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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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 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 125B (6B active) Flash-Next

M5 24GB · Inco Splash · 256,000 ctx

generation:: 49.0 tokens/s
quant:: Q2_0

codingtool-useagenticlong-context

User reports Qwen3.8-Flash-Next at 49 t/s decode on an M5 24GB MacBook Air. Setup is Splash 1.4.0 with a small fork, ISTA GSQ-RCO Q2_0 weights and a Flash-Next-DFlash2 draft model at 256K context. About 28% of expert slots (6,800 of 24,576, ~8.8 GB) stay resident and the rest are streamed from the SSD. Cold prefill without prompt cache is 522 t/s on a 32,620-token prompt and 485 t/s on 63,201 tokens. Real agent use in pi averages ~45 t/s with no prompt-cache losses, and draft acceptance is ~.70 for code and ~.80 for tool calls.

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Community benchmarks snapshot

Records by GPU

AMD Strix Halo 128GB313NVIDIA RTX 3090217NVIDIA RTX 5090161NVIDIA DGX Spark87NVIDIA RTX Pro 6000 Blackwell70NVIDIA RTX 5060 Ti 16GB68M5 Max 48GB67NVIDIA RTX 3060 12GB64AMD Radeon AI PRO R9700 32GB61NVIDIA RTX 409056

Records by model

Qwen3.81221

Qwen3.6275

Gemma 4165

DeepSeek V4 Flash149

Qwen3.5117

Qwen371

other598

Records by engine

llama.cpp988

vLLM202

Strata135

MLX106

Ollama79

other408

Use cases

coding 691agentic 438long-context 326tool-use 191vision 127summarization 61math 51creative-writing 43multilingual 26rp 12text-generation 9reasoning 3

coding691agentic438long-context326tool-use191vision127summarization61math51creative-writing43

Median generation t/s by GPU

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

RTX 509073RTX Pro 600067M5 Ultra 256GB50RX 7900 XTX43RTX 409040RTX 309038RX 7800 XT 16GB35RTX 5090 Laptop 24GB34M3 Ultra 512GB32V100 32GB31

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 27B686Qwen3.8 125B · 6B active474Qwen3.6 35B · 3B active185DeepSeek V4 Flash 284B · 13B active132Qwen3.6 27B79Gemma 4 26B · 4B active59GLM-5.3 320B · 18B active43DeepSeek V4.1 Flash 552B · 16B active42

Quants

Q4_K_M333UD-Q4_K_XL153NVFP4145Q8_01054-bit87IQ4_XS75Q4_K_XL74MXFP465IQ3_XXS61IQ3_S55