vLLM is built for a different problem than Ollama solves: not running a model easily on a laptop, but serving one efficiently at real production scale across many concurrent requests. Its PagedAttention technique manages the memory used for attention key-value caches more like an operating system manages virtual memory, cutting the waste that comes from naively allocating memory for a worst-case sequence length on every request.
That efficiency gain translates directly into more requests served per GPU, which matters enormously at scale, where GPU cost is usually the single largest line item in running an LLM-based product. It supports a wide range of open models and integrates with the broader serving ecosystem, including being used as the backend for several hosted inference providers rather than only self-hosted deployments.
Being free, open source, and backed by active development from both academia, it originated at UC Berkeley, and industry contributors, it's become close to a standard choice for teams self-hosting LLM inference at any meaningful scale, the same way Ollama has become standard for casual local use.