Meta rediscovers Apache 2.0 the same week Alibaba outgrows it

The big story

The week's two biggest releases moved in opposite directions on licensing, and that's the story. Meta's Muse Glimmer (30B dense, Apache 2.0) is the company's first open-weight model from Meta Superintelligence Labs and its first under a genuinely permissive license — no acceptable-use policy, no user-count clauses, none of the Llama-license residue. It arrived with a Zuckerberg manifesto (The Future Is For Everyone) attacking "closed" rivals and warning about concentrated control of AI. Read it as a policy document, not a philosophy paper: Meta needs the political credit of openness while Washington drafts rules.

Alibaba went the other way. Qwen3.8-2.4T-A95B — the text-only Max flagship — shipped under a custom Qwen3.8-Max License with a revenue-sharing requirement for large commercial deployers and cloud providers, abandoning the Apache 2.0 that built Qwen's claimed three-billion-download lead. The commodity tier — the excellent 27B — stays Apache 2.0. That's a platform play: give away what runs on your desk, charge rent on what runs in a datacenter. Both companies have concluded the same thing from different positions: weights are marketing, licenses are the product. Practitioners still came out ahead this week, but read the license file before you build a business on anything with "Max" in the name.

New open-weight releases

• Qwen3.8-27B (27B dense, Apache 2.0) — the community event of the week. 262K native context, prompt-steered reasoning effort, same architecture as the 3.6 series (all gains from training), and r/LocalLLaMA spent days one-shotting Mario clones and ray-tracers with it. Model card.
• Qwen3.8-2.4T-A95B (2.4T MoE, 95B active, Qwen3.8-Max License) — weights-available, not open: revenue-share terms for large-scale commercial serving. Text-only, no vision. Unsloth's 1-bit quant technically runs on a 5090 at 0.25 tok/s, which is archaeology, not inference.
• Muse Glimmer (30B dense incl. ~1.8B perception encoder, Apache 2.0) — built for always-on local agents: tool use, failure recovery, hours-long sessions. Fits a single 3090 at Q4 with full context, and early reports say it quantizes unusually well.
• DeepSeek-V4-Pro-0813 (1.57T MoE, 48B active, MIT) — 384K context, agentic focus, and it ships with no chat template at all — you're expected to use the new MIT-licensed DeepSeek Harness. MIT at 1.57T remains the frontier's cleanest licensing.
• MiniMax-Music3 — open-weight music generation, ComfyUI support on day one; check the card's license terms before commercial use.
• LTX-2.5 (Lightricks, open weights) — a world model for video, robotics and simulation; reportedly generates 10 seconds of video from an image in under 7 seconds on NVIDIA superchips.
• Nemotron 3.5 Lightning (30B MoE, A3B, NVIDIA) — shipped in BF16 and NVFP4 alongside the NeMo Switchyard open router; reports say a trillion-parameter Nemotron 4 is in development. Jensen Huang has fully internalized that free models sell chips.

Not actually released: Zhipu announced GLM-5.3 (reportedly 743B) claiming the strongest open-weights coding model — a 50% jump over 5.2 from post-training alone — but the weights are promised "in two weeks." It's a benchmark claim until there's a checksum.

Policy & politics

The White House is expanding its AI framework to cover open models, with follow-up reporting suggesting safety reviews could extend to open releases. Combine that with the still-pending question of a Chinese model ban — against which Nvidia and two dozen companies signed an open-weights letter last month, with OpenAI, Anthropic and Google conspicuously absent — and the US open ecosystem faces pressure from both directions: review gates on domestic releases, import restrictions on the Chinese models that currently dominate local inference. Meanwhile in Brussels, six major labs signed the EU Code of Practice on transparency of AI-generated content, which means watermarking obligations will flow downstream into those companies' open models too. The pattern across all three: policy is being written at the release boundary, which is exactly where open weights live.

Ecosystem

Speculative decoding had its coming-of-age week. DeepSeek's DSpark drafters are now everywhere: mlx-dspark makes Qwen3.8-27B up to 3× faster on Apple Silicon, and SGLang's day-0 support combines NVFP4 with DSpark for 200+ tok/s on a 5090 and 38 tok/s on a DGX Spark. Elsewhere: antirez published h3.c, native MiniMax-H3 inference for Apple Silicon in dependency-free C; Tim Dettmers is teasing a new quantization scheme that ran GLM-5.3 on a single DGX Spark at 7 tok/s (grain of salt until code lands); Qwen shipped 3.8 with a broken Jinja chat template again, and the community fixed it within a day. And the Stealing Reasoning Traces paper showed that "encrypted" chain-of-thought from Anthropic, OpenAI and Google APIs can be recovered — recovered traces are distillation fuel, so expect this to surface in the next licensing fight.

From the index

The llama.cpp PR adding Kimi-K3 text support means demand for /torrent/kimi-k3/ (1454 GB) is about to spike — if you have the disk, seed it. /torrent/glm-5-2/ stays relevant: GLM-5.3 is a post-train of that MIT lineage, and we'll list it the day weights actually appear. Qwen3.8-2.4T-A95B is the week's archival priority — revenue-share licenses have a way of getting stricter, and mirroring weights before terms change is the whole point of this site. Someone on r/LocalLLaMA asked this week why there's no P2P for model transfers. There is. You're soaking in it.