Sensitivity-guided mixed-precision quantization of Qwen/Qwen3.6-35B-A3B. Cerebellum measures which weight groups survive extreme compression and which don't, then writes a single GGUF with per-tensor precision assignments — a standard GGUF that runs on stock llama.cpp, no fork.

Coding — upstream EvalPlus (evalplus.codegen against llama-server, greedy / temp 0, n=164), same protocol across the size ladder:

Long-context: needle recall passes to 90K+ (verify-stress). Throughput: ~168 tok/s decode (3B-active MoE); fits 160K+ context at ~19 GB on a 24 GB card. Per-question artifacts in benchmark_results/14gb/.

v3 (11 GB) is the tightest-VRAM build. The 14 GB spends ~3 GB more to promote the routed ffn_down_exps to Q4_K — the group the ablation identifies as where the quality lives — and that gives it the family's best coding plus 160K+ context headroom. It posts above the 16 GB uniform Q3_K_M (−2 GB) and matches the 17.3 GB Base (−3.3 GB): the Base's extra promotions buy ~0 coding, so 14 GB is the efficient point. Pick v3 only when VRAM is tight or you need the vision projector.

Built with Cerebellum — sensitivity-guided mixed-precision quantization: crush each tensor group, measure the impact, allocate precision under a size budget, output a plain GGUF. imatrix-calibrated. Quantized by @deucebucket.

This line has a recorded data point in club-3090's BENCHMARKS (author-rig numbers from a full report.sh --full chain). The same report corrected their engine-support table for this model (issue #390, PR #393). Numbers there are author-reported, not club-validated.