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 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.

NVIDIA hardware
generation:
28.6 tokens/s
quant:
exl3-3.05bpw (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 GLM-5.3-Flash at 28.55 tok/s decode on an RTX 3090. Setup is ExLlamaV3 with exl3-3.05bpw and 131k context, all-RAM fast mode with CPU offload requiring ~238 GB free RAM. Lab suite passed 6/6 with 28.6 tok/s decode and 989 tok/s prefill; at concurrency 4 the aggregate was 28.62 tok/s (28.45 per stream) because fast mode decodes one stream at a time.

Oct 9, 2026
NVIDIA hardware
generation:
17.3 tokens/s
prompt processing (prefill):
844.0 tokens/s
quant:
3.05bpw (EXL3)

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 GLM-5.3-Flash at 17.3 tok/s decode on an RTX 3090. Setup is ExLlamaV3 with a 3.05bpw EXL3 quant in NVMe mode (55 GB), 128k context, single card. The context gate read 93,437 prompt tokens at 844 tok/s prefill. An NVMe read cap of 8 GB/s cost about 8% of C1 decode and 26% of prefill; uncapped same-session figures were C1 18.23 and C4 19.59 tok/s. Capped standard table: 8k C1 16.61, C2 14.91, C4 13.10; 32k C1 14.43, C2 15.41.

Oct 9, 2026

Qwen3.8 27B

NVIDIA RTX 3090 · ExLlamaV3 · 150,000 ctx

NVIDIA hardware
generation:
60.0 tokens/s
quant:
exl3-5.00bpw (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.

codingtool-use

User reports Qwen3.8 27B at ~60 t/s on a single RTX 3090. Setup is ExLlamaV3 with an exl3-5.00bpw quant at ~150k context; prefill was not benchmarked. The machine has an Intel 12400 CPU and 32GB DDR4 RAM. User asks whether to buy 128GB DDR5 and a Ryzen 9700X to run the IQ3 quant under Strata, and how Qwen3.8 27B compares to Qwen Next on output accuracy for coding and Hermes assistant use.

Oct 7, 2026

Qwen3.8 27B

NVIDIA RTX 3090 · ExLlamaV3 · 262,144 ctx

NVIDIA hardware
generation:
162.9 tokens/s
quant:
EXL3-4.00bpw (EXL3)
kv:
cq3

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 162.9 tok/s decode on an RTX 3090 with native DFlash2 speculative decoding in ExLlamaV3. Setup is ExLlamaV3 (community fork, pin 355c6ee) with EXL3 4.00bpw target and DFlash2 draft, cq3 KV cache, at the full 262,144-token context. Acceptance length 5.66; autoregressive decode 42.8 tok/s and MTP head 116.3 tok/s for comparison. At 150k-token depth: 25.3 tok/s decode, 594 tok/s prefill, peak 23.13 GB.

Sep 27, 2026
Tone: positiveNVIDIA hardware
generation:
80.0 tokens/s
prompt processing (prefill):
1500.0 tokens/s
quant:
3bpw (EXL3)

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 Flash-Next at 80 t/s generation and 1500 t/s prefill on 3x RTX 3090 with 128GB DDR4. Setup is ExLlamaV3 with an exl3 3bpw quant at 262,144 context, with vision and speculative decoding enabled. A single RTX 5090 with 128GB DDR4 reached 29 t/s generation at the same 1500 t/s prefill and 262k context. User plans to test 4bpw later and says ExLlamaV3 is replacing vLLM and llama.cpp for this model.

Sep 20, 2026
Tone: positiveNVIDIA hardware
generation:
110.0 tokens/s
prompt processing (prefill):
1500.0 tokens/s
quant:
3.05bpw (EXL3)
kv:
FP16

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.

codingtool-use

User reports Qwen3.8 Flash Next at 110+ t/s generation and over 1,500 t/s prompt processing on 3x RTX 3090s. Setup is ExLlamaV3 with a 3.05 bpw EXL3 quant at full context and FP16 KV cache, with only the engram table offloaded to RAM. All three cards run at 250-265W and one is on a TB4 eGPU. User also ran Qwen3.8 27B (UD-Q8_K_L) on 2x 3090s in TP and a UD-Q4_K_XL of Qwen3.8 Flash Next that partially offloaded to RAM, reaching about 50 t/s max output, 38 t/s average, and prompt processing in the low hundreds.

Sep 20, 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