How to Deploy LLMs at Scale with vLLM

Serving a large language model to many users is a throughput problem, not just a model problem. vLLM is an open-source inference engine built for exactly this job. It uses a technique called PagedAttention to manage GPU memory efficiently, delivering dramatically higher requests-per-second than naive implementations. This tutorial walks you through deploying a model with vLLM from installation to a production-ready endpoint.

What Makes vLLM Fast

Traditional serving wastes GPU memory because each request reserves a fixed block of key-value cache. vLLM solves this with PagedAttention, which allocates memory in small pages and shares them across requests. The result is higher utilization, continuous batching, and much lower latency under load.

Step 1: Prepare Your Environment

You need a machine with an NVIDIA GPU and recent drivers. Create a clean Python virtual environment, then install vLLM with pip. Verify the installation by importing the package and checking that CUDA is detected.

Step 2: Start the OpenAI-Compatible Server

vLLM ships an OpenAI-compatible API server. Launch it by pointing the command at any Hugging Face model repository. Add flags to control tensor parallelism, GPU memory utilization, and the maximum context length. When the process starts, it downloads the weights and reports the listening port.

Step 3: Send Your First Request

Once the server is up, send a chat completion request to the /v1/chat/completions endpoint. Because the API matches the OpenAI schema, you can reuse existing client libraries by changing only the base URL. This compatibility makes migration from a cloud provider almost effortless.

Step 4: Tune Throughput

The most important knobs are the maximum number of sequences and the GPU memory fraction. Increase batch size until memory pressure appears, then back off slightly. Enable prefix caching if many requests share a common system prompt. Benchmark with realistic prompts before finalizing settings.

Step 5: Scale Across GPUs

For models too large for one card, use tensor parallelism to shard weights across multiple GPUs. For more throughput, run several vLLM instances behind a load balancer. Add health checks so unhealthy workers are removed automatically.

Step 6: Containerize and Monitor

Package the server in a Docker image with your chosen model baked in or mounted. Expose the API port and add metrics collection so you can watch latency, tokens per second, and queue depth. Alerts on these signals catch capacity problems before users notice.

Step 7: When to Choose a Simpler Tool

If you are a single developer experimenting on a laptop, Ollama is far easier to set up. Reach for vLLM when you need to serve concurrent users, hit strict latency targets, or maximize hardware ROI.

Conclusion

vLLM brings production-grade performance to open-model serving. Install it, start the OpenAI-compatible server, tune your batch settings, and scale across GPUs. With a few commands you move from a prototype to an endpoint that can handle real traffic.