In this article
vLLM introduced PagedAttention — a KV-cache management technique that allocates attention cache in non-contiguous memory blocks (similar to virtual memory paging), eliminating fragmentation and enabling 24x higher throughput vs naive inference. vLLM supports: continuous batching (no padding waste), speculative decoding (draft + verify for 2-3x speedup), tensor/pipeline parallelism, prefix caching (reuse shared prompt prefixes), and most open-source models (Llama, Mistral, Qwen, Falcon). It is the de facto standard for self-hosted LLM serving in production.
What it means in practice
vLLM is not just vocabulary; it is a design handle. In AI engineering, this term usually appears when engineers are designing, reviewing, or troubleshooting real production flows rather than only naming the concept. It helps teams choose the right model/runtime balance across quality, speed, memory, governance, and cost.
Why engineers care
- It gives teams a shared name for the behaviour, risk, or architecture choice being discussed.
- It helps separate the goal from the implementation detail, so you can compare alternatives instead of copying a tool pattern blindly.
- It creates a useful checklist for reviews: inputs, outputs, failure modes, ownership, cost, latency, and measurement.
Production watch-outs
Benchmarks are only a starting point. Validate with your prompts, data, latency budget, concurrency pattern, and safety requirements.
Related context
Useful neighbouring concepts: Model Serving, Inference, Quantisation.