Deploy Local LLAMA-3 with NVIDIA NIM: A Comprehensive Guide

To deploy a Lama family of model using NVIDIA NIM on your own cloud or local machine, follow these steps:

1. Set up the Environment: If running on your local machine, install Docker engine and NVIDIA container toolkit. Links to these are provided in the video description.

2. Obtain API Key: Sign up for an NVIDIA account to generate your API key and personal key. These will be required for interacting with the remote server.

3. Run the Docker Container: Use the provided Docker command to run the container, specifying the container name, GPU usage, API key, model cache location, and port. This will download and set up the Lama 3 8 billion instruct model.

4. Interact with the Model: Use the provided cURL command to interact with the deployed model. This command follows the OpenAI API standard, allowing you to use the OpenAI client for interaction.

5. Stress Test the API: Use a Python script with the requests library to send multiple concurrent requests to the API endpoint and monitor the GPU utilization and throughput.

6. Use OpenAI API Client: Demonstrate how to use the OpenAI API client with the NVIDIA NIM, by updating the base URL and other parameters to match the deployed model.

The NVIDIA NIM provides a convenient and optimized way to deploy large language models for inference, with the ability to achieve up to 3 times better performance compared to other deployment options. The 90-day free trial allows you to explore this solution further.

To access the NVIDIA Launchpad and the GPU metrics dashboard, follow these steps:

1. As part of the NVIDIA Launchpad, you get access to a code IDE, which is Visual Studio Code. You can use this IDE to interact with the GPU instance and deploy the Llama 3 8 billion instruct model.

2. The GPU metrics dashboard is a Grafana dashboard that provides detailed information about the GPU usage and performance. You can access this dashboard to monitor the GPU utilization, CUDA version, GPU drivers, and other relevant metrics.

3. The Grafana dashboard gives you a visual representation of the GPU usage over time, allowing you to track the performance and optimize the deployment accordingly.

4. You can use the watch command in the terminal to monitor the GPU usage in real-time. The command watch -n 1 nvidia-smi will update the GPU usage every second, providing you with a live view of the GPU utilization.

5. The GPU metrics dashboard and the real-time monitoring tools allow you to understand the performance characteristics of the Llama 3 8 billion instruct model deployment, helping you optimize the resource utilization and ensure efficient inference.

To deploy the Llama 3 8 billion instruct model using NVIDIA NIM, follow these steps:

1. Open the provided IDE and ensure you have access to an H100 GPU.
2. Set up your API key by signing up for an NVIDIA account and generating the necessary keys.
3. Run the following Docker command to start the NVIDIA NIM container:

docker run -it --gpus all -e NVIDIA_API_KEY=$NVIDIA_API_KEY -p 8000:8000 --name llama-3-8b-instruct nvcr.io/nvidia/nim:latest --model-name llama-3-8b-instruct

This command will:

• Create a Docker container named "llama-3-8b-instruct"
• Use all available GPUs on the system
• Set the NVIDIA_API_KEY environment variable
• Expose port 8000 for the NIM server
• Use the "llama-3-8b-instruct" model from the NVIDIA NIM catalog

4. Once the container is running, you can use the provided cURL command to interact with the model:

curl -X POST -H "Content-Type: application/json" -d '{"model": "llama-3-8b-instruct", "prompt": "Tell me a joke.", "max_tokens": 1000, "temperature": 0.7, "top_p": 0.95, "stop": ["\n"]}' http://localhost:8000/v1/completions

This cURL command sends a request to the NIM server running on localhost:8000 to generate a response for the prompt "Tell me a joke."

5. To stress test the API endpoint, you can run the provided Python script test_server.py. This script will send multiple concurrent requests to the NIM server and monitor the GPU utilization.

6. Finally, you can also use the OpenAI API client to interact with the NIM server by updating the base URL to point to the NIM server's address and port.

By following these steps, you can successfully deploy the Llama 3 8 billion instruct model using the NVIDIA NIM Docker container and test its performance.

To interact with the NVIDIA NIM API, we can use a simple cURL command. The cURL command provides the following:

• Makes a POST request to the local host at port 8000, which is where the NVIDIA NIM server is running.
• Uses the OpenAI-compatible API, so we can use the OpenAI client to interact with the NIM server.
• Specifies the Llama 3 8 billion instruct model to use.
• Sets the message structure similar to what OpenAI expects.
• Allows setting additional parameters like max_tokens and temperature.

Here's the cURL command:

curl -X POST -H "Content-Type: application/json" -d '{"model": "nlp/lama-3-8b-instruct", "messages": [{"role": "user", "content": "Tell me a joke"}], "max_tokens": 1000, "temperature": 0.7}' http://localhost:8000/v1/chat/completions

This cURL command will send a request to the NVIDIA NIM server, which will then generate a response from the Llama 3 8 billion instruct model. The response will be streamed back, with each token being displayed as it is generated.

To further stress test the API endpoint, we can use a Python script that utilizes the requests library to make multiple concurrent requests. This script will send 50 joke generation requests in parallel and monitor the GPU utilization and throughput.

1import requests
2import threading
3
4url = "http://localhost:8000/v1/chat/completions"
5headers = {"Content-Type": "application/json"}
6payload = {
7    "model": "nlp/lama-3-8b-instruct",
8    "messages": [{"role": "user", "content": "Tell me 50 jokes"}],
9    "max_tokens": 1000,
10    "temperature": 0.7,
11    "stream": False
12}
13
14def send_requests():
15    for _ in range(50):
16        response = requests.post(url, headers=headers, json=payload)
17        print(response.json())
18
19threads = []
20for _ in range(10):
21    t = threading.Thread(target=send_requests)
22    t.start()
23    threads.append(t)
24
25for thread in threads:
26    thread.join()

This script demonstrates how to use the OpenAI-compatible API with the NVIDIA NIM server. It shows that the NVIDIA NIM server can provide high-performance inference for large language models, with the potential for up to 3x improvement compared to not using the NIM.

To stress test the NVIDIA NIM API endpoint, we will use a simple Python script that leverages the requests library to make multiple concurrent requests to the API. Here's how it works:

1. We define the API endpoint URL, which in this case is the local host since we're running the server locally. If you were to deploy this on a remote server, you would need to use the external IP address and enable port forwarding.

2. We set the necessary headers, including the Content-Type header to application/json.

3. We create the payload, which includes the model name ("model": "llama-3-8b-instruct"), the prompt ("prompt": "Tell me 50 jokes."), and other parameters like max_tokens, temperature, and stop.

4. We define a function send_requests() that sends the requests using the requests.post() method and the URL, headers, and payload we defined earlier.

5. We use multithreading to run multiple instances of the send_requests() function concurrently, simulating a high volume of requests to the API endpoint.

6. We monitor the GPU usage in real-time using the watch command and the nvidia-smi tool, which provides insights into the GPU utilization.

The output shows that the API endpoint is able to handle a significant number of concurrent requests, with an average throughput of around 2,500 tokens per second. The GPU utilization also remains relatively high, indicating that the NVIDIA NIM is effectively leveraging the hardware resources to deliver high-performance inference.

This stress test demonstrates the scalability and performance of the NVIDIA NIM solution, making it a compelling option for deploying large language models in a production environment.

To use the OpenAI API client with the NVIDIA NIM, follow these steps:

1. Change the base URL to the URL of your NVIDIA NIM instance. In this example, we're running it on localhost:8000:

1openai.api_base = "http://localhost:8000"

2. You don't need to provide the API key, as the NVIDIA NIM handles the authentication.

3. Set the model to the NVIDIA NIM-hosted model, in this case, "meta-llama-38b-instruct":

1model = "meta-llama-38b-instruct"

4. Set the other parameters, such as the maximum number of tokens to generate, temperature, and whether to stream the responses:

1response = openai.ChatCompletion.create(
2    model=model,
3    messages=[{"role": "user", "content": "Tell me 50 different jokes"}],
4    max_tokens=1024,
5    temperature=0.7,
6    stream=True,
7)

5. Iterate through the streaming response and print the generated text:

1for chunk in response:
2    print(chunk.choices[0].text, end="")

This approach allows you to leverage the performance and ease of deployment provided by the NVIDIA NIM while using the familiar OpenAI API client. The streaming response ensures you get the generated text in real-time, providing a responsive user experience.

In this video, we have explored how to deploy a Lama 3 8 billion instruct model using NVIDIA NIM on your own cloud or local machine. NVIDIA NIM is a set of microservices developed by NVIDIA that accelerates the deployment of foundation models, including language models and other AI models.

We have walked through the steps to set up the NVIDIA NIM environment, including generating the API key and running the Docker container to deploy the Lama 3 model. We have also tested the performance of the deployed model by sending multiple requests simultaneously and monitoring the GPU utilization and throughput.

Additionally, we have shown how to use the OpenAI API client to interact with the NVIDIA NIM-deployed model, demonstrating the compatibility with the OpenAI API standard.

Overall, NVIDIA NIM provides a convenient and efficient way to deploy large language models in a production environment. The ability to leverage NVIDIA's hardware and software stack can lead to significant performance improvements compared to other deployment options. If you are interested in exploring more deployment options for your projects, be sure to subscribe to the channel for upcoming content on VLLM and other related topics.