NVIDIA Holoscan
NVIDIA Holoscan SDK NVIDIA Holoscan is a domain-agnostic, multimodal AI sensor processing platform that provides the accelerated, full-stack infrastructure needed for real-time processing of streaming data at the edge…
Purpose-built for AI infrastructure, NVIDIA BlueField DPUs combine high-performance networking, programmable compute, hardware acceleration, and advanced security capabilities into a single platform embedded into every AI factory compute node. Unlike traditional security approaches that rely on host system software, BlueField establishes a hardware-enforced, in-silicon, and workload-independent security layer. Operating within its own trusted execution domain, BlueField isolates infrastructure and security services from the host system. Monitoring, policy enforcement, and telemetry operate eve
Advancing AI Infrastructure for Agentic AI with NVIDIA DOCA In-Silicon Security | NVIDIA Technical BlogDOCA Flow is a foundational library within the DOCA software platform that enables developers and cybersecurity providers to create high-performance, hardware-accelerated packet processing pipelines on BlueField processors. Through a programmable API, developers can define packet processing “pipes” that execute directly in networking hardware, offloading networking and security operations from the host CPU while maintaining ultra-low latency and high throughput. By executing packet inspection, encryption, filtering, and policy enforcement directly in silicon, DOCA Flow enables network security
Advancing AI Infrastructure for Agentic AI with NVIDIA DOCA In-Silicon Security | NVIDIA Technical BlogNVIDIA Holoscan SDK NVIDIA Holoscan is a domain-agnostic, multimodal AI sensor processing platform that provides the accelerated, full-stack infrastructure needed for real-time processing of streaming data at the edge…
…Real-time AI at the edge Replacing an intent classification pipeline with a reasoning loop requires substantially more on-device compute. A production agentic AI assistant running on-device needs to: Run…
…of investment in Slurm job scripts, fair-share policies, and accounting workflows. The challenge is getting Slurm scheduling capabilities onto Kubernetes—the standard platform for managing GPU infrastructure at scale—without maintaining…
…policies are trained and validated against physically grounded environments. To make NVIDIA Omniverse easier to integrate into existing applications, NVIDIA is adding a modular, library‑based architecture alongside the existing platform. Core…
…Security (IPsec) and Platform Security Protocol (PSP) to secure GPU-to-GPU communications Data-at-rest encryption acceleration to secure storage platforms Secure boot, firmware authentication, and device attestation These features allow…
…Yet many platform teams running AI workloads on Kubernetes operate with... 6 MIN READ May 21, 2026 Unlock Exascale Performance on NVIDIA GB200 NVL72 with Slurm Topology-Aware Job Scheduling As AI…
…AIConfigurator currently ships with silicon-validated performance data for TensorRT LLM and SGLang across NVIDIA H100, H200, and B200 systems, with vLLM support on select platforms as well. WideEP inference for SGLang…
…Post-Training Quantization Using NVIDIA Model Optimizer Model quantization is an effective method to reduce VRAM usage and improve inference performance on consumer devices such as NVIDIA GeForce RTX GPUs. By... 8…