709Qwen3.8 125B · 6B active496Qwen3.6 35B · 3B active192DeepSeek V4 Flash 284B · 13B active132Qwen3.6 27B79Gemma 4 26B · 4B active59DeepSeek V4.1 Flash 552B · 16B active48GLM-5.3 320B · 18B active45

Quants

Q4_K_M341UD-Q4_K_XL156NVFP4148Q8_01074-bit91IQ4_XS76Q4_K_XL74MXFP466IQ3_XXS65IQ3_S58