{"slug": "muse-spark-open-the-most-capable-us-open-weight-model-to-date", "title": "Muse Spark Open, the Most Capable US Open‑Weight Model to Date", "summary": "Meta released Muse Spark Open, an 816B-total-parameter, 42B-active open-weight multimodal mixture-of-experts model with a 1M-token context window, tuned for agentic coding, long-horizon tasks and multi-step reasoning. The weights are available day one across Groq, Fireworks, Together, vLLM, SGLang, TensorRT-LLM and llama.cpp, and the model can run locally on two Apple M5 Ultra Mac Studios via MLX. Meta says the 42B active-parameter count and on-prem viability give teams a path to lower per-token agent costs and to keep sensitive codebases off third-party endpoints.", "body_md": "[Hacker News](https://research.meta.ai/blog/muse-spark-open)\n\n### Muse Spark Open, the Most Capable US Open‑Weight Model to Date\n\nWhich summary reads better? Pick one — models revealed after.Both summaries are AI-generated.\n\nMeta released an 816B-parameter (42B active) open-weight multimodal MoE with a 1M-token context, tuned for agentic coding and long-horizon tasks, and it can run locally on two Apple M5 Ultra Mac Studios via MLX. It's live day-one across the major inference providers (Groq, Fireworks, Together, vLLM, SGLang, TensorRT-LLM, llama.cpp), so you can benchmark it against your current frontier API immediately—and the sparse active-param count plus on-prem viability means you now have a credible path to cut per-token agent costs and keep sensitive codebases off third-party endpoints.\n\nMeta released Muse Spark Open’s weights: an 816B-total-parameter, 42B-active natively multimodal model with a 1M-token context window aimed at coding, long-horizon agentic work, and multi-step reasoning. The practical shift is deployment flexibility: teams can run it via major API platforms, download weights from cloud/model hubs, or run it locally on two Apple M5 Ultra Mac Studios using MLX and other inference stacks, improving control over customization and sensitive workloads.\n\n### AI vs. AI Debate\n\n“The summary incorrectly groups inference engines like vLLM, SGLang, TensorRT-LLM, and llama.cpp as “inference providers” and overstates cost savings as a consequence not established in the excerpt.”\n\n“The grouping reflects practical deployment reality—these engines are how you access the model—and the sparse 42B-active-parameter count is a well-established, direct driver of lower per-token compute cost, not an unsupported leap.”", "url": "https://wpnews.pro/news/muse-spark-open-the-most-capable-us-open-weight-model-to-date", "canonical_source": "https://www.snipvote.com/story/cmvan8gtp000csdo6cxwdltdu", "published_at": "2026-10-16 07:40:49.401734+00:00", "updated_at": "2026-10-16 07:40:51.481623+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-infrastructure", "ai-products"], "entities": ["Meta", "Muse Spark Open", "Apple M5 Ultra Mac Studio", "MLX", "Groq", "Fireworks", "Together", "vLLM"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/muse-spark-open-the-most-capable-us-open-weight-model-to-date", "markdown": "https://wpnews.pro/news/muse-spark-open-the-most-capable-us-open-weight-model-to-date.md", "text": "https://wpnews.pro/news/muse-spark-open-the-most-capable-us-open-weight-model-to-date.txt", "jsonld": "https://wpnews.pro/news/muse-spark-open-the-most-capable-us-open-weight-model-to-date.jsonld"}}