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 3090
Compare setup details (1 active)

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

Qwen3.8 27B

4× NVIDIA RTX 3090 · SGLang · 512 ctx

Tone: mixedNVIDIA hardware
prompt processing (prefill):
3843.0 tokens/s
quant:
W8A8 (safetensors)
kv:
bf16

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.

codingagenticlong-context

User reports Qwen3.8-27B W8A8 on 4x RTX 3090 capped at 220 W with 3,843 t/s prefill at 512 prompt tokens (single request); decode is given only per task, 146.6 t/s narrative and 265.5 t/s code, with no overall figure. Setup is SGLang v0.5.19 on 4x RTX 3090 with TP=4, W8A8 INT8 weights, bf16 KV cache, DFlash2 speculative decoding with 8 draft tokens, HiCache ratio 1.5 and a 262144 context window. The c=1 prefill sweep ran 3,843 t/s at 512 tokens up to a 5,058 t/s peak at 4,096, and about 4,000-4,300 t/s at 10K and 3,029 t/s at 90K. All four GPUs were capped to 220 W of the 350 W default (62%), so every figure is power-limited. A concurrency sweep reached 1,811 t/s aggregate code decode at c=16 (113.2 t/s per request).

Oct 8, 2026

Qwen3.8 27B

NVIDIA RTX 3090 · SGLang · 262,144 ctx

NVIDIA hardware
prompt processing (prefill):
904.0 tokens/s
quant:
3.00bpw (EXL3)
kv:
fp8

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 Qwen3.8-27B EXL3 3.00bpw on a bare RTX 3090 24 GB through SGLang with the sglang-exl3 plugin, with cold prefill at 904 tok/s. Setup uses fp8 KV cache, MTP speculative decoding on, thinking off, one request, at the full 262,144-token window; the 2.4 GB token embedding sits in pinned host memory. Decode is given per workload: 98.8 t/s on prose, 143.3 on code and 89.6 on prose with a 32k prompt. A Qwen3.6-35B-A3B EXL3 recipe on the same card reached 251.9 prose and 352.1 code. A prior AWQ-INT4 27B recipe with no draft is cited at 45.1 t/s prose.

Oct 8, 2026
NVIDIA hardware
generation:
96.2 tokens/s
quant:
EXL3 3.0 bpw (EXL3)

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 96.2 tok/s on prose and 141.1 tok/s on code on an RTX 3090. Setup is SGLang with EXL3 3.0 bpw weights and an MTP draft model, single request. The figures come from the trellis-serve project's registry recipes; the same table also lists Qwen3.6-35B-A3B at 251.7 tok/s prose and 357.9 tok/s code on the RTX 3090, and Qwen3.8-27B at 91.2 tok/s prose and 66.1 tok/s code on an Intel Arc Pro B70.

Oct 5, 2026

Qwen3.8 27B

4× NVIDIA RTX 3090 · SGLang · 8,192 ctx

NVIDIA hardware
generation:
136.8 tokens/s
prompt processing (prefill):
1581.0 tokens/s
quant:
AWQ-INT4 (AWQ)
kv:
bf16

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.

codingvisionlong-context

User reports Qwen3.8-27B at 136.8 tok/s single-stream decode (8k context, concurrency 1) on 4x RTX 3090. Setup is SGLang with AWQ-INT4 (W4A16 Marlin) weights and bf16 KV cache, tensor parallel 4, DSpark speculative decoding enabled with a 1.36B draft model, 275,018-token KV pool. Prefill sustains 1,581 tok/s at 8k c1 and 1.3-1.6k tok/s across all context lengths; aggregate decode reaches 169.8 tok/s at 8 concurrent requests. Max context 128k, max 9 concurrent requests (GDN state bound). Also benchmarks 2x 3090 (112.9 tok/s decode at 8k c1) and 1x 3090 without DSpark (43.2 tok/s decode, 8k context limit).

Oct 3, 2026

Qwen3.8 27B

NVIDIA RTX 3090 · SGLang · 131,072 ctx

NVIDIA hardware
generation:
98.2 tokens/s
prompt processing (prefill):
914.0 tokens/s
quant:
3.00bpw (EXL3)
kv:
fp8

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 Qwen3.8-27B at 98.2 t/s on prose and 225.1 t/s on code on one RTX 3090. Setup is SGLang with the sglang-exl3 plugin, EXL3 3.00bpw weights, fp8 KV cache, a 131,072-token window, and DFlash2 speculative decoding with a 5.0bpw draft. Prompt processing on a new uncached 32k prompt runs at about 914 tok/s with 35.9 s to first token. Compared with the MTP recipe on the same card, code is +57% (225 vs 143) and prose is the same (98 vs 98), with a 131k window instead of 262k. Max concurrency is 1 because two 32k streams stall each other's decode.

Oct 3, 2026
Showing 1–5 of 5
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