vLLM is a widely used open-source engine for self-hosted LLM inference. Here is what PagedAttention actually does, and why Hugging Face now recommends switching to it.
vLLM is one of the most widely adopted open-source engines for LLM inference today, with more than 46,500 GitHub stars and over 1,000 contributors as of its PyTorch Foundation announcement in May 2025. It's not a model, it's the serving software that sits between a trained model and the requests hitting it, and its core contribution, PagedAttention, solved a memory-waste problem that used to make serving large models on limited GPU memory needlessly expensive. This guide is vLLM explained properly: what it actually is, how vLLM works under the hood, and where it fits in the current inference-engine landscape.
Key takeaways
vLLM is an open source inference engine for serving large language models: software that loads a trained model's weights onto a GPU and handles the actual work of processing incoming requests and generating responses, at scale, for many users at once. As a vllm inference engine, it began as a research project at UC Berkeley's Sky Computing Lab, and became a PyTorch Foundation-hosted open-source project in May 2025, with contributions from a wide community of companies and individual maintainers rather than a single vendor.
The vllm meaning worth being precise about: it's infrastructure, not a model. You don't train anything with vLLM, and it doesn't have "knowledge" of its own. It takes an already-trained open-weight model, whatever family or size, and serves it efficiently. This is the same relationship a web server has to the website it hosts: vLLM doesn't create the content, it delivers it well.
Before vLLM, serving an LLM efficiently on a GPU ran into a specific, expensive problem. Every request being processed needs a KV cache, a running memory of the attention computations for everything generated so far, and that cache grows as the response gets longer. Older serving approaches often allocated this KV cache memory inefficiently, commonly by reserving one large contiguous block of GPU memory per request upfront, sized for the maximum possible output length, whether or not the request ever actually used that much.
This wasted memory two ways. Most requests never reach the maximum length, so a large share of every reservation sat empty and unusable by anything else. And where a large contiguous block was reserved, memory could become fragmented into unusable gaps over time as requests of different sizes came and went, the same way a hard drive fragments, even when the total free memory was technically enough for a new request. In practice, this meant a GPU that looked full was often only using a fraction of its memory for actual useful work, directly limiting how many concurrent requests a server could handle.
PagedAttention, vLLM's original contribution from its 2023 research paper, borrows an idea directly from how operating systems manage virtual memory. Instead of reserving one large contiguous block per request, it splits each request's KV cache into fixed-size blocks, and those blocks don't need to sit next to each other in physical GPU memory. A block table, one per request, keeps track of which physical blocks make up that request's logical sequence, the same role a page table plays in an operating system.
Three consequences follow directly from this design. First, memory is allocated incrementally, block by block, as a response actually grows, rather than reserved upfront for a worst-case length that may never be reached. Second, fragmentation stops being a problem, since blocks can be scattered wherever physical memory happens to be free rather than needing one large open region. Third, once a request finishes, its blocks are immediately freed and returned to the pool for the next request, rather than sitting reserved and idle.
⚡ How vLLM PagedAttention connects to prefix caching
Because PagedAttention already manages the KV cache as separate, addressable blocks, it makes another optimization possible almost for free: if two different requests happen to share the same starting blocks, like an identical system prompt, those blocks can be reused rather than recomputed. This is the mechanism behind prefix caching. See the packet.ai vLLM prefix caching guide for exactly how that works and what it saves.
The practical result of all this, demonstrated in vLLM's original paper and confirmed repeatedly since, is dramatically higher throughput on the same GPU hardware, since far less memory sits wasted on padding and unused reservations, leaving more room for genuinely concurrent requests.
PagedAttention is the foundational idea, but a modern vLLM deployment includes considerably more. An OpenAI-compatible REST API server means existing code written against a hosted API can often point at a self-hosted vLLM instance with just a base URL change. Continuous batching keeps new requests moving into execution as others finish, rather than waiting for an entire batch to complete together; the packet.ai continuous batching guide covers that mechanism specifically. Support spans quantization formats including FP8, GPTQ, and AWQ, and vLLM runs across a wide range of hardware backends beyond just NVIDIA GPUs, including AMD, Intel, and other accelerators.
vLLM is one of the most widely adopted open-source engines for LLM inference today, but it isn't the only option, and it isn't automatically the best fit for every workload. For a full technical comparison against its closest active competitors, see the packet.ai SGLang vs vLLM vs TensorRT-LLM guide, which covers where each one wins on real workloads.
One comparison worth addressing directly, since it's still a common search: vllm vs huggingface's own serving toolkit, TGI. This isn't really a live rivalry anymore. Hugging Face placed TGI into maintenance mode in December 2025, and archived the project's repository as read-only in March 2026. Hugging Face's own current guidance for anyone still running TGI is to migrate to vLLM or SGLang. If you're evaluating vLLM vs Hugging Face today, the honest answer is that Hugging Face itself has already made that call.
For the actual installation and deployment steps, including working commands, the packet.ai vLLM deployment tutorial and vLLM Docker deployment guide walk through it end to end. This post has focused on what vLLM is and why it works the way it does; those two guides pick up from here with the practical setup.
packet.ai's Token Factory runs on a managed vLLM stack under the hood, so the PagedAttention and continuous batching benefits described above apply without needing to install, configure, or tune vLLM yourself. It's the same serving engine, without the operational side.
Join the waitlist for early access once it opens.
Last reviewed: August 22, 2026. For deploying vLLM step by step, see the vLLM deployment tutorial. For how vLLM compares to other serving engines, see the SGLang vs vLLM vs TensorRT-LLM guide. For the fundamentals this post builds on, see the What Is LLM Inference guide.
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