vLLM came out of Berkeley's Sky Computing Lab with a specific insight about memory management during inference, and it turned into the default open source serving engine for anyone running models on their own hardware. The documentation reflects an infrastructure project rather than a consumer product, which means it is dense, technically honest and completely unsuitable for a beginner who wandered in from a tutorial. The getting started material does what it should. Install, load a model, run offline inference, start a server.
Because the server is OpenAI API compatible, existing client code pointed at a different base URL usually works unchanged, and the documentation makes that path clear. That compatibility is the single most practical decision the project made and it lowers the barrier to trying it considerably. The design documents are the best part of this site and I would recommend them to people who have no intention of ever running vLLM. PagedAttention, the idea that borrows virtual memory paging to manage the key value cache, is explained properly.
Continuous batching, where requests join and leave a running batch rather than waiting for a fixed batch to complete, is explained properly. Prefix caching, speculative decoding and the scheduler behaviour are all described in terms of what they do and why they help. Read these and you will understand why serving a model at scale is a memory problem before it is a compute problem, why your throughput collapses at long context, and why hosted inference is priced the way it is. That understanding is worth having whether you self host or not, and it is hard to get anywhere else in this much detail.
Distributed serving is covered thoroughly. Tensor parallelism, pipeline parallelism, data parallelism, expert parallelism for mixture of experts models, and the combinations. This is where large deployments live and where the documentation earns its keep, because getting these choices wrong wastes hardware in ways that are not obvious until you measure. Quantisation gets honest treatment.
The supported formats are listed with their memory and quality implications, and the docs do not pretend that quantisation is free. Anyone deciding whether to run a larger model quantised or a smaller model at full precision will find the material they need to reason about it, though not a straight answer, because there is not one. Model support is broad and the compatibility list is maintained, which matters more than it sounds. Support arriving for a newly released architecture within days is a real advantage of an actively developed open source project, and the list is where you check rather than guessing.
Now the problems. Prerequisites are severe and unacknowledged. To use this you need to understand GPU memory, CUDA versions, driver compatibility, how attention works well enough for the terminology to mean something, and for anything multi node, distributed systems. None of this is stated up front.
Someone arriving from a hosted API with a vague sense that self hosting would be cheaper will be defeated quickly and will not understand why. A short honest page saying who this is for would improve the experience of everyone who is not in that group. Pace of change hurts. vLLM ships fast, the internals change, flags come and go, and documentation lags.
Version specific behaviour differences appear in issues and release notes more reliably than in the docs. Always check which version the page you are reading describes. Performance tuning is scattered. The knobs that matter, GPU memory utilisation, maximum model length, batch size limits, chunked prefill, KV cache settings, are documented individually.
What is missing is a single practical guide that walks through tuning a deployment for a target workload, which is exactly what someone actually deploying needs. You end up assembling it from a dozen pages and a lot of experimentation. The economics are never addressed. The genuine question anyone faces before touching this is whether to self host at all.
Hosted inference has become cheap, GPUs are expensive whether you rent or own, and utilisation is the whole game, because a GPU at fifteen percent utilisation is dramatically worse value than an API. There are good reasons to self host, which are data residency, latency, cost at genuinely high sustained volume, custom models and control, and the documentation never helps you work out whether you have one. That is not really its job and its absence still leads people into expensive infrastructure they did not need. My three point nine is for excellent technical documentation with design material that genuinely teaches, held back by unstated prerequisites, scattered tuning guidance and a pace of change that leaves the written material behind.
If you are the audience for this, it is very good. If you are not sure whether you are the audience, you are not.