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I would size a local AI system from its loaded memory, not the active-parameter headline. Sparse computation can reduce the work done for each token while leaving a very large library of weights to keep available. Understanding that difference is the first step toward deciding whether self-hosting is practical. Meta released Muse Spark Open on October 15, 2026: an open-weight, multimodal model with 816B total and 42B active parameters . This guide explains the memory and communication behind those numbers, then turns the published hardware results into a small validation plan rather than a shopping recommendation.