Meta open-sourced Muse Glimmer, a 30-billion-parameter model designed to run on consumer GPUs and laptops for local AI agent tasks like scheduling, coding, and tool use. The model uses quantization and speculative decoding to fit within 20-32GB of memory while maintaining speed for real-time interaction. It's available now under Apache 2.0 license with integrations for llama.cpp, MLX, and other frameworks.

• Meta open-sourced Muse Glimmer, a 30B-parameter agentic model quantized to under 20GB so it runs locally on consumer GPUs/laptops (24-32GB) while competing with Gemma 4-31B and Qwen 3.6-27B.
• Speculative decoding with a lightweight drafter model proposes token blocks at once instead of one token at a time, speeding up reasoning and tool calls without changing outputs.
• Training combined logit distillation from a larger teacher model, agent-heavy mid-training data, and post-training RL/distillation across reasoning, coding, and agentic tasks.
• Released under Apache 2.0 with upcoming llama.cpp, MLX, and ExecuTorch integrations and support from Ollama, LM Studio, Together AI, and chipmakers like NVIDIA, Intel, AMD, and Arm.

Reflection AI will pay $150 million per month from July 2026 through 2029 for Nvidia GB300 chips and hardware at SpaceX’s Colossus 2 data center in Tennessee, in a contract worth up to $6.3 billion. The open-source-focused startup calls this its first major compute deal and one of the largest infrastructure commitments in the open AI space.

• Reflection AI committed to $150M/month from July 2026-2029 (up to $6.3B total) for Nvidia GB300 chips at SpaceX's Colossus 2 data center, with 90-day cancellation option after the first quarter
• This dwarfs in comparison to Anthropic ($1.25B/month) and Google ($920M/month) deals for the same facility, but is still notable as a small startup's first major compute deal
• Colossus began as xAI's private training hub before SpaceX absorbed it and opened capacity to outside labs once xAI's plans stalled
• Reflection positions itself as an open-weight lab, arguing this reduces vendor lock-in and geopolitical risk, gaining relevance after the U.S. blocked Anthropic's closed models Fable and Mythos