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.

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.

Looking for a particular model?

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: M4 Pro 48GB
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: positiveApple hardware
generation:
90.0 tokens/s
quant:
oQ4 (MLX)

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is shown only when the source reports it; generation speed cannot tell us time to first token. Check the full setup before comparing.

User reports Qwen3.6 35B-A3B at 90 tok/s on an M4 Pro Mac mini with 48GB unified memory. Setup is oMLX 0.7.0 with a custom oQ4 quantised model using MTP headers. User notes performance had recently fallen as low as 10-11 tok/s before this improvement.

Oct 11, 2026

Qwen3.8 27B

M4 Pro 48GB · oMLX · 200,000 ctx

Apple hardware
generation:
24.8 tokens/s
quant:
oQ4e (MLX)

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is shown only when the source reports it; generation speed cannot tell us time to first token. Check the full setup before comparing.

User reports Qwen3.8-27B at 24.8 tok/s decode on an M4 Pro 48GB, with median TTFT 943 ms over three samples of 4096 prompt tokens and 128 generated tokens. Setup is oMLX 0.6.4 with the oQ4e 4-bit MLX model and Lightning MTP draft depth 3 enabled via the mtp3 profile, at a 200k context window. Enabling MTP through a profile rather than settings.json raised decode from 10 tok/s to 24.8 tok/s. A separate sweep documents the MTP-on prefill curve at 1.8k-30k prompts and a 5-turn agentic session reaching 157k context with 85% token reuse.

Sep 29, 2026
Apple hardware
generation:
1.8 tokens/s
quant:
JANG

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is shown only when the source reports it; generation speed cannot tell us time to first token. Check the full setup before comparing.

User benchmarks MoE streaming in oMLX on an M4 Pro 48GB across four models. GLM-5.3-Flash-JANG-MTP reaches 1.82 tok/s with 12.36s TTFT, 10.52 GiB after load and 14.68 GiB peak. Qwen3.8-JANG 4S reaches 3.63 tok/s with 8.56s TTFT, 7.04 GiB after load and 11.19 GiB peak. Qwen3.8-JANG 4M reaches 3.19 tok/s with 11.23s TTFT, 7.05 GiB after load and 11.11 GiB peak. DeepSeek-V4-Flash-0731-JANG reaches 2.71 tok/s with 6.52s TTFT, 8.25 GiB after load and 16.87 GiB peak. The primary record uses GLM-5.3-Flash-JANG-MTP. The other models are Qwen3.8 in JANG 4S and 4M quants and DeepSeek V4 Flash in the 0731 variant with the JANG quant. MoE streaming allows larger MoE models to run with a lower memory footprint at the cost of speed.

Sep 10, 2026
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Community benchmarks snapshot

Records by GPU

AMD Strix Halo 128GB294NVIDIA RTX 3090190NVIDIA RTX 5090134NVIDIA DGX Spark77M5 Max 48GB65NVIDIA RTX Pro 6000 Blackwell62NVIDIA RTX 5060 Ti 16GB58NVIDIA RTX 3060 12GB54AMD Radeon AI PRO R9700 32GB50M4 Max 128GB49

Records by model

2211 total
Qwen3.81021
Qwen3.6238
DeepSeek V4 Flash147
Gemma 4144
Qwen3.597
Qwen353
other511

Records by engine

1645 total
llama.cpp875
vLLM189
MLX100
Strata70
Ollama62
other349

Use cases

coding 556agentic 357long-context 272tool-use 153vision 109summarization 53math 43creative-writing 37multilingual 23text-generation 9rp 8reasoning 3
coding556agentic357long-context272tool-use153vision109summarization53math43creative-writing37

Median generation t/s by GPU

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

RTX 509082RTX Pro 600067M5 Ultra 256GB50RX 7900 XTX48RTX 309036RTX 5090 Laptop 24GB34RTX 409032V100 32GB31Radeon AI PRO R9700 32GB30RX 7800 XT 16GB30

Prompt processing, same model

M5 Ultra 256GB1800RX 7900 XTX530RTX 3090561RTX 40901949V100 32GB941RTX 3080 20GB997Arc Pro B70512M1 Ultra 128GB153Arc Pro B60 24GB380M1 Max 32GB82

Reports by model size

Qwen3.8 27B566Qwen3.8 125B · 6B active371DeepSeek V4 Flash 284B · 13B active130Qwen3.6 35B · 3B active129Qwen3.6 27B72Gemma 4 26B · 4B active36GLM-5.3 320B · 18B active33DeepSeek V4.1 Flash 552B · 16B active28

Quants

Q4_K_M290UD-Q4_K_XL140NVFP4120Q8_0984-bit79Q4_K_XL71IQ4_XS70MXFP458IQ3_XXS52Q450