MLX-compatible REAP for pruning MoE models on Apple Silicon
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Updated
Oct 14, 2026 - Python
MLX-compatible REAP for pruning MoE models on Apple Silicon
MoE expert compression toolkit — prune, merge, and forge specialist models for local inference.
RAZOR: Pruning Replaceable Experts in LLMs — training-free expert pruning for Mixture-of-Experts (MoE) models, with the MoECal calibration corpus
Experimental MoE expert pruning and router analysis for safetensors checkpoints. CPU probing, keep plans, dry runs, and checkpoint rewriting with synthetic tests.
Less is MoE (EMNLP 2026 Main); Fisher-MoE: intra-expert structured pruning and compression for Mixture-of-Experts (MoE) LLMs. Prunes FFN intermediate dimensions by Fisher importance instead of dropping whole experts. Official code.
Code release for DIET: DeletIon-response Expert Trimming for Video Diffusion Transformers
KL-divergence + blind task eval of GLM-4.7-Flash and its REAP-pruned variant vs gpt-oss-20b on llama.cpp
How I fit 35B-parameter MoE models into 16 GB of consumer VRAM: REAP expert pruning + NVFP4 quantization on an RTX 5070 Ti (SM120), served on vLLM, with measured evals.
Serving a large MoE coder model on consumer Blackwell GPUs via REAP expert pruning + NVFP4 quantization.
REAP pruning + NVFP4 quantization of Ornith-1.5-35B-A3B for 16GB VRAM
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