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  3. Rohan PaulSelected

    Meta open-sources 816B-parameter model Muse Spark Open, adapted for Apple MLX to run on Mac

    Meta has open-sourced the 816B-parameter Muse Spark Open on Hugging Face, sharing the same base model as Muse Spark 1.3 with added safety post-training. The model has 256 expert networks per layer, routing each token to 8 of them, so deployment requires 816B-parameter-level VRAM but runs at the speed of a 42B model; the 4-bit NVFP4 version requires at least 700GB of GPU VRAM, and Apple has adapted it for its MLX framework, with two M5 Ultra Mac Studios each providing up to 512GB of unified memory and 1.2TB/s bandwidth to carry it. It tops the MCP-Atlas tool-calling test at 89.2, and the license allows free commercial use but requires attribution as Built with Muse Spark Open; API model sellers with over $20 million in revenue over 12 months must sign a separate agreement with Meta.

    Meta has open-sourced the 816B-parameter Muse Spark Open on Hugging Face, sharing the same base model as Muse Spark 1.3 with added safety post-training. The model has 256 expert networks per layer, routing each token to 8 of them, so deployment requires 816B-parameter-level VRAM but runs at the speed of a 42B model; the 4-bit NVFP4 version requires at least 700GB of GPU VRAM, and Apple has adapted it for its MLX framework, with two M5 Ultra Mac Studios each providing up to 512GB of unified memor

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    #ai-news#Model releases#Open-source ecosystem#English text is being prepared.
  7. Unite.AISelected

    Meta open-sources 816B-parameter multimodal model Muse Spark Open

    On October 15, Meta released the weights of Muse Spark Open, a natively multimodal open-source model with 816B total parameters, 42B activated parameters, and a 1M token context window, targeting coding and long-horizon agent tasks. The model uses a MoE Transformer plus Perception Encoder architecture, can be invoked through API platforms such as Baseten, Fireworks AI, and Groq, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and elsewhere; running the NVFP4 version locally requires at least 700GB of VRAM. Meta claims it is the first model of its class that can run on desktop, carried by two Mac Studios equipped with M5 Ultra paired with the MLX framework; the model card shows its Artificial Analysis Intelligence Index at 39, lower than GLM 5.3's 45 and Kimi K3's 44 in the table.

    The model uses a MoE Transformer plus Perception Encoder architecture, can be invoked through API platforms such as Baseten, Fireworks AI, and Groq, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and elsewhere; running the NVFP4 version locally requires at least 700GB of VRAM. Meta claims it is the first model of its class that can run on desktop, carried by two Mac Studios equipped with M5 Ultra paired with the MLX framework; the model card shows it

    Matched fieldsSummary、Body

    #hot-signal#Model releases#Multimodal#Open-source ecosystem
  10. The New StackSelected

    Meta releases 816B open-source model Muse Spark Open

    Meta releases Muse Spark Open, an open-weight model with 816B parameters and 42B activated parameters, using a 1 million token context window, callable via APIs such as Baseten, Fireworks AI, Groq, and OpenRouter, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and others. Meta says it targets coding and agent tasks and leads Kimi K3, GLM 5.3, and Qwen3.8 on agent and tool-calling benchmarks such as JobBench and MCP-Atlas, but trails on coding benchmarks such as Terminal-Bench 2.1 and DeepSWE 1.1. The model uses the Muse Model License; companies with annual revenue over $20 million must first notify Meta and sign a separate agreement to offer model services, and users must prominently label Built with Muse Spark Open in product interfaces.

    ameters and 42B activated parameters, using a 1 million token context window, callable via APIs such as Baseten, Fireworks AI, Groq, and OpenRouter, with weights downloadable from Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden, and others. Meta says it targets coding and agent tasks and leads Kimi K3, GLM 5.3, and Qwen3.8 on agent and tool-calling benchmarks such as JobBench and MCP-Atlas, but trails on coding benchmarks such as Terminal-Bench 2.1 and DeepSWE 1.1. The model uses th

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    #hot-signal#Model releases#Open-source ecosystem#Reasoning
  14. 36Kr · AISelected

    StepFun open-sources Step 5 Preview, a 600B-parameter MoE model that ranks second on the OpenRouter weekly chart

    StepFun officially releases the Step 5 Preview model weights, with BF16 and FP8 versions launched simultaneously on Hugging Face and ModelScope. The model uses a sparse MoE architecture, with 600B total parameters and 27B activated parameters, supports a 1 million Token context window, and scores 44 on Artificial Analysis's Intelligence Index, placing it in the global top three open-source models. As of October 15, 2026, its Token usage ranks second on both the OpenRouter weekly and daily charts, with about 18.8 trillion Tokens used in the past seven days; according to evaluation data at release, its average cost per task is $0.71, about 1/8 that of Claude Opus 5.

    StepFun officially releases the Step 5 Preview model weights, with BF16 and FP8 versions launched simultaneously on Hugging Face and ModelScope. The model uses a sparse MoE architecture, with 600B total parameters and 27B activated parameters, supports a 1 million Token context window, and scores 44 on Artificial Analysis's Intelligence Index, placing it in the global top three open-source models. As of October 15, 2026, its Token usage ranks second on both the OpenRouter weekly and daily charts

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    #hot-signal#Model releases#Open-source ecosystem#Hugging Face

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