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llama-3.1-8b-instruct

Run Anywhere

Advanced state-of-the-art model with language understanding, superior reasoning, and text generation.

chatlanguage generationrun-on-rtxtext-to-textcode generation
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API Reference
Accelerated by DGX Cloud
Deploying your application in production? Get started with a 90-day evaluation of NVIDIA AI Enterprise

Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.

Requirements

  • NVIDIA GeForce RTX 4080 or above (see supported GPUs)
  • Install the latest NVIDIA GPU Driver on Windows (Version 570+)

Experience via App

ChatRTXAnythingLLMAI Toolkit for VSCode

Step 1
Enable Virtualization

Ensure virtualization is enabled in the system BIOS. In Windows, open Task Manager, select the Performance tab, and find Virtualization. If Disabled, see here to enable.

Step 2
Open the Windows Subsystem for Linux 2 - WSL2 - Distro

Install WSL2. For additional instructions refer to the documentation.

Once installed, open the NVIDIA-Workbench WSL2 distro using the following command in the Windows terminal.

wsl -d NVIDIA-Workbench

Step 3
Run the Container

$ podman login nvcr.io Username: $oauthtoken Password: <PASTE_API_KEY_HERE>

Pull and run the NVIDIA NIM with the command below. This will download the optimized model for your infrastructure.

export NGC_API_KEY=<PASTE_API_KEY_HERE> export LOCAL_NIM_CACHE=~/.cache/nim mkdir -p "$LOCAL_NIM_CACHE" chmod -R a+w "$LOCAL_NIM_CACHE" podman run -it --rm \ --device nvidia.com/gpu=all \ --shm-size=8GB \ -e NGC_API_KEY=$NGC_API_KEY \ -v "$LOCAL_NIM_CACHE:/opt/nim/.cache" \ -e NIM_RELAX_MEM_CONSTRAINTS=1 \ -u $(id -u) \ -p 8000:8000 \ nvcr.io/nim/meta/llama-3.1-8b-instruct:1.8.0-RTX

Step 4
Test the NIM

You can now make a local API call using this curl command:

curl -X 'POST' \ 'http://0.0.0.0:8000/v1/chat/completions' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "model": "meta/llama-3.1-8b-instruct", "messages": [{"role":"user", "content":"Hello! How are you?"}], "max_tokens": 64 }'

For more details on getting started with this NIM, visit the NVIDIA NIM Docs.