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NIM

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Headlines · 3

  1. Nvidia case highlights Foxconn’s AI use in manufacturing

    TechNews, citing a customer case published by Nvidia, reports that Foxconn used AI to optimize injection-molding parameter settings and changes, reducing the related engineering workload by 50%. Automated production scheduling reduced the hours required for manual scheduling by 25%, while AI-assisted SMT analysis addressed more than 20 major production-line defects, with an anomaly improvement rate above 90%. Nvidia also reports that Foxconn uses DGX SuperPOD and DGX GB200 systems to develop its FoxBrain language model, while Nvidia deployment and simulation tools support the rollout of Foxconn-developed AI technologies across factories.

    科技新报 · 🔥 0
  2. NVIDIA Expands Cosmos World-Model Platform With Training Tools and Blueprints

    NVIDIA expanded its Cosmos platform with new training tools, simulation checkpoints and Cosmos Data Factory blueprints aimed at developers building world models for physical AI. The company said Data Factory now offers five cloud-native blueprints available through GitHub, Microsoft Azure, Nebius and CoreWeave, while new Cosmos 3 Nano variants support policy testing in repeatable multiview, multisensor simulations. NVIDIA also said updated Cosmos 3 NIM microservices deliver up to 2x faster inference in validated configurations, and it reported that the Cosmos platform ranks in the top five on six open-model leaderboards—a ranking not independently verified in the supplied material.

    NVIDIA Blog · 🔥 0
  3. NVIDIA tutorial shows RFD4-Proteina protein design with reward-guided search

    NVIDIA's technical blog published a tutorial on designing protein binders with RFD4-Proteina, an NVIDIA BioNeMo open model co-developed with the Institute for Protein Design at the University of Washington's School of Medicine. The model co-designs protein sequences and all-atom structures and uses reward-guided inference-time search to focus compute on the most promising candidates; the walkthrough targets PD-L1 binder candidates for computational biologists, ML engineers and drug discovery researchers. NVIDIA says users can take the open model for training, inference, method development and direct control over sampling code, or a NIM microservice with an HTTP interface, and that RFD4-Proteina is faster and uses less memory than RFD3, while the accompanying manuscript reports state-of-the-art results across a range of molecular tasks.

    NVIDIA Technical Blog · 🔥 0

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