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 SDKWith JetPack 7.2, Jetson is NemoClaw-ready out of the box. JetPack 7.2 comes preconfigured with the required dependencies and software stack, so you can deploy and run NemoClaw-based workflows on Jetson without manual environment setup. This enables you to easily build agentic physical AI applications across robotics, industrial automation, vision agents, and edge AI systems. To install NemoClaw on a Jetson device running JetPack 7.2, run the following single command: curl -fsSL nvidia.com/nemoclaw.sh | bash
Deploy Agentic-Ready AI at the Edge with Memory Efficiency in NVIDIA JetPack 7.2 | NVIDIA Technical Blog…The core lineup These libraries are designed to solve the specific integration pain points of industrial software development: These libraries plug into existing Omniverse components for scene description ( OpenUSD ), simulation‑ready assets…
…For use on GPU as well as CPU. Support for MDL's module system. Import and export of MDL modules including dependencies. Requirements Operating System Windows, Linux and Mac OS Development Environment…
…Linux distributions, AI frameworks, and orchestration platforms run unmodified, allowing existing infrastructure software to scale onto Vera-based systems without disruption. Confidential computing is supported natively, enabling secure execution across CPU–GPU…
…However, the software ecosystem is a new bottleneck. While the MLIP models themselves run on GPUs, the surrounding simulation infrastructure often relies on legacy CPU-centric code. NVIDIA ALCHEMI (AI Lab for…
…We're constantly improving tools to enable CUDA developers to take advantage of the latest hardware and software features. If you’re doing compute on GPUs, this presentation will have something for…
…Combined with DGX Linux OS and the full NVIDIA software stack, developers can efficiently prototype and build agentic AI workflows with Gemma 4 while maintaining private, secure on-device execution. The vLLM…
…Unlocks CRIU-style workflows for GPU processes: fault-tolerant long jobs, preemption and migration on shared clusters, and fast warm-start of inference workers. Only available in Linux. Inter-process sharing (IPC…
…1, the Linux Foundation’s permissive license purpose-built for open AI model distributions. OpenMDW is designed to cover the full set of model materials, including architecture, parameters, documentation, software, and other…
…Benchmarking setup The software stack is based on NVIDIA Enterprise RAs (Figure 1). This includes the NVIDIA AI Enterprise stack to manage GPUs using NVIDIA GPU Operator for lifecycle management, NVIDIA Network…
…4 MIN READ Apr 09, 2026 Running Large-Scale GPU Workloads on Kubernetes with Slurm Slurm is an open source cluster management and job scheduling system for Linux. It manages job scheduling…