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.

Free to use. No account needed. Memory estimates and community measurements are labelled separately.

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Search for a model to see its reported speeds across GPUs and Macs.

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: M2 Ultra 128GB
Compare setup details (1 active)

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

Apple hardware
generation:
71.2 tokens/s
prompt processing (prefill):
1096.8 tokens/s
quant:
UD-Q4_K_XL (GGUF)
kv:
F16
flash attention:
on
mtp (multi-token prediction):
off

User reports Qwen3-Coder 30B-A3B at 71.2 t/s generation and 1096.8 t/s prompt processing on a Mac Studio M2 Ultra with 128GB unified memory. Setup is llama.cpp build 6060 with the UD-Q4_K_XL GGUF and flash attention enabled, run with -ngl 100 on one 60-core GPU. The figures are separate synthetic llama-bench tests: PP512 at 1096.84 +/- 5.48 t/s and TG128 at 71.23 +/- 0.08 t/s, with 16 CPU threads and a 16.45 GiB model footprint.

Oct 13, 2026
Apple hardware
generation:
69.3 tokens/s
prompt processing (prefill):
1080.5 tokens/s
quant:
UD-Q4_K_XL (GGUF)
kv:
F16
flash attention:
off
mtp (multi-token prediction):
off

User reports Qwen3-Coder 30B-A3B at 69.3 t/s generation and 1080.5 t/s prompt processing on a Mac Studio M2 Ultra with 128GB unified memory. Setup is llama.cpp build 6060 with the UD-Q4_K_XL GGUF and flash attention off, one GPU with -ngl 100 and 16 threads. The figures are separate synthetic llama-bench tests: PP512 and TG128, not a 512-input/128-output request. The same run measured 818.85 t/s PP8192 and 40.35 t/s TG8192.

Oct 13, 2026
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Community benchmarks snapshot

Records by GPU

AMD Strix Halo 128GB320NVIDIA RTX 3090219NVIDIA RTX 5090170NVIDIA 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

2698 total
Qwen3.81272
Qwen3.6284
Gemma 4168
DeepSeek V4 Flash149
Qwen3.5121
Qwen375
other629

Records by engine

1980 total
llama.cpp1008
vLLM205
Strata150
MLX106
Ollama83
other428

Use cases

coding 737agentic 466long-context 342tool-use 207vision 137summarization 61math 57creative-writing 46multilingual 29rp 13text-generation 9reasoning 3
coding737agentic466long-context342tool-use207vision137summarization61math57creative-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 active499Qwen3.6 35B · 3B active193DeepSeek 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_M341UD-Q4_K_XL157NVFP4148Q8_01074-bit91IQ4_XS76Q4_K_XL74MXFP466IQ3_XXS65IQ3_S59