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Marin Launches Open Training of a 535B-A23B Model, Publicly Trackable Across 11 GB200 NVL72 Systems

Marin has begun training a mixture-of-experts (MoE) model with 535B total parameters and approximately 23B active parameters per token. The project plans to use 11 GB200 NVL72 systems to process roughly 18T to 18.75T tokens. Configurations, scaling experiments, and real-time telemetry are public, but model quality, the long-context strategy, and the final token budget have yet to be finalized.

推薦系統

NVIDIA Rewrites KV and Beam Search for Its Generative Recommendation Stack, Making 5K-Context Inference 2.27× Faster

NVIDIA has assembled HSTU, dynamic embeddings, and Semantic ID-based generative recommendation into an executable PyTorch reference stack. It rewrites the caching and decoding paths for recommendation workloads characterized by long histories, generation of only a few tokens, and extremely large beam widths, though the results are currently limited to NVIDIA hardware and synthetic configurations.