llama-stack-mirror/llama_stack/providers/remote/inference/nvidia/NVIDIA.md

NVIDIA Inference Provider for LlamaStack

This provider enables running inference using NVIDIA NIM.

Features

• Endpoints for completions, chat completions, and embeddings for registered models

Getting Started

Prerequisites

• LlamaStack with NVIDIA configuration
• Access to NVIDIA NIM deployment
• NIM for model to use for inference is deployed

Setup

Build the NVIDIA environment:

llama stack build --distro nvidia --image-type venv

Basic Usage using the LlamaStack Python Client

Initialize the client

import os

os.environ["NVIDIA_API_KEY"] = (
    ""  # Required if using hosted NIM endpoint. If self-hosted, not required.
)
os.environ["NVIDIA_BASE_URL"] = "http://nim.test"  # NIM URL

from llama_stack.core.library_client import LlamaStackAsLibraryClient

client = LlamaStackAsLibraryClient("nvidia")
client.initialize()

Create Completion

response = client.inference.completion(
    model_id="meta-llama/Llama-3.1-8B-Instruct",
    content="Complete the sentence using one word: Roses are red, violets are :",
    stream=False,
    sampling_params={
        "max_tokens": 50,
    },
)
print(f"Response: {response.content}")

Create Chat Completion

response = client.inference.chat_completion(
    model_id="meta-llama/Llama-3.1-8B-Instruct",
    messages=[
        {
            "role": "system",
            "content": "You must respond to each message with only one word",
        },
        {
            "role": "user",
            "content": "Complete the sentence using one word: Roses are red, violets are:",
        },
    ],
    stream=False,
    sampling_params={
        "max_tokens": 50,
    },
)
print(f"Response: {response.completion_message.content}")

Create Embeddings

response = client.inference.embeddings(
    model_id="nvidia/llama-3.2-nv-embedqa-1b-v2",
    contents=["What is the capital of France?"],
    task_type="query",
)
print(f"Embeddings: {response.embeddings}")