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: AMD MI50 16GB
Compare setup details

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

AMD hardware
generation:
49.2 tokens/s
prompt processing (prefill):
937.5 tokens/s
quant:
Q5_K_XL (GGUF)

User benchmarks Qwen3.6-35B-A3B at 49.19 t/s generation and 937.47 t/s prompt processing on two AMD MI50 16GB cards. Setup is llama.cpp build b11325 with Vulkan, Q5_K_XL GGUF, flash attention on, 99 GPU layers, and each card's power limit lowered to 145 watts. The table also covers eight other models on the same dual-MI50 rig, with generation rates from 12.05 to 60.57 t/s. A third MI50 is on hand but not yet benchmarked.

Oct 4, 2026

Qwen3.8 27B

2× AMD MI50 16GB · llama.cpp

Tone: positiveAMD hardware
generation:
48.0 tokens/s
quant:
Q4_0 (GGUF)
agenticcoding

User reports Qwen3.8-27B at 48 tok/s generation on two AMD Instinct MI50 16GB cards, up from 19.4 tok/s with stock llama.cpp. Setup is patched llama.cpp (b11235) on ROCm 7.14 with Q4_0 weights, tensor split plus MTP speculative decoding with 3 draft tokens, 85 W per card. The 48 tok/s is the patched result; the same setup without the gfx906 kernel patches gave 40-44 tok/s, and a vLLM gfx906 fork with GPTQ-Int4 and TP=2 reached 34.7 tok/s.

Oct 3, 2026

Qwen3.8 27B

3× AMD MI50 16GB · llama.cpp · 262,144 ctx

Tone: positiveAMD hardware
generation:
30.1 tokens/s
quant:
Q8_0 (GGUF)
kv:
F16
flash attention:
on
agenticlong-context

User reports Qwen3.8-27B Q8_0 at about 30 tok/s decode on 3x AMD Instinct MI50 16GB (gfx906) with 262,144 configured context. Setup is llama.cpp with HIP/ROCm 6.2.4, RCCL, tensor split mode, F16 KV cache, flash attention on, and about 43.7 GiB aggregate VRAM use. At 216k occupied context decode was 18.24 tok/s with 187.75 tok/s prompt throughput. With HIP graphs enabled, a tiny request after a ~236k stress run stayed at 8.74 tok/s until the server was restarted; disabling graphs (GGML_CUDA_DISABLE_GRAPHS=1) cost about 3% at short context but restored ~30 tok/s immediately after the long-context run.

Sep 26, 2026
Tone: positiveAMD hardware
generation:
16.9 tokens/s
quant:
IQ4 (GGUF)
kv:
Q8

User reports Qwen3.8 Flash-Next at 16.9 t/s on a single MI50 16GB with the gfx906-16gb-expert-pool llama.cpp fork. Setup uses the moe-expert-pool branch with IQ4 GGUF weights, 128K context and Q8 KV cache; the expert cache profile of 66 admits 144 pools at roughly 69% hit rate and peaks at 15.24 GiB VRAM. A stock CPU MoE path without the expert cache reached 11.76 t/s, and up to 19.8 t/s was observed once warm.

Sep 18, 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