mlx-dspark
An inference engine for running open-weight LLMs locally.
2 community reports
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 mlx-dspark
| GPU | VRAM | Reports | Median t/s, Muse 30B 8-bit |
|---|---|---|---|
| M4 Pro 48GBapple | 48GB | 1 | 8.2 |
| M5 Max 128GBapple | 128GB | 1 | no plain run of this model |
mlx-dspark 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 mlx-dspark and on another engine, so llamaperf can't say how it compares on speed. Add a run.
mlx-dspark results by GPU
Every card people have run mlx-dspark 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.
mlx-dspark on M4 Pro 48GB1 report
- Muse 30B · 8-bit8.2 t/s
mlx-dspark on M5 Max 128GB1 report
- Qwen3.8 27B · 8-bit39.9 t/s(speculative)
Top models on mlx-dspark
Frequently asked
Is mlx-dspark faster than other engines?
llamaperf has no matched comparison for mlx-dspark yet: no card has plain runs of the same model size at the same bit class on mlx-dspark and on another engine. Speed claims about engines need that pairing, so this page doesn't make one.