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: NVIDIA RTX 4060 Ti 16GB
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: positiveNVIDIA hardware
generation:
18.7 tokens/s
prompt processing (prefill):
210.0 tokens/s
quant:
Q5_K_M (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.

codingcreative-writing

User reports Qwen3.8 Flash at 18.7 t/s generation on a Ryzen 7700 with 64 GB DDR5 and an RTX 4060 Ti 8 GB. Setup is a custom llama.cpp fork (myllama.cpp) running the 35B MoE model with 6B active parameters, CUDA and Windows specific, with no MTP support. An initial IQ4_XS quant gave 100 t/s prefill and 20 t/s generation, degrading to 15 t/s at 100K context. A switch to Q5_K_M dropped to 15 t/s prefill and 10-13 t/s generation from MMAP page faulting. A four-layer system followed: an unbuffered raw file reader with a RAM buffer for 2 slabs reached 150 t/s prefill, a RAM expert cache with decay factor (55-56 GB, covering 85-95% of requests) gave 14-15 t/s generation, memcpy from cache to buffer during prefill reached 215 t/s prefill, a 2 GB VRAM cache for top experts gave 18.0 t/s generation, and direct IO for embeddings gave 18.7 t/s.

Sep 11, 2026
Tone: positiveNVIDIA hardware
generation:
7.4 tokens/s
prompt processing (prefill):
15.2 tokens/s
quant:
UD-Q2_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 reports a 98 GB model running on 16 GB VRAM with CPU offload, improving from 2 to 7 t/s with recent llama.cpp updates.

Sep 7, 2026

Qwen3.8 27B

NVIDIA RTX 4060 Ti 16GB · llama.cpp · 128,000 ctx

Tone: mixedNVIDIA hardware
generation:
11.0 tokens/s
quant:
IQ3_S (GGUF)
kv:
Q8

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.

codingagentic

User reports Qwen3.8 27B IQ3_S at about 11 t/s for code generation and 6-7 t/s on reasoning. Setup is speculative decoding with a Q4_K_M draft model. User asks for optimization advice.

Sep 7, 2026
Showing 1–3 of 3
Page 1 of 1

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