The community interest came from a practical question: can a huge MoE model be useful on ordinary hardware? Colibri uses GLM-5.2’s sparse activation pattern to avoid loading the whole model into RAM or a GPU at once.
The 1.6T-total, 48B-active MoE numbers are only half the story. HN discussion treated domestic AI ASIC training as the bigger signal.
The thread focused on whether a 6B-active MoE can sit near the edge of practical local use.
The draw for LocalLLaMA was not just another coding model, but Cohere asking the local-inference crowd to test pre-release weights first.
Liquid AI's new LFM2.5 8B-A1B MoE model delivers 253 tokens/s on M5 Max, runs under 6GB memory on mobile, and achieves 18,500 output tokens/s on H100—all while outperforming similarly-sized dense models on key benchmarks.
LocalLLaMA lit up because Xiaomi MiMo dropped an MIT-licensed MoE with 1.02T total parameters, 42B active parameters, and a 1M-token context window. The excitement was real, but so was the hardware reality check: people loved the openness and agentic claims while joking about how many serious GPUs you still need.
LocalLLaMA did not just celebrate the DeepSeek V4 release. The thread instantly turned into a collective calculation about 1M context, activated parameters, and what this actually means for real hardware, with MIT license praise mixed in.
HN did not latch onto DeepSeek V4 because of a polished launch page. The thread took off when commenters realized the front-page link was just updated docs while the weights and base models were already live for inspection.
LocalLLaMA upvoted this because it felt like real plumbing, not another benchmark screenshot. The excitement was about DeepSeek open-sourcing faster expert-parallel communication and reusable GPU kernels.
Why it matters: Alibaba is putting a small-active-parameter multimodal coding model into open weights rather than keeping it API-only. The tweet says Qwen3.6-35B-A3B has 35B total parameters, 3B active parameters, and an Apache 2.0 license; the blog reports 73.4 on SWE-bench Verified and 51.5 on Terminal-Bench 2.0.
HN latched onto the open-weight angle: a 35B MoE model with only 3B active parameters is interesting if it can actually carry coding-agent work. Qwen says Qwen3.6-35B-A3B improves sharply over Qwen3.5-35B-A3B, while commenters immediately moved to GGUF builds, Mac memory limits, and whether open-model-only benchmark tables are enough context.
LocalLLaMA reacted because the post attacks a very real pain point for running large MoE models on limited VRAM. The author tested a llama.cpp fork that tracks recently routed experts and keeps the hot ones in VRAM for Qwen3.5-122B-A10B, reporting 26.8% faster token generation than layer-based offload at a similar 22GB VRAM budget.