Find which open-weight LLMs fit in your GPU or Mac and compare the speeds people report on setups like yours.
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.
Search for a model to see its reported speeds across GPUs and Macs.
These are individual setups, not a controlled benchmark. Compare GPU count, quantization, context and offloading before comparing speeds. How to read a report →
Exact recorded values. Context may be a configured limit; matching filters does not establish identical prompts, offloading or concurrency.
M5 Max 128GB · mlx-serve
User reports Qwen3.8-Flash-Next at 1900 t/s prefill and 90 t/s generation on an M5 Max 128GB. Setup is mlx-serve with Sushi-4 quant, an MLX-EXL3 hybrid, with MTP and Vision supported. User also cites M1 Max 64GB at ~350 t/s prefill and 30 t/s gen, and M5 Pro at ~900 t/s prefill and 60 t/s gen.
M5 Max 128GB · mlx-serve · 1,048,576 ctx
User reports about 40 t/s on prose and 75 t/s on coding at roughly 760k context. Setup uses 8-bit dense layers and 4-bit expert layers, with peak memory around 117 GB and a requirement of iogpu.wired_limit_mb=120000. Model weights are at ddalcu/Qwen3.8-Flash-Next-MLX-Serve-mixed-4-8bit.
On Qwen3.8 27B at 4-bit, plain single-GPU runs. Full ranking