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 →

Model: MiniMax M3.1 Flash
Compare setup details

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

NVIDIA hardware
generation:
23-31 tokens/s
quant:
2-bit (GGUF)
codingagentictool-usevisionlong-context

User rounds up community quantizations of MiniMax M3.1 Flash (428B total, 23B active), whose open weights are 252.4 GB, and reports each builder's own figures. The one local run with numbers is superalesha's on 4x RTX 3090 with an own engine and Unsloth 2-bit weights: 23 to 31 tok/s decode and 1120 tok/s prefill at 16K context. The builder ran out of RAM at 128 GB and moved to 512 GB to continue. Other builds listed: Maya-XS at 93 GB for a 128 GB PC, SAGE-EXL3 at 108.64 GiB on one DGX Spark with 24.37 to 29.72 tok/s decode at 48K context, Unsloth UD-Q2_K_XL at 143.9 GB, plus oMLX, AMD Quark MXFP4, EXL3-EAQ and Atomic Chat releases. The user says they have only used the model hosted.

Nov 2, 2026
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Community benchmarks snapshot

Records by GPU

AMD Strix Halo 128GB327NVIDIA RTX 3090228NVIDIA RTX 5090175NVIDIA DGX Spark94NVIDIA RTX 5060 Ti 16GB72NVIDIA RTX Pro 6000 Blackwell71NVIDIA RTX 3060 12GB69M5 Max 48GB67AMD Radeon AI PRO R9700 32GB66NVIDIA RTX 409062

Records by model

2785 total
Qwen3.81326
Qwen3.6291
Gemma 4168
DeepSeek V4 Flash149
Qwen3.5124
Qwen375
other652

Records by engine

2031 total
llama.cpp1022
vLLM209
Strata165
MLX107
Ollama87
other441

Use cases

coding 767agentic 493long-context 357tool-use 216vision 146summarization 64math 58creative-writing 47multilingual 31rp 13text-generation 9reasoning 3
coding767agentic493long-context357tool-use216vision146summarization64math58creative-writing47

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 309037RX 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 27B725Qwen3.8 125B · 6B active535Qwen3.6 35B · 3B active200DeepSeek V4 Flash 284B · 13B active132Qwen3.6 27B80Gemma 4 26B · 4B active59DeepSeek V4.1 Flash 552B · 16B active55GLM-5.3 320B · 18B active47

Quants

Q4_K_M350UD-Q4_K_XL164NVFP4151Q8_01104-bit95IQ4_XS77Q4_K_XL76MXFP466IQ3_XXS66IQ3_S66