Solutions Engineer

Deploying a custom Large Language Model (LLM) in the cloud has never been more accessible, thanks to containerization technologies like Docker and cloud GPU providers like RunPod. Whether you're fine-tuning an open-source LLM or building a private inference API, this guide walks you through deploying your model using Docker — from building your container to exposing endpoints for production use.

In this tutorial, we’ll cover:

- Building a Docker container with your LLM, tokenizer, and inference server
- Configuring GPU runtime for efficient inference
- Exposing your LLM via HTTP endpoints
- Deploying the container on RunPod
- FAQs on GPU selection, scaling, and container limits

Let’s get started!

## Prerequisites

Before we begin, ensure you have the following:

- A trained or fine-tuned LLM (e.g., from Hugging Face or a local model checkpoint)
- Docker installed on your local machine
- A RunPod account (Sign up for free [here](https://www.runpod.io/))
- Basic familiarity with Python and Docker

> 💡 Want to skip ahead? Explore our [Docker-based LLM templates](https://www.runpod.io/templates) to jumpstart your deployment.

---

## Step 1: Choose an Inference Server

To serve your model efficiently, you’ll need an inference server optimized for LLMs. Two popular options are:

- [vLLM](https://github.com/vllm-project/vllm): High-throughput inference server built for transformer models
- Hugging Face’s [text-generation-inference](https://huggingface.co/docs/text-generation-inference/index): Production-grade inference server used to power Hugging Face APIs

In this guide, we’ll use Hugging Face’s `text-generation-inference`, but the process is similar for `vLLM`.

---

## Step 2: Prepare Your Model Files

Gather the following components:

- Model weights (e.g., `pytorch_model.bin`, `model.safetensors`)
- `config.json`
- `tokenizer.json` or tokenizer folder

You can download these from Hugging Face using `transformers` CLI or `git lfs`:

```bash
git lfs install
git clone https://huggingface.co/your-username/your-model
```

Place these files in a directory called `model/` inside your Docker project.

---

## Step 3: Write a Dockerfile

Your `Dockerfile` should install required dependencies, copy your model files, and start the inference server.

Here’s an example Dockerfile using Hugging Face’s TGI:

```Dockerfile
FROM ghcr.io/huggingface/text-generation-inference:1.2

# Copy your model into the container
COPY ./model /data

# Set environment variables (optional)
ENV MODEL_ID=/data
ENV MAX_INPUT_LENGTH=1024
ENV MAX_TOTAL_TOKENS=2048

# Expose the inference port
EXPOSE 80
```

> 🛠 You can find more Dockerfile examples in our [LLM deployment templates](https://www.runpod.io/templates).

Build your Docker image:

```bash
docker build -t my-custom-llm .
```

Run it locally (for testing):

```bash
docker run --gpus all -p 8080:80 my-custom-llm
```

Now navigate to `http://localhost:8080/generate` and test your model via POST requests.

---

## Step 4: Push to a Container Registry

To deploy on RunPod, your container must be accessible from a registry like Docker Hub or GitHub Container Registry.

Tag and push your image:

```bash
docker tag my-custom-llm your-dockerhub-username/my-custom-llm
docker push your-dockerhub-username/my-custom-llm
```

---

## Step 5: Deploy on RunPod

1. Log into your [RunPod dashboard](https://www.runpod.io/console)
2. Click **“Deploy”** > **“Custom Container”**
3. Under **Container Image**, enter your Docker image URL (`docker.io/your-dockerhub-username/my-custom-llm`)
4. Enable **GPU Access** and select a GPU type (e.g., A100, RTX 3090, 4090)
5. Under **Container Ports**, expose port `80`
6. Optionally mount volumes for logging or persistent model data
7. Click **“Deploy Pod”**

Your LLM is now running in the cloud with GPU acceleration!

> 🚀 Want to automate deployments? Use our [RunPod API](https://docs.runpod.io) to spin up pods programmatically.

---

## Step 6: Access Your Model via API

Once deployed, RunPod provides an endpoint like:

```
https://<your-pod-id>.runpod.io/generate
```

Send a POST request to generate text:

```bash
curl -X POST https://<your-pod-id>.runpod.io/generate \
    -H "Content-Type: application/json" \
    -d '{"inputs":"Hello, my name is"}'
```

You’ll receive a JSON response with the generated output.

---

## Optional: Scaling and Load Balancing

For production use, consider auto-scaling via multiple pods. RunPod supports:

- Horizontal scaling (multiple pods)
- Load balancing via custom endpoints or external reverse proxies
- Auto-shutdown and restart options to save costs

Check out our [inference scaling guide](https://docs.runpod.io/docs/inference-scaling) to build robust deployments.

---

## Common Questions

### What GPU should I choose?

- **RTX 3090 / 4090**: Great for medium-sized models (e.g., LLaMA 7B, Mistral)
- **A100 / H100**: Best for large models (13B+, multi-threaded inference)
- **T4**: Cost-effective for low-latency or smaller models

Check our [GPU comparison guide](https://www.runpod.io/gpu-comparison) for performance benchmarks.

### Are there container size limits?

RunPod supports containers up to 10GB in size via registry pull. For larger models, mount a volume with your model files or use model downloading logic in your container.

### Can I scale inference across multiple GPUs?

Yes! Use inference servers like `vLLM` or `DeepSpeed` with multi-GPU support. RunPod supports multi-GPU pods and can be configured via environment variables or launch scripts.

---

## Final Thoughts

Deploying your own LLM in the cloud doesn’t have to be complex. With Docker and RunPod, you can containerize your model, expose it via a secure API, and scale on demand — all while benefiting from high-performance GPUs at a fraction of traditional cloud costs.

> 🔧 Ready to bring your model to life? [Sign up for RunPod](https://www.runpod.io) and deploy your first LLM today.

> 📦 Need a head start? Explore our [prebuilt LLM templates](https://www.runpod.io/templates) and modify them to suit your needs.

---

## Resources

- [Docker Documentation](https://docs.docker.com/)
- [Hugging Face TGI Server](https://huggingface.co/docs/text-generation-inference/)
- [RunPod Docs](https://docs.runpod.io)
- [RunPod Templates](https://www.runpod.io/templates)

Happy deploying! 🚀

How to Deploy a Custom LLM in the Cloud Using Docker