The NVIDIA Virtual GPU software management SDK provides an open platform for ISV’s and organizations deploying NVIDIA virtualization solutions. The SDK enables developers to build monitoring and management solutions that can provide data-driven insights into how their GPU-accelerated environment is performing. User experience is central to ensuring desktop virtualization success, and more organizations are looking for a lifecycle approach that extends from design to operations to support. The NVIDIA Virtual GPU software management SDK exposes valuable information that can aid IT across the lif
NVIDIA Virtual GPU software management SDKHeader files that define management interfaces Pre-compiled libraries implementing management interfaces Example source code API documentation Getting Started User Guide
NVIDIA Virtual GPU software management SDK…managing the service lifecycle programmatically on both Windows and Linux servers. This gives your application full control over when and how streaming runs. Server prerequisites NVIDIA RTX GPU (RTX 6000 Ada Generation…
…It streamlines the deployment of Nsight tools so you can easily profile and capture data from the CPU, GPU, network, storage, workload, API, and more. This helps you identify and fix performance…
…GPU-accelerated EDA and advance next-generation silicon development. Previously, Pawini was a product manager at Synopsys and an Engineering Manager at Intel, where she built deep expertise across the semiconductor lifecycle…
…This includes the NVIDIA AI Enterprise stack to manage GPUs using NVIDIA GPU Operator for lifecycle management, NVIDIA Network Operator for north-south and east-west networking, NVIDIA NIM Operator to download…
…be configured to ship GPU metrics to the existing stack instead of deploying a new Prometheus alongside it. This keeps metric retention, alerting rules, and data lifecycle management centralized. Custom resource allocation…
…signal “bring these blocks from storage to GPU ahead of the next request.” Combined with the retention APIs (below), this gives the harness full lifecycle control: pin blocks to prevent eviction, set…
…NVIDIA ModelExpress (MX) efficiently accelerates the model weight lifecycle by selecting the fastest available path for loading model weights, prioritizing direct GPU-to-GPU P2P RDMA transfers via NIXL, and reducing reliance…
…Native Kubernetes and GPU operator integration : Manage this stack using standard Kubernetes primitives and the NVIDIA GPU Operator, for a “lift-and-shift” deployment without rewriting the deployment manifests or the AI…
…Endpoints are also available as NVIDIA NIM™ microservices for easy deployment on any GPU-accelerated system. Nemotron reasoning models are optimized for various platforms: Nano provides cost efficiency with high accuracy specialized…
…Holoscan CLI with Bash execution permission HoloHub repository with documentation in a progressive disclosure pattern via the agents.md HoloHub development skills, including holohub-app-lifecycle and holohub-debug-build-run The…