Meta Superintelligence Labs released the weights for Muse Spark Open on October 15, lifting the lid on a flagship multimodal model that previously lived only behind clouds and APIs. It is a sparse Mixture-of-Experts: 816B total parameters, but only about 42B activated per token, so each inference step runs at a fraction of the compute a same-tier dense model would burn. The 1M-token context window can swallow a mid-sized code repository whole without losing the thread.
Rewritten for Apple Silicon
The headline is not the parameter count, it is that the model now runs on two Apple M5 Ultra Mac Studios. Meta rewrote the inference path using Apple's latest MLX framework, squeezing the unified memory architecture for everything it has. The M5 Ultra's integrated GPU can hold up to 512GB of unified memory with 1.2 TB/s of bandwidth. Pair two machines and you effectively have one supercomputer with a terabyte of unified memory, which is exactly what the full model needs. Meta tested a battery of purely on-device agent workflows with Muse Code powered by Muse Spark Open: shipping real iOS changes to the open-source Mastodon client Ice Cubes, from bug fixes to new features to a full build, and driving DaVinci Resolve end-to-end, with the model reviewing footage frame by frame to pick the strongest moments and crop them vertically. This is end-to-end deliverable engineering, not a toy demo.
Beyond the Mac: seven inference platforms at launch
The weights are not just for the Mac. Muse Spark Open simultaneously shipped on seven hosted inference platforms: Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI. Weights download through Amazon SageMaker AI, Google Cloud Model Garden and Hugging Face. Meta ran the model through the major inference engines — SGLang, vLLM, Apple MLX, llama.cpp, Unsloth — across AMD, Apple, Intel and NVIDIA silicon. On SGLang the custom auto-tunable Helion GPU kernels deliver 1.03-3.95x kernel-level speedups on NVIDIA and AMD, with a 3% end-to-end uplift on AMD MI350. TorchTitan is wired in for further fine-tuning.
Open, but commercially constrained
Weights ship under Meta's own Muse Model License with limited commercial terms — many developers can use it for free, but commercial use has gates. Meta emphasizes that the model received additional safety post-training and passed the Meta Superintelligence Scaling Framework release thresholds. The model card is already on Hugging Face.
My take
Meta putting 816B flagship open weights on a desktop is not really about the 816B number. It is about the gap between "flagship in the cloud" and "runnable locally" collapsing in one move. DeepSeek brought dense flagships down to consumer-grade GPUs; Meta is now slotting an 800B-class model into two Mac Studios with MoE, unified memory and MLX. The parameter race for open-source models is approaching diminishing returns. The real competition is shifting to: at the same scale, who runs cheapest, runs anywhere, and hosts the broadest ecosystem. Meta lining up every major inference engine and every hardware family in one launch is not charity — it is a land grab for "model portability." Going forward, developers will weight ecosystem completeness above single benchmark scores. The two questions worth tracking are whether the MoE-plus-unified-memory stack can keep being pushed down to consumer laptops, and whether Apple MLX becomes a standard distribution channel for open-weight models.