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Supported Model TypesCurrently we list NIM models of following types
  • Large Language Models (LLMs)
  • Vision Language Models (VLMs)
  • Embedding Models
  • Reranking Models

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Adding nvcr.io Docker Registry

  1. Generate an API Key from https://org.ngc.nvidia.com/setup/api-keys Make sure to give it access to NGC Catalog image.png
  2. Add a Custom Docker Registry to the Platform
    • Registry URL: nvcr.io
    • Username: $oauthtoken
    • Password: The API Key from the previous step
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Adding NGC API Key to Secrets

  1. Add the same API Key as a Secret on the Platform. We are calling the secret NGC_API_KEY Add Ngc Api Key As Secret Pn

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Deploying a NIM Model

  1. From New Deployment page, select NVIDIA NIM.
    • Select the workspace you want to deploy to
    • Select the NVCR Model Registry Integration we created in the previous step
    • Select the NGC API Key Secret we created in the previous step
    • Select the model you want to deploy
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  2. Click Next. You will be presented with optimized profiles (for latency or throughput ) for differerent precision and GPU options for which TRT-LLM engines are prebuilt and available. You can select any of the profile and Continue to Deployment. Nim Deployment Options Pn

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(Optional) Caching NIM Model to External Volume

Recommended for Large Models and Production Environments
To avoid re-downloading Model on every restart, you can Create a Volume and Mount the Volume at /opt/nim/.cache 4fcd568c 5d90c572d92dd8c09ec0590fba11f5058f486f2d663594b95086fec59f074340 Image Pn

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Running Inferences

You can now run inferences via the OpenAPI tab. You can also Add the Model to LLM Gateway using the button on the top 84390370 9d4356719c8ce8f41dc04108e037fab0f980d78a06cbbb8f5d85f55bac0d5887 Image Pn