vllm-mlx
An inference engine for running open-weight LLMs locally.
1 community report
This engine doesn't yet have an editorial profile on llamaperf. The community reports below show how it's been used in practice across different hardware.
Top GPUs running vllm-mlx
| GPU | VRAM | Reports | Median t/s, Qwen3 30B · 3B active 4-bit |
|---|---|---|---|
| M4 Max 128GBapple | 128GB | 1 | 127.7 |
vllm-mlx against other engines
Pairs of plain runs on the same card, of the same model size at the same bit class: one device, one request, no speculative decoding, the whole model in memory. Context length and build still differ between the two sides, and each side shows its own.
No matched pair yet. No card has plain runs of one model size at one bit class on vllm-mlx and on another engine, so llamaperf can't say how it compares on speed. Add a run.
vllm-mlx results by GPU
Every card people have run vllm-mlx on, with each report's model, quant and speed, newest first. Runs on several cards, with speculative decoding, with batched requests or with part of the model in system RAM say so, since each describes a different setup.
vllm-mlx on M4 Max 128GB1 report
- Qwen3 30B · 3B active · 4-bit127.7 t/s
Top models on vllm-mlx
Frequently asked
Is vllm-mlx faster than other engines?
llamaperf has no matched comparison for vllm-mlx yet: no card has plain runs of the same model size at the same bit class on vllm-mlx and on another engine. Speed claims about engines need that pairing, so this page doesn't make one.