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.

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r/llamaperf on RedditNew reports as they land, and questions about what runs on what.llamaperf on DiscordChat live with people running local models, and ask what fits your card.

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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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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: M5 Pro 24GB

Compare setup details (1 active)

QuantizationEngineRecorded GPU countReported context (tokens)

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

Qwen3.8 27B

M5 Pro 24GB · Inco Splash · 120,000 ctx

generation:: 55.0 tokens/s
prompt processing (prefill):: 448.0 tokens/s
quant:: UD-IQ3_XXS (GGUF)

agentictool-use

User reports Qwen3.8-27B at about 55 t/s generation and 448 t/s prefill on an M5 Pro MacBook Pro with 24GB unified memory. Setup is the Inco Splash engine (v1.4.1) with the UD-IQ3_XXS GGUF (10.9GB), capped at 18GB memory for around 120k context. User's own KLD measurements give 89.5% top-1 agreement with mean KLD 0.0542, and they say Safari and Discord still run comfortably alongside it.

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Community benchmarks snapshot

Records by GPU

AMD Strix Halo 128GB328NVIDIA RTX 3090229NVIDIA RTX 5090175NVIDIA DGX Spark94NVIDIA RTX 5060 Ti 16GB72NVIDIA RTX Pro 6000 Blackwell71NVIDIA RTX 3060 12GB69AMD Radeon AI PRO R9700 32GB67M5 Max 48GB67NVIDIA RTX 409062

Records by model

Qwen3.81334

Qwen3.6291

Gemma 4168

DeepSeek V4 Flash149

Qwen3.5125

Qwen375

other657

Records by engine

llama.cpp1025

vLLM209

Strata169

MLX107

Ollama87

other444

Use cases

coding 769agentic 500long-context 361tool-use 221vision 146summarization 65math 59creative-writing 48multilingual 31rp 14text-generation 9reasoning 3

coding769agentic500long-context361tool-use221vision146summarization65math59creative-writing48

Median generation t/s by GPU

On Qwen3.8 27B at 4-bit, plain single-GPU runs. Full ranking

RTX 509080RTX Pro 600067RTX 5070 Ti55M5 Ultra 256GB50RX 7900 XTX43RTX 409040RTX 309037RX 7800 XT 16GB35RTX 5090 Laptop 24GB34M3 Ultra 512GB32

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 27B728Qwen3.8 125B · 6B active540Qwen3.6 35B · 3B active200DeepSeek V4 Flash 284B · 13B active132Qwen3.6 27B80Gemma 4 26B · 4B active59DeepSeek V4.1 Flash 552B · 16B active58GLM-5.3 320B · 18B active47

Quants

Q4_K_M351UD-Q4_K_XL164NVFP4151Q8_01104-bit95IQ4_XS77Q4_K_XL77MXFP467IQ3_S66IQ3_XXS66