How to Deploy Llama 3.1 405B on GPU Cloud in Under 5 Minutes

Introduction: Why Run Llama 3.1 405B?

Meta's Llama 3.1 405B is the most powerful open-source large language model available today. With 405 billion parameters, it rivals GPT-4 and Claude in many benchmarks while being completely free to use.

But here's the challenge: running a 405B parameter model requires serious GPU power. You'll need at least 8x NVIDIA H100 GPUs (or equivalent) with a combined 640GB+ of VRAM just to load the model.

That's where cloud GPUs come in. Instead of spending $300,000+ on hardware, you can rent the exact GPUs you need for under $3/hour and deploy Llama 3.1 405B in minutes.

Requirements

To run Llama 3.1 405B with good inference speed, you'll need:

Configuration	VRAM	Est. Cost/Hour	Throughput
8x NVIDIA H100 80GB	640 GB	~$24/hour	~50 tok/s
8x NVIDIA A100 80GB	640 GB	~$16/hour	~30 tok/s
4x NVIDIA H100 (FP8 quantized)	320 GB	~$12/hour	~35 tok/s

For this tutorial, we'll use the 8x H100 configuration for maximum performance, but I'll also show you how to run the quantized version if you want to save money.

Step 1: Launch Your GPU Instance

First, you need to spin up a GPU instance. The fastest way is through GPUBrazil's console:

1. Sign up for a free account (takes 30 seconds)
2. Add credits via credit card or crypto
3. Go to the Console and click "Deploy New Instance"
4. Select 8x H100 80GB configuration
5. Choose Ubuntu 22.04 with CUDA 12.1 pre-installed
6. Click Deploy

Your instance will be ready in about 60 seconds. You'll get an IP address and SSH credentials.

Step 2: Connect to Your Instance

Once your instance is ready, connect via SSH:

# Connect to your GPU instance
ssh root@YOUR_INSTANCE_IP

# Verify GPUs are detected
nvidia-smi

You should see all 8 H100 GPUs listed with their memory and utilization.

Step 3: Install vLLM

vLLM is the fastest inference engine for LLMs. It uses PagedAttention to maximize throughput and supports tensor parallelism across multiple GPUs.

# Create a virtual environment
python3 -m venv llama-env
source llama-env/bin/activate

# Install vLLM (this takes about 2 minutes)
pip install vllm

# Verify installation
python -c "import vllm; print(vllm.__version__)"

Step 4: Download Llama 3.1 405B

You'll need to accept Meta's license on Hugging Face first, then download the model:

# Login to Hugging Face (you need an account with model access)
pip install huggingface_hub
huggingface-cli login

# Download the model (this takes 15-30 minutes depending on connection)
# The model is ~750GB in size
python -c "from huggingface_hub import snapshot_download; snapshot_download('meta-llama/Meta-Llama-3.1-405B-Instruct')"

Step 5: Launch the Inference Server

Now for the magic - launch vLLM with tensor parallelism across all 8 GPUs:

# Start vLLM server with tensor parallelism
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Meta-Llama-3.1-405B-Instruct \
    --tensor-parallel-size 8 \
    --gpu-memory-utilization 0.95 \
    --max-model-len 8192 \
    --port 8000

The server will load the model across all GPUs (takes 3-5 minutes) and then start listening for requests.

Step 6: Test Your Deployment

vLLM provides an OpenAI-compatible API, so you can use it as a drop-in replacement:

# Test with curl
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/Meta-Llama-3.1-405B-Instruct",
    "messages": [
      {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    "max_tokens": 500
  }'

Or use the OpenAI Python SDK:

from openai import OpenAI

# Point to your vLLM server
client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="dummy"  # vLLM doesn't require auth by default
)

response = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3.1-405B-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful AI assistant."},
        {"role": "user", "content": "Write a Python function to calculate Fibonacci numbers."}
    ],
    max_tokens=500,
    temperature=0.7
)

print(response.choices[0].message.content)

Performance Benchmarks

Here's what you can expect from your deployment:

Metric	8x H100	8x A100
First Token Latency	~200ms	~350ms
Generation Speed	~50 tok/s	~30 tok/s
Concurrent Users	~20	~12
Cost per 1M tokens	~$0.12	~$0.15

Compare that to OpenAI's GPT-4 at $30 per 1M tokens for input + output. Running Llama 3.1 405B yourself is 200x cheaper!

Running the Quantized Version (Save 50%)

If you want to reduce costs, you can run the FP8 quantized version on just 4x H100 GPUs:

# For FP8 quantized model (requires 4x H100)
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Meta-Llama-3.1-405B-Instruct-FP8 \
    --tensor-parallel-size 4 \
    --dtype float16 \
    --quantization fp8 \
    --gpu-memory-utilization 0.95 \
    --port 8000

The quantized version has minimal quality loss (~1-2% on benchmarks) but runs on half the hardware.

Making Your API Production-Ready

1. Add Authentication

Protect your endpoint with a simple API key:

# Start vLLM with API key authentication
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Meta-Llama-3.1-405B-Instruct \
    --tensor-parallel-size 8 \
    --api-key YOUR_SECRET_KEY \
    --port 8000

2. Run as a Service

Create a systemd service for automatic restarts:

# /etc/systemd/system/llama-api.service
[Unit]
Description=Llama 3.1 405B API Server
After=network.target

[Service]
Type=simple
User=root
WorkingDirectory=/root
ExecStart=/root/llama-env/bin/python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Meta-Llama-3.1-405B-Instruct \
    --tensor-parallel-size 8 \
    --port 8000
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target

3. Add a Reverse Proxy

Put nginx in front for HTTPS and rate limiting:

apt install nginx certbot python3-certbot-nginx -y
# Configure your domain and SSL

Cost Comparison: Self-Hosting vs API Providers

Let's do the math for a typical workload of 100M tokens/month:

Provider	Cost per 1M Tokens	100M Tokens/Month
OpenAI GPT-4	$30.00	$3,000/month
Anthropic Claude	$15.00	$1,500/month
Together AI (Llama)	$0.90	$90/month
GPUBrazil (Self-Hosted)	$0.12	$12/month

Self-hosting on GPUBrazil is 250x cheaper than OpenAI and 7.5x cheaper than Together AI. Plus, you get full control over your data and no rate limits.

Conclusion

Running Llama 3.1 405B on cloud GPUs is not only possible but practical and affordable. In under 5 minutes of actual work (plus download time), you can have the world's most powerful open-source LLM running on your own infrastructure.

The key benefits:

• Cost savings: 200x cheaper than OpenAI API
• Data privacy: Your prompts never leave your server
• No rate limits: Scale as much as you need
• Customization: Fine-tune on your own data

Ready to start? Sign up for GPUBrazil and deploy your first Llama instance today. Use code LLAMA10 for an extra $10 credit on your first deposit.