If you're working with large language models, there's a good chance you've heard about Meta Llama 3.1 405B. With its hefty size and advanced capabilities, it's one of the standout models in the current generation. But having the model isn't enough. To derive real value from it, you need a platform that offers both power and flexibility. Google Cloud's Vertex AI is a reliable option for that.

This article will guide you through the process of deploying Meta Llama 3.1 405B on Vertex AI. You don't need to be a cloud veteran, but a decent grasp of terminal commands, containers, and basic Google Cloud services will make things easier.

Deploy Meta Llama 3.1 405B on Google Cloud Vertex AI

Step 1: Prepare Your Google Cloud Project

Before anything else, you need a clean and properly configured Google Cloud project. This is where your model will live and run.

Set Up Billing And Apis

Go to your Google Cloud Console and make sure billing is set up. Once that’s done, you’ll want to enable the necessary APIs:

• Vertex AI API
• Artifact Registry API
• Cloud Build API
• Compute Engine API
• IAM Service Account Credentials API

You can do this through the console or with the gcloud CLI:

bash

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gcloud services enable \

aiplatform.googleapis.com \

artifactregistry.googleapis.com \

cloudbuild.googleapis.com \

compute.googleapis.com \

iamcredentials.googleapis.com

Create A Service Account

This service account will run the model and access different services on your behalf.

bash

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gcloud iam service-accounts create llama3-runner \

--description="Runs Llama 3.1 405B on Vertex AI" \

--display-name="Llama3 Runner"

Then, give it the required permissions:

bash

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gcloud projects add-iam-policy-binding [YOUR_PROJECT_ID] \

--member="serviceAccount:llama3-runner@[YOUR_PROJECT_ID].iam.gserviceaccount.com" \

--role="roles/aiplatform.admin"

Step 2: Get the Llama 3.1 405B Model Files

Meta Llama models are available via Meta's gated release system, which requires acceptance of terms before downloading. Once you've received access, you'll be provided with links or instructions to securely download the model files.

What you’ll typically get:

• Model weights (sharded files, likely .safetensors or .pth)
• Tokenizer configuration
• Model architecture config (JSON or YAML)

Keep all these in a dedicated folder. You’ll use them to build your container image.

Step 3: Build a Custom Docker Container

Vertex AI works best with containers. Since Llama 3.1 405B is a huge model, you'll need a container with the right environment to load it efficiently. This includes PyTorch, Hugging Face Transformers (if applicable), and possibly DeepSpeed or TensorRT if you're optimizing for inference.

Here’s a simple Dockerfile template to get you started:

Dockerfile

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FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04

ENV DEBIAN_FRONTEND=noninteractive

RUN apt-get update && apt-get install -y \

python3-pip \

git \

curl \

&& rm -rf /var/lib/apt/lists/*

RUN pip3 install torch==2.2.0 \

transformers==4.41.0 \

accelerate \

sentencepiece \

bitsandbytes \

einops \

peft

COPY . /app

WORKDIR /app

CMD ["python3", "serve.py"]

In this case, serve.py is the script you’ll use to load and serve the model. It should accept input from a REST endpoint and return the model’s responses.

Here’s a very basic serve.py just to illustrate the structure:

python

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from transformers import AutoTokenizer, AutoModelForCausalLM

from flask import Flask, request, jsonify

tokenizer = AutoTokenizer.from_pretrained("path_to_tokenizer")

model = AutoModelForCausalLM.from_pretrained("path_to_model", device_map="auto")

app = Flask(__name__)

@app.route("/predict", methods=["POST"])

def predict():

input_text = request.json.get("text", "")

inputs = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=50)

decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)

return jsonify({"response": decoded})

if __name__ == "__main__":

app.run(host="0.0.0.0", port=8080)

Once your Dockerfile and serve.py are in place, you can build the image:

bash

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docker build -t llama3-infer .

Then, tag and push it to Google Artifact Registry:

bash

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gcloud auth configure-docker [REGION]-docker.pkg.dev

docker tag llama3-infer [REGION]-docker.pkg.dev/[PROJECT_ID]/llama3/llama3-infer:latest

docker push [REGION]-docker.pkg.dev/[PROJECT_ID]/llama3/llama3-infer:latest

Step 4: Deploy to Vertex AI

Now that the container is ready, it’s time to create an endpoint on Vertex AI and deploy the model.

Create a Model Resource

bash

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gcloud ai models upload \

--region=[REGION] \

--display-name=llama3_405b \

--container-image-uri=[REGION]-docker.pkg.dev/[PROJECT_ID]/llama3/llama3-infer:latest

This registers your container as a model in Vertex AI.

Deploy the Model to an Endpoint

bash

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gcloud ai endpoints create \

--region=[REGION] \

--display-name=llama3-endpoint

Then deploy the model:

bash

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gcloud ai endpoints deploy-model [ENDPOINT_ID] \

--region=[REGION] \

--model=[MODEL_ID] \

--display-name=llama3-deployment \

--machine-type=n1-standard-8 \

--accelerator-type=NVIDIA_TESLA_T4 \

--accelerator-count=2

You can adjust the machine type and GPU according to your needs. For something as large as 405B, you'll likely need A100s or even multiple nodes with model parallelism—depending on how you've optimized the model and container.

Step 5: Test Your Deployment

Once deployed, you’ll get an endpoint URL. You can use curl or any HTTP client to send a request.

bash

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curl -X POST \

-H "Content-Type: application/json" \

-H "Authorization: Bearer $(gcloud auth print-access-token)" \

https://[REGION]-aiplatform.googleapis.com/v1/projects/[PROJECT_ID]/locations/[REGION]/endpoints/[ENDPOINT_ID]:predict \

-d '{"instances": [{"text": "What is the capital of Japan?"}]}'

The model should respond with a JSON containing the generated text. At this point, you’ve successfully deployed Llama 3.1 405B on Vertex AI.

Manage Logs, Errors, and Scaling Behavior

Once your model is up and running, monitor its performance under load. Llama 3.1 405B isn't light, so even a few requests with long prompts can push memory and processing limits. Use Vertex AI's built-in logging to track slow inferences, errors, or GPU bottlenecks. If you expect traffic to vary, enable auto-scaling with a sensible range of replicas. However, ensure your container can start quickly; otherwise, scaling won't be effective when demand spikes.

Wrapping Up

Deploying Meta Llama 3.1 405B on Google Cloud’s Vertex AI is entirely possible with the right preparation. You’ll need a well-built container, model files from Meta, a capable Google Cloud setup, and a working script to serve requests. While the process may seem involved, each step is fairly repeatable once you've done it the first time. This setup allows you to scale up or down as needed, and you keep full control over your deployment.