Qwen3.6-35B-A3B-OptiQ-4bit-REAP-19B

mlx-community/Qwen3.6-35B-A3B-OptiQ-4bit-REAP-19B#

Built with mlx-optiq, the MLX-native toolkit to quantize, prune, fine-tune, and serve LLMs locally on Apple Silicon. All OptiQ models · Docs

13.8 GB instead of 23.0 GB. 11.6 GB of memory to run instead of 24.5 GB. Capability Score 76.57 against the parent's 80.03.

50% of the routed experts are removed from mlx-community/Qwen3.6-35B-A3B-OptiQ-4bit; active parameters per token are unchanged, since top-8 routing is preserved and only the stored expert bank shrinks. That is why it gets smaller without getting slower.

Retained experts are copied bit-for-bit from the parent quant. Nothing is dequantized, re-quantized, merged, or retrained.

What pruning costs#

Details#

Use it#

pip install mlx-optiq
optiq serve --model mlx-community/Qwen3.6-35B-A3B-OptiQ-4bit-REAP-19B

from mlx_lm import load, generate
model, tok = load("mlx-community/Qwen3.6-35B-A3B-OptiQ-4bit-REAP-19B")
print(generate(model, tok, prompt="Hello", max_tokens=64))

Method#

Expert pruning follows REAP (Cerebras Research, ICLR 2026 — REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression). Experts are ranked by the conditional mean of router weight × expert output norm over calibration data; the lowest-ranked are removed and the router is sliced to match.

OptiQ applies it in the quantized domain — directly on a quantized checkpoint, with no BF16 parent and no dequantization of survivors — via optiq prune-experts. See the pruning docs.