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.

Join the community

Follow along:XLinkedIn

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: NVIDIA RTX 6000 Ada
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:
49.6 tokens/s
prompt processing (prefill):
2370.2 tokens/s
quant:
2.20 bpw (EXL3)

User reports MiMo-V2.6-Flash-RL at 49.57 tok/s decode (p50, single stream) on an NVIDIA RTX 6000 Ada 96GB. Setup is ExLlamaV3 (vcruz305 fork) with a 2.20 bpw EXL3 pack (86.94 GB) at 65,536-token context and a 65,536-token KV pool; prefill measured 2,370.2 tok/s p50. With the DFlash drafter attached, decode rises to 184.11 tok/s p50 and prefill is 2,257.7 tok/s. Aggregate throughput under load reaches 236.2 tok/s at C=8 without the drafter and 330.6 tok/s with it; the eight-stream per-stream decode p50 is 34.95 and 54.46 tok/s respectively. The 2.50 bpw pack (98.48 GB) does not fit a 96 GB card. On a DGX Spark / GB10 unified-memory host the 2.50 bpw pack runs at 34.9 tok/s decode (code) and 23.7 tok/s (prose) with draft_accept 0.78.

Oct 6, 2026
NVIDIA hardware
generation:
260.0 tokens/s
quant:
Q4_K_M (GGUF)
kv:
q8_0

User reports Ornith-Agents-A1-3.6-35B-A3B (a dare_ties merge on Qwen3.6-35B-A3B with a grafted MTP block) at up to ~260 tok/s on an RTX 6000 Ada with 96GB VRAM. Setup is llama.cpp with Q4_K_M GGUF, q8_0 KV cache, 131072 context, --parallel 4, and native MTP speculative decoding (--spec-type draft-mtp, --spec-draft-n-max 4). Model used ~25 GB of the 95.6 GB VRAM. MTP draft acceptance stayed in the 42-48% range across generation lengths; measured throughput converged to ~245-260 tok/s. Q8_0 with MTP gave 190 tok/s, MTP disabled gave 150-200 tok/s, and vLLM measured 130 tok/s. spec-draft-n-max 3 gave 270 tok/s and spec-draft-n-max 2 gave 277 tok/s.

Oct 4, 2026
Showing 1–2 of 2
Page 1 of 1

Community benchmarks snapshot

Records by GPU

AMD Strix Halo 128GB320NVIDIA RTX 3090219NVIDIA RTX 5090170NVIDIA DGX Spark91NVIDIA RTX 5060 Ti 16GB72NVIDIA RTX Pro 6000 Blackwell71M5 Max 48GB67NVIDIA RTX 3060 12GB65AMD Radeon AI PRO R9700 32GB63NVIDIA RTX 409057

Records by model

2700 total
Qwen3.81274
Qwen3.6284
Gemma 4168
DeepSeek V4 Flash149
Qwen3.5121
Qwen375
other629

Records by engine

1981 total
llama.cpp1008
vLLM206
Strata150
MLX106
Ollama83
other428

Use cases

coding 739agentic 467long-context 342tool-use 207vision 137summarization 61math 58creative-writing 46multilingual 29rp 13text-generation 9reasoning 3
coding739agentic467long-context342tool-use207vision137summarization61math58creative-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 active501Qwen3.6 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_XL157NVFP4149Q8_01074-bit91IQ4_XS76Q4_K_XL74MXFP466IQ3_XXS65IQ3_S59