I measured Qwen3.6-35B-A3B at 4-bit (UD-IQ4_XS) hitting 89.6% pass@1 on HumanEval on a single RTX 2080 Ti 22GB — and then ran a controlled A/B of a routing technique I've been playing with: MoE expansion, which activates 20 experts per token instead of the stock 8 on the last 15 layers. Result: 90.9% (+2 problems) at −19% decode speed. (MoE expansion works!) Setup (both runs identical except routing): Unsloth UD-IQ4_XS dynamic 4-bit (4.25 bpw) — the whole model fits in VRAM, no offload KV cache q8_0, ctx 16384, flash-attn on OpenAI HumanEval, all 164 problems, original tests (not EvalPlus+), pass@1, temp 0, single sample Thinking budget 4096 tokens in both arms Code executed in a sandbox with the canonical check(candidate) tests, 12s timeout Results: Config pass@1 decode Stock routing (top-8) 89.63% (147/164) 69 tok/s MoE expansion (20 experts, adaptive, layers 25–39) 90.85% (149/164) 56 tok/s Paired per-problem: 139 solved by both, 10 solved only by expansion, 8 only by stock. Rolling pass rate stayed expansion-ahead by +2–3 problems at every checkpoint. What is MoE expansion? No retraining, no file changes — at inference time the router keeps more experts per token than the model's native top-K (here: 20 instead of 8, with an adaptive threshold so easy tokens keep fewer), on a slice of layers (25–39 of 40). You're consulting more of the network per token. Same trick that gave 84.34% vs 81.82% on GPQA-Diamond at Q8 in earlier benchmarks — now confirmed in coding too, at 4-bit. Honest caveats: +2 problems on 164 is within statistical noise (±3 pts CI). Read it as "equal or slightly better quality", not a proven gain It's original HumanEval tests, not HumanEval+/EvalPlus — don't compare 1:1 with the EvalPlus leaderboard pass@1 greedy n=1 — not the 20-sample protocol some leaderboards use Expansion costs ~19% decode speed on a fully-resident model (more experts = more FLOPs per token) The tool — I wrapped all of this into AgrillaMoE, a dedicated llama.cpp server for this model: it detects your VRAM and suggests/downloads the right Unsloth quant, applies the expansion profile by default (overridable), exposes OpenAI and Anthropic-compatible APIs (Claude Code works out of the box), and runs on NVIDIA from GTX 10xx to RTX 50xx, AMD via Vulkan, and Apple Silicon via Metal. Static binaries for Linux and Windows on the releases page. ref.: https://github.com/vagrillo/AgrillaMoE https://zenodo.org/records/22255483   submitted by   /u/Specific-Tax-6700 [link]   [comments]