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 A2000 12GB
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:
51.9 tokens/s
prompt processing (prefill):
1831.8 tokens/s
quant:
Q4_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.

User reports 51.92 t/s generation and 1831.78 t/s prompt processing. Source evidence (verbatim): Source: https://blog.majid.info/llama-bench/zanzibar.txt # NVIDIA RTX A2000 12GB: original Gemma4 E4B Q4_K_M llama-bench log First-hand author log linked from https://blog.majid.info/llama-bench/ (April 13, 2026 article). Measured hardware: NVIDIA RTX A2000 12GB. Gemma4 E4B Q4_K_M GGUF, llama.cpp 82764d8f4 (8770), backend CUDA. pp512 1831.78 ±13.79 tokens/s; tg128 51.92 ±0.39 tokens/s. One device, ngl99; 11906 MiB visible VRAM, not resident use. CPU and system RAM unknown. Command names --hf-repo unsloth/gemma-4-E4B-it-GGUF; exact tested quantization repository https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF ; tested revision unknown. Log reports 4.62 GiB model size and 7.52B counted parameters; size is not resident memory. Context depth, KV quantization, measured power, driver version and explicit speculation/concurrency declarations unreported. Raw source: https://blog.majid.info/llama-bench/zanzibar.txt .

Oct 14, 2026
NVIDIA hardware
generation:
5.5 tokens/s
prompt processing (prefill):
118.0 tokens/s
quant:
UD-Q4_K_XL (GGUF)
kv:
q8_0

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.

visionlong-contextsummarization

User reports Qwen3.8 Flash-Next at 5.5 t/s generation and 118 t/s prompt eval at 131,072 context on an RTX A2000 12GB with a Xeon Silver 4314 and 128 GB DDR4. Setup is llama.cpp master with the unsloth UD-Q4_K_XL GGUF (103.7 GiB), q8_0 KV cache, --n-cpu-moe 99 and -ub 1536, all routed experts in RAM, 11.6 GB VRAM peak. Generation falls from about 15.4 t/s empty to 8.8 t/s at 58k and 5.5 t/s at 118k. A 3000x2000 image request took 46 s. The same box also ran Qwen3.6-35B-A3B at 26.1 t/s and DeepSeek-V4-Flash at 7.5 t/s at 118k deep.

Sep 18, 2026
NVIDIA hardware
generation:
26.1 tokens/s
prompt processing (prefill):
233.0 tokens/s
quant:
UD-Q5_K_M (GGUF)
kv:
q8_0
mtp (multi-token prediction):
on

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.

visionlong-context

User reports Qwen3.6-35B-A3B at 26.1 t/s generation and 233 t/s prompt eval at 131,072 context on an RTX A2000 12GB with a Xeon Silver 4314 and 128 GB DDR4. Setup is llama.cpp master with the unsloth UD-Q5_K_M GGUF, q8_0 KV cache, --n-cpu-moe 32, MTP, and mmap on. Empty-context generation was 35-38 t/s; the 118k-deep figure was measured at --n-cpu-moe 31. A 3000x2000 image request took 32 s, and the user notes that --n-cpu-moe 31 crashed on that image while 32 passed.

Sep 18, 2026
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Community benchmarks snapshot

Records by GPU

AMD Strix Halo 128GB299NVIDIA RTX 3090193NVIDIA RTX 5090138NVIDIA DGX Spark79M5 Max 48GB66NVIDIA RTX Pro 6000 Blackwell63NVIDIA RTX 5060 Ti 16GB60NVIDIA RTX 3060 12GB55AMD Radeon AI PRO R9700 32GB53M4 Max 128GB49

Records by model

2290 total
Qwen3.81060
Qwen3.6240
Gemma 4154
DeepSeek V4 Flash147
Qwen3.5101
Qwen368
other520

Records by engine

1703 total
llama.cpp902
vLLM189
MLX102
Strata85
Ollama65
other360

Use cases

coding 578agentic 370long-context 281tool-use 157vision 113summarization 54math 43creative-writing 39multilingual 25text-generation 9rp 8reasoning 3
coding578agentic370long-context281tool-use157vision113summarization54math43creative-writing39

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 309036RX 7800 XT 16GB35RTX 5090 Laptop 24GB34RTX 409032V100 32GB31Radeon AI PRO R9700 32GB30

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 27B581Qwen3.8 125B · 6B active395Qwen3.6 35B · 3B active131DeepSeek V4 Flash 284B · 13B active130Qwen3.6 27B72Gemma 4 26B · 4B active37GLM-5.3 320B · 18B active35DeepSeek V4.1 Flash 552B · 16B active29

Quants

Q4_K_M302UD-Q4_K_XL144NVFP4123Q8_0994-bit83IQ4_XS71Q4_K_XL71MXFP459IQ3_XXS57Q451