
NVIDIA and Google Cloud are expanding their joint developer community, which now includes over 100,000 developers, with new resources and learning paths. These additions, announced at Google I/O, include a JAX library learning path for NVIDIA GPUs and a NVIDIA Dynamo codelab focused on inference optimizations. The collaboration aims to enhance AI development using NVIDIA's AI platform on Google Cloud, enabling developers to build production-ready applications. This partnership highlights the companies' commitment to advancing AI technology and supporting developers with the necessary tools and infrastructure.
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NVIDIA and Google Cloud Boost AI Developer Community
2 developments
This release quietly cements llama.cpp as the universal inference runtime by finally supporting NVIDIA's latest CUDA 13 stack alongside AMD's ROCm 10.0. The inclusion of native builds for Linux arm64 with Snapdragon chips, covering CPU, Adreno GPU, and Hexagon NPU, signals a serious push into mobile edge deployment. While Apple Silicon KleidiAI is disabled in this specific build, the broader hardware coverage means developers no longer need to compile from source for most modern accelerators. It’s less about new features and more about removing friction for anyone running local models on bleeding-edge or diverse hardware.
© The Verge AIApple is finally unlocking its AI suite for Chinese users with the iOS 27.3 beta, marking a significant regulatory milestone after months of approval delays. The rollout relies on Alibaba’s Qwen model rather than OpenAI, integrating features like notification summaries and visual intelligence directly into the OS. By sidestepping the new Siri in favor of established text and image tools, Apple is prioritizing immediate utility over the full generative experience. This move effectively brings Apple’s AI capabilities to a massive new market, albeit with localized constraints that distinguish it from the global release.
© Matt WolfeGoogle launched Gemini 4 Carbon, a new model variant aimed at providing high performance at a lower cost for inference tasks.