WOSS
vLLM: How Open Source Serves LLMs at Scale
Aman Mundra · 2026-07-10 · 4 min read
Contents
TL;DR - vLLM is a high-throughput, memory-efficient inference and serving engine for large language models. Born at UC Berkeley's Sky Computing Lab, it is best known for PagedAttention - a KV-cache scheme that dramatically raises throughput by letting many requests share GPU memory efficiently - and for its OpenAI-compatible server. It joined the PyTorch Foundation in 2025, re-architected its core into the V1 engine, and now runs across nearly every AI accelerator on the market. It is the serving layer of the cloud-native AI stack.
Training a language model gets the headlines. Serving it - answering millions of requests efficiently, without a GPU bill that sinks the product - is the harder engineering problem in practice, and it is the one vLLM set out to solve. It solved it well enough to become the default open-source answer.
The problem vLLM solves: memory
When an LLM generates text, it keeps a KV cache - the attention keys and values for every token processed so far. That cache is large, it grows with every token, and it is the real bottleneck in LLM serving. Naive implementations reserve a big contiguous block of GPU memory per request, and most of it sits wasted, which sharply limits how many requests a GPU can handle at once.
vLLM's breakthrough, PagedAttention, borrows an idea from operating systems: virtual memory and paging. Instead of one contiguous reservation per request, the KV cache is split into small blocks that can live anywhere in GPU memory, allocated on demand. The waste largely disappears, and a single GPU can suddenly serve far more concurrent requests. That one insight is most of why vLLM took over open-source inference.
On top of PagedAttention, vLLM adds the features that make it production-grade:
- Continuous batching - new requests join the running batch immediately instead of waiting for a fixed batch to finish, keeping the GPU busy.
- An OpenAI-compatible server - existing clients point at a self-hosted vLLM endpoint with minimal changes, which made adoption nearly frictionless.
- Broad model, quantization, and hardware support - across a huge range of open models and precision formats.
The V1 engine and open governance
vLLM has not stood still. Its core was re-architected into the V1 engine - a cleaner, modular design that splits request handling from model execution into separate processes communicating over fast IPC. The practical effect is higher throughput with near-zero CPU overhead (tokenization, streaming, and multimodal processing overlap with the GPU execution loop instead of blocking it), and optimizations like chunked prefill and speculative decoding enabled by default. It is faster, especially for long-context workloads, and deliberately easier to hack on.
Just as important as the code is the governance. UC Berkeley contributed vLLM to the Linux Foundation in 2024, and in 2025 it was adopted as a hosted project of the LF's PyTorch Foundation - so it now has neutral, long-term stewardship rather than depending on any single company. That matters when a project becomes critical infrastructure: the serving layer that a large slice of the open-model world depends on is community-governed. Its reach reflects that status - vLLM runs natively across NVIDIA and AMD GPUs, AWS Inferentia and Trainium, Google TPUs, Intel Gaudi, IBM's Spyre accelerator, and Apple Silicon. Its momentum is such that its creators launched a startup, Inferact, in early 2026 to commercialize it - while the project itself stays open.
Where vLLM fits
vLLM is the inference layer of a modern AI stack: the piece that actually runs the model behind your product. It pairs naturally with the agentic and cloud-native side - a useful way to remember the split is kagent runs the agents, vLLM serves the models behind them. In a cloud-native AI deployment, vLLM is typically the serving workload running on Kubernetes, sized and scaled like any other production service, with the difference that its efficiency directly sets your inference cost.
Getting involved
vLLM is a large, fast-moving, high-visibility project, which makes it both a demanding and a rewarding place to contribute. The surface spans model support, kernels and quantization, scheduling and memory (PagedAttention) improvements, hardware backends, and the documentation, benchmarks, and tests that are legitimate first contributions given the codebase's complexity. Triage on the very active issue tracker is high-leverage work. Build from source and pin versions - it moves quickly.
