Support Matrix#

Please refer to below minimum requirements to ensure successful deployment and usage of USD Code API.

Models and NIMs#

  • Llama3.1- 70B NIM. See model card information here.

  • NV-Embed-e5-v5 NIM. See model card information here.

System Requirements#

Deployment#

Hardware#

  • Kubernetes cluster minimum hardware requirements:

  • A suitable setup example for running USDCode models efficiently is a Microk8s cluster configured with 5 (4+1) H100 GPUs enabled. By meeting these system requirements, you’ll be well-equipped to deploy and utilize your USDCode models successfully.

Software#

  • Linux operating systems (Ubuntu 20.04 or later recommended)

  • NVIDIA Driver >= 560

  • NVIDIA Docker >= 23.0.1

  • CUDA >= 12.6.1

Llama 3.1 -70b:1.3.0 is based on CUDA 12.6.1 which requires NVIDIA Driver release 560 or later. However, if you are running on a data center GPU (for example, A100 or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 535.86 (or later R535), or 550.54 (or later R550). Nv-embed-e5-v5 from 1.0.0 uses Triton Inference Server 24.05.

VS Code / Other IDE Integration#

Software#

  • Required: Visual Studio Code

  • Required: Continue.dev (or your preferred copilot extension)

  • Required: Python 3.10

Sample Kit Extension#

Hardware#

  • GPU: NVIDIA RTX capable GPU (Turing or newer recommended)

Software#

  • Operating System: Windows 10/11 or Linux (Ubuntu 20.04/22.04 recommended)

  • Driver: Please follow Omniverse developer guide - Technical Requirements page to ensure the correct versions are used.

  • Internet Access: Required for downloading the Omniverse Kit SDK, extensions, and tools.

  • Software Dependencies:

    • Required: Git (with LFS enabled)

    • Recommended: VS Code (or your preferred IDE)

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