A new edge-LLM sample has arrived. FINAL-Bench released the POCKET 35B series on Hugging Face, built on Darwin-36B-Opus (a Qwen3.5-family MoE architecture) and optimized across three layers: MoE-aware in-domain expert pruning cuts 256 experts down to 128, halving the model size; then a MoE-aware mixed-precision quantization is applied to the remaining experts (Korean is more sensitive to extreme quantization so it gets compressed harder, English is more robust to quantization so it gets pruned harder — two mirrored routes); and most importantly, the entire pipeline slots into upstream, unmodified llama.cpp and MLX with no forking of any inference runtime. The benchmark numbers are far more concrete than the marketing: on a Xeon 16-thread CPU, POCKET-35B IQ1_M hits 27.0 tok/s — 2.69x faster than the same-size 1-bit 27B Bonsai (10.1 tok/s); on H100 it's 197 tok/s vs 89 tok/s, a 2.22x speedup; on an Apple MacBook M3 Pro 18GB it runs at 25.4 tok/s on Metal and 13.8 tok/s on CPU, beating Bonsai on every axis. It also fits onto iPhone and 8GB Android: POCKET-EN on iPhone in mixed-precision GGUF is 5.3GB, and POCKET-KR in MLX 2-bit is 5.1GB, retaining 88% and 94% routing consistency respectively. Technically, POCKET carves out an engineering path of "small file + standardized runtime + decent quality" — not a single-point innovation, but a pipeline that strings together MoE sparsity × quantization × cross-lingual expert routing. The most counter-intuitive takeaway: CPU inference is memory-bandwidth bound, and a sparse MoE only reads 0.66 GB per token (vs. 3.5 GB for Bonsai), so the weaker the hardware the better MoE performs — which is why POCKET widens its lead most in no-GPU scenarios. The lesson for every local-LLM team: the weaker the hardware, the sweeter MoE gets.