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Industry tag545 stories

  1. IndustryReach 31

    Nvidia may use AI to boost GPU performance up to 30%

    Nvidia is reportedly planning to optimize its GeForce graphics drivers using AI to squeeze out better gaming performance. Leaker CapFrameX claims the updates will tune instructions and hardware utilization, delivering an average 10% uplift and up to 30% in select titles. Why it matters: If true, AI-driven driver tuning could give Nvidia GPUs lasting performance gains over time without requiring new silicon.

  2. IndustryReach 44

    AMD brings dedicated on-chip AI to x86 laptops

    AMD just unveiled its Ryzen 7040 series mobile processors, marking the first time an x86 chip features a dedicated on-die AI engine called Ryzen AI. Built on the XDNA adaptive architecture, AMD claims it outperforms Apple's M2 in both AI speed and battery efficiency. The first laptops powered by the chip will ship this March.

  3. AI toolsReach 36

    Nvidia's AI upscaling comes to web video

    Nvidia just unveiled RTX Video Super Resolution, bringing AI video upscaling to web browsers starting in February. Users with RTX 30 and 40 series GPUs will be able to upscale 1080p web videos to 4K inside Chrome and Edge. Why it matters: It sharpens blurry online streams into crisp 4K footage, though RTX 20 series cards and Firefox are left out for now.

  4. AI toolsReach 33

    Nvidia adds generative AI and Unity to Omniverse

    Nvidia just updated its Omniverse simulation platform, adding generative AI capabilities and a new connector for Unity's engine. Powered by the Ada Lovelace architecture and DLSS 3, the release boosts performance by up to 3x and rolls out DeepSearch for natural language 3D asset discovery. Why it matters: It bridges disparate 3D ecosystems, making industrial digital twins far easier to build.

  5. ResearchReach 31

    Dendrocentric AI cuts power from megawatts to watts

    Stanford researchers just designed a computational model that emulates neural dendrites instead of synapses to dramatically slash AI power draw. By responding only to signals in precise sequences, the ferroelectric transistor approach encodes richer data with minimal juice, potentially letting advanced neural networks run directly on smartphone-level wattage.