#MoE

Qwen3.6-35B-A3B pairs Gated DeltaNet with MoE and raises the bar on agentic coding

Alibaba's Qwen team released Qwen3.6-35B-A3B as open weights. A 40-layer hybrid of Gated DeltaNet, Gated Attention, and MoE hits 73.4 on SWE-bench Verified, 37.0 on MCPMark, and 1397 on QwenWebBench.

Zhipu AI's GLM-5.1: An Agent Model That Doesn't Degrade After 600+ Iterations

Zhipu AI releases GLM-5.1, a 744B MoE (40B active) model achieving 58.4% SOTA on SWE-Bench Pro. Its standout feature is sustained performance across 8-hour sessions with 6,000+ tool calls—no degradation.

LLM-jp-4-32B-A3B on ROCm + Strix Halo: 41% Faster Than Qwen3.5

Benchmarking NII's LLM-jp-4-32B-A3B-thinking on EVO-X2 (Ryzen AI Max+ 395) with ROCm. 62.9 t/s vs Qwen3.5-35B-A3B's 44.7 t/s. Covers thinking control issues, KV cache trade-offs, knowledge cutoff, Japanese quality comparisons, code generation tests, and training data composition.

Google's Gemma 4 launches in four sizes (E2B–A4B), publishing Gemini 3–derived reasoning under Apache 2.0

Google DeepMind has released Gemma 4: four models—31B dense, 26B MoE (A4B), E4B, and E2B—with a 256K context, multimodal input, tool calling, and support for 140 languages.

SwiftLM is a Swift-based LLM inference server that integrates TurboQuant and SSD streaming into Metal shaders

SwiftLM, an Apple Silicon–only MLX inference server, provides a native Metal implementation of TurboQuant V2+V3 hybrid KV‑cache compression and NVMe SSD expert streaming.

Flash-MoE: Running a 397B-parameter model on a 48GB MacBook

Flash-MoE is a C/Metal inference engine that runs Qwen3.5-397B-A17B on a MacBook Pro M3 Max at 4.36 tokens/s. With expert streaming from SSD and hand-written Metal shaders, it fits the 209GB model into a 48GB memory budget.