
Meta has released Muse Spark Open, an 816-billion parameter multimodal model, in collaboration with Apple to optimize performance on Mac hardware. The model is designed to run locally on dual M5 Ultra Mac Studios using Apple's MLX framework, enabling complex tasks like long-horizon reasoning and software engineering without cloud reliance. Meta CEO Mark Zuckerberg and Apple CEO John Ternus publicly endorsed the partnership, highlighting the efficiency of running such a large model on-device. This development marks a significant step in local AI capabilities for high-end consumer hardware.
Read originalEarlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
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Meta and Apple collaborate on running Muse AI on Macs
3 developments
© The Verge AIMeta’s Muse Charm isn’t a finished product; it’s an invitation to hack. A non-coder used Claude Code to transform a $50 Waveshare ESP32-S3 dev board into a fully functional AI companion, adding ElevenLabs voice synthesis, custom Tamagotchi-style minigames, and remote camera controls that Meta’s official firmware lacks. This reveals a critical truth: the value of Meta’s new hardware ecosystem lies not in its out-of-the-box utility, but in its open-source API allowing anyone to rewrite the experience. The gap between Meta’s polished demo and the actual user reality is bridged entirely by third-party AI coding tools.
© The Verge AIApple is finally unlocking its AI suite for Chinese users with the iOS 27.3 beta, marking a significant regulatory milestone after months of approval delays. The rollout relies on Alibaba’s Qwen model rather than OpenAI, integrating features like notification summaries and visual intelligence directly into the OS. By sidestepping the new Siri in favor of established text and image tools, Apple is prioritizing immediate utility over the full generative experience. This move effectively brings Apple’s AI capabilities to a massive new market, albeit with localized constraints that distinguish it from the global release.
© The Verge AISam Altman is pushing back against the local AI trend, arguing that hoarding DRAM in personal devices is a global waste during a severe supply crunch. He points out that consumer hardware sits idle most of the time, whereas cloud data centers achieve the high utilization rates needed to justify the billions invested by players like OpenAI and Stargate. This isn't just an opinion; it's a defense of the centralized infrastructure model against the growing open-source movement. The real-world impact is visible in the locked-up supply from Samsung and SK Hynix, which drives up costs for everyone else.
This release quietly cements llama.cpp as the universal inference runtime by finally supporting NVIDIA's latest CUDA 13 stack alongside AMD's ROCm 10.0. The inclusion of native builds for Linux arm64 with Snapdragon chips, covering CPU, Adreno GPU, and Hexagon NPU, signals a serious push into mobile edge deployment. While Apple Silicon KleidiAI is disabled in this specific build, the broader hardware coverage means developers no longer need to compile from source for most modern accelerators. It’s less about new features and more about removing friction for anyone running local models on bleeding-edge or diverse hardware.
© Matt WolfeGoogle launched Gemini 4 Carbon, a new model variant aimed at providing high performance at a lower cost for inference tasks.
© Lev SelectorGoogle's new model, Gemini 4 Carbon, has taken the top spot on the LMSYS Chatbot Arena leaderboard.