RotorQuant tops Qwen MLX memory benchmark
A detailed community benchmark evaluated the performance and memory footprint of different MLX quantizations—Vanilla, TurboQuant, and RotorQuant at 5-bit—for the Qwen3.6-35b model running locally on Apple Silicon. The results indicate that RotorQuant requires the least RAM (10.2 GB) and delivers the fastest peak generation, making it ideal for memory-constrained setups. Conversely, TurboQuant proved to be the most stable option, showing the least generation speed degradation over extended context windows.
This breakdown provides actionable insights for developers serving large local models on Macs, emphasizing that the "best" quantization depends heavily on whether one prioritizes peak speed, memory savings, or consistent output rates over long contexts.
- –RotorQuant reduces the 35B model's RAM usage by 8% compared to the baseline, making room for running auxiliary models simultaneously.
- –TurboQuant maintains the most stable generation speed, suffering only a 15.4% degradation from turn 1 to turn 7.
- –Using a 2B model for mundane tasks like context compression yields massive efficiency gains, prefilling 86% faster and finishing tasks nearly 4x faster than the 35B models.
DISCOVERED
45d ago
2026-04-22
PUBLISHED
45d ago
2026-04-22
RELEVANCE
AUTHOR
JLeonsarmiento