Pruning Qwen to 288 experts enables 180B model on Mac with 39GB RAM
EyalToledano · x · 2026-08-28
The author experimented with structural pruning on Qwen3.8-Flash-Next (180B MoE). By analyzing expert activity in real coding sessions, experts per layer were reduced from 512 to 288. Combined with MLX 4-bit quantization, this maintains 91.5% HumanEval accuracy while dropping memory requirements to 39GB (via streaming n-gram table), enabling the model to run on Apple Silicon.
Related event: Developer Prunes 180B-class Qwen MoE to Fit in 48GB of Mac Memory(9 posts)→
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