❌

Normal view

Advancing AI Infrastructure for Agentic AI with NVIDIA DOCA In-Silicon Security

1 June 2026 at 04:21
The AI era is driving a new class of infrastructure: AI factories that transform data into intelligence for autonomous AI agents operating at unprecedented...

The AI era is driving a new class of infrastructure: AI factories that transform data into intelligence for autonomous AI agents operating at unprecedented scale. Powered by accelerated computing, AI factories enable enterprises to train, fine-tune, and deploy AI with greater speed and efficiency. This new class of infrastructure also introduces a fundamentally new attack surface spanning…

Source

How the NVIDIA Vera Rubin Platform is Solving Agentic AI’s Scale-Up Problem

14 May 2026 at 19:24
Agentic inference has fundamentally changed the runtime dynamics of inference workloads by introducing non-deterministic trajectoriesβ€”actions, observations,...

Agentic inference has fundamentally changed the runtime dynamics of inference workloads by introducing non-deterministic trajectoriesβ€”actions, observations, and decisions that an AI agent produces while working through a task. These trajectories compound end-to-end latency across hundreds of inference requests per session. NVIDIA Vera Rubin NVL72 handles the bulk of that inference load as…

Source

Accelerated X-Ray Analysis for Nanoscale Imaging (XANI) of Novel Materials

13 May 2026 at 16:39
A massive-scale X-ray free-electron laser (XFEL) enables tracking structural and electron dynamics in novel systems, including fusion materials, semiconductors,...

A massive-scale X-ray free-electron laser (XFEL) enables tracking structural and electron dynamics in novel systems, including fusion materials, semiconductors, batteries, and catalysis. It produces ultrashort X-ray pulses that can record the movements of atoms and electrons. These instruments can detect the smallest change in material structure caused by defects and other influences.

Source

Real-Time Performance Monitoring and Faster Debugging with NCCL Inspector and Prometheus

7 May 2026 at 16:02
Decorative image.Distributed deep learning depends on fast, reliable GPU-to-GPU communication using the NVIDIA Collective Communication Library (NCCL). When training slows down,...Decorative image.

Distributed deep learning depends on fast, reliable GPU-to-GPU communication using the NVIDIA Collective Communication Library (NCCL). When training slows down, it becomes challenging to determine why and what to do next. A problem can span computation, communication, a specific rank, or underlying hardware. NVIDIA NCCL Inspector accelerates triaging by providing a lightweight and continuous…

Source

❌