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.

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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: NVIDIA A40 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.

NVIDIA hardware
generation:
41.1 tokens/s
prompt processing (prefill):
532-541 tokens/s
quant:
Q2_K (GGUF)

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports DeepSeek V4.1 Flash at 41.1 tok/s decode on 8× NVIDIA A40 with layer-split execution. Setup is the TensorSharp engine with Q2_K weights; prefill measured 532–541 tok/s with GPU-resident Engram, and cold model loading from MooseFS took 144–155 seconds. Decode improved from 35.6 to 41.1 tok/s after reducing decode graph partitions from 565–577 to 8. These are project benchmarks, not a comparison against llama.cpp.

Sep 18, 2026
NVIDIA hardware
generation:
32.5 tokens/s
prompt processing (prefill):
492.1 tokens/s
quant:
Q4_K_M (GGUF)
kv:
F16

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports DeepSeek V4.1 Flash at 32.5 t/s single-stream decode on 8× NVIDIA A40 with Q4_K_M GGUF. Setup is TensorSharp with layer split, F16 KV cache, and 65,536-token configured context. Prefill measurements use approximately 4.9K-token prompts. Q2_K achieved 40.72 t/s decode and 539 t/s prefill. Q4_K_M aggregate decode throughput was 39.3 t/s at 2 concurrent requests, 48.9 t/s at 4, and 48.5 t/s at 8. Layer split beat routed-MoE tensor parallelism (31–32.5 vs. 21.4–22 t/s).

Sep 12, 2026
Tone: positiveNVIDIA hardware
generation:
31.5 tokens/s
prompt processing (prefill):
836.0 tokens/s
quant:
Q8_K_XL (GGUF)

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User benchmarks TensorSharp against llama.cpp on 4x A40 GPUs, with TensorSharp's CUDA backend reaching 836 t/s prefill at 16K context and 31.5 t/s decode short. The model is DeepSeek-V4-Flash-0731-UD-Q8_K_XL from unsloth.

Sep 7, 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