NVIDIA IGX Thor is an enterprise-ready platform for physical AI. It offers server‑class AI performance together with industrial-grade hardware, advanced functional safety capabilities, extended lifecycle support, and an enterprise software stack in configurations suitable for industrial and medical environments. IGX Thor extends this compute and safety foundation to edge systems where uptime, reliability, and standards compliance are central to system design. With the IGX Thor platform, developers can build mission-critical edge computers that operate reliably in harsh physical conditions, int
NVIDIA IGX Thor Powers Industrial, Medical, and Robotics Edge AI Applications | NVIDIA Technical Blog…Developers can use various runtimes on a GPU or fall back to a CPU depending on available hardware for seamless integration of neural network features in real-time graphics workflows. This blog…
…Tech Mahindra, a leading global provider of technology consulting and digital solutions to enterprises across industries, and NVIDIA are collaborating to close this AI skills gap. They’re doing so by making…
…At scale, teams are juggling heterogeneous hardware, fast‑moving software stacks, tight power envelopes, and spiky, multitenant workloads. A single hotspot, misconfigured driver, or subtle hardware fault can ripple, causing throttled jobs…
…Inside a single checkpoint Hardware interruptions at 1000+ GPU scale aren’t rare. Meta reported 419 unexpected interruptions across 54 days of Llama 3 training on 16,384 NVIDIA H100 GPUs (~one…
…High-radix point-to-point links LPU Compiler-scheduled data movement Hardware-driven plesiosynchronous timing Together, these technologies enable Groq 3 LPU accelerators the flexibility to scale to thousands of chips while…
…A problem can span computation, communication, a specific rank, or underlying hardware. NVIDIA NCCL Inspector accelerates triaging by providing a lightweight and continuous report of NCCL communication performance. It tracks operation type…
…facilities and their hardware–software ecosystem. By testing these libraries first in high-performance internal stacks and industrial blueprints, we ensure they meet the rigorous demands of enterprise-scale physical AI before…
…This advance shifts inference performance limits from matrix math to non-linear SFU operations, making hardware-software co-design techniquesincluding LDTM.STAT offloading, CUDNN optimization, and NVFP4 KVCache managementcritical for maximizing attention…
…By using the latency constraint and peak requests per second, developers can calculate the required number of model instances and servers, and then build a TCO calculator to estimate hardware and software…
Agentic AI / Generative AI How NVIDIA Extreme Hardware-Software Co-Design Delivered a Large Inference Boost for Sarvam AI’s Sovereign Models Feb 18, 2026 By Utkarsh Uppal , Sriharsha Niverty , Diya Shah…