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… The NVIDIA Virtual GPU software management SDK exposes valuable information that can aid IT across the lifecycle of a VDI deployment. NVIDIA taking this open platform approach to make it easier for every organization to tap into the power of the GPU to deliver a better user experience for all. …
… KAI Scheduler and NVIDIA Run:ai provide GPU-aware workload placement with fractional allocation and hierarchical quotas. …
… This includes GPU telemetry, kernel logs, hardware events, PCIe state, firmware information, and crash diagnostics. …
… Efficient DPU provisioning and lifecycle management DPF provides end-to-end support for BlueField DPU provisioning and lifecycle management, automating processes like firmware updates, flashing, and configuration to streamline setup and reduce downtime. …
… It handles up to 20x more interactive users by using the iGPU and dGPU concurrently, making it an excellent choice for high‑concurrency edge workloads. It also offers robust mixed‑criticality isolation, so workloads running on the dGPU operate independently without degrading performance on the iGPU. …
… Simulation history The history feature provides a detailed event log that tracks events through a simulation’s lifecycle. …
… Browse by Resource Type More Resources Accelerate Your Enterprise AI Lifecycle To execute high-throughput, agentic workflows at scale, master the frameworks powering next-generation financial infrastructure. …
… Automate GPU management with the NVIDIA GPU Operator The NVIDIA GPU Operator automates GPU driver installation, container runtime configuration, and device plugin deployment, giving worker pods automatic access to GPUs. The NVIDIA GPU Operator also deploys DCGM Exporter, a GPU telemetry collector. …
… GPU compute: Securing and accelerating Rubin GPUs GPU performance depends on the full system’s ability to keep accelerators supplied with work, data, and secure connectivity. BlueField-4 operates as an infrastructure processor in the GPU compute tray. …
… It expresses all roles in a single PodCliqueSet: apiVersion: grove.io/v1alpha1 kind: PodCliqueSet metadata: name: inference-disaggregated spec: replicas: 1 template: cliqueStartupType: CliqueStartupTypeExplicit terminationDelay: 30s cliques: - name: router spec: roleName: router replicas: 2 podSpec… …