What Is vLLM?
vLLM is an open-source engine for running inference on large language models. It began as a research project focused on managing the KV cache more efficiently and has become one of the most widely used ways to serve open models in production.
The problem vLLM solves is throughput. A naive server that handles one request at a time leaves most of a GPU idle. vLLM keeps the GPU busy by packing many concurrent requests through the model together and managing their memory carefully, so a single GPU serves far more tokens per second than a simple implementation.
How It Works
Two ideas do most of the work:
- PagedAttention: vLLM manages the KV cache in fixed-size blocks, the way an operating system pages memory. This reduces fragmentation and lets the server fit more concurrent requests into the same GPU memory. See paged attention.
- Continuous batching: instead of waiting for a whole batch to finish, vLLM swaps individual requests in and out as they complete, so the GPU is never idle waiting on the slowest request in a batch.
On top of these, vLLM supports prefix caching, quantization, and serving many fine-tuned LoRA adapters against a shared base model. It exposes an OpenAI-compatible API, which makes it a drop-in backend for applications already written against that interface.
vLLM and Saturn Cloud
vLLM is the serving layer. It still needs GPUs to run on, autoscaling to match demand, and monitoring to stay healthy in production.
Saturn Cloud runs vLLM as a first-class serving option on GPU infrastructure across providers. Teams deploy a model with vLLM, autoscale it to actual request volume, and watch throughput, latency, and GPU utilization, without assembling and operating the serving cluster themselves.
