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meta

llama-3.1-8b-instruct

Run Anywhere

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

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
Ensure virtualization is enabled in the system BIOS

In Windows, open the Task Manager. Select the Performance tab and click on CPU. Check if Virtualization is enabled. If it is 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
Export API Key

Export your personal credentials as environment variables:

export NGC_API_KEY=<PASTE_API_KEY_HERE>

Step 4
Login to NVIDIA NGC

Login to NVIDIA NGC so that you can pull the NIM container:

echo "$NGC_API_KEY" | podman login nvcr.io --username '$oauthtoken' --password-stdin

Step 5
Pull and Run NVIDIA NIM

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

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 6
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.