vLLM

What is vLLM?

vLLM is a high-performance open-source library for serving large language models — the inference engine that turns a trained model into a fast, efficient API endpoint. Its claim to fame is dramatically improving throughput (how many requests/tokens you can serve per GPU per second) compared to naive serving, which directly lowers the cost of running LLMs in production. It has become one of the most widely-adopted inference servers precisely because inference — not training — is where most organizations spend their GPU budget over a model’s lifetime, and vLLM makes that spend go much further.

PagedAttention — the core idea

vLLM’s central innovation is PagedAttention, and understanding it explains why vLLM is fast. LLM inference is dominated by the KV cache — the stored keys and values for all tokens processed so far, which grows with sequence length and must live in GPU memory. Traditional serving allocated KV-cache memory in large contiguous blocks per request, which wasted enormous amounts of GPU memory to fragmentation and over-reservation (you reserve for the maximum possible length, but most requests are shorter). PagedAttention borrows the idea of virtual memory and paging from operating systems: it splits the KV cache into small fixed-size blocks that can be stored non-contiguously and allocated on demand, near-eliminating the wasted memory. The payoff is large: far less memory waste means you can fit many more concurrent requests on the same GPU, and it enables efficient memory sharing across requests (e.g., multiple requests sharing a common prompt prefix share those KV blocks instead of duplicating them). More concurrent requests on a GPU means higher throughput and lower cost per token — the whole ballgame in production inference.

How it fits, and what to weigh

vLLM pairs PagedAttention with continuous batching (dynamically adding and removing requests from the running batch as they arrive and finish, instead of waiting to assemble static batches — keeping the GPU busy), plus optimized kernels, quantization support, and an OpenAI-compatible API server that makes it a near-drop-in serving layer. In the serving landscape it’s the popular open-source, self-hosted choice; alternatives and complements include NVIDIA Triton (a general multi-framework serving platform, often paired with TensorRT-LLM for maximum optimized performance on NVIDIA hardware), Hugging Face TGI, and the fully-managed inference endpoints from cloud and model providers (where you trade control and cost-efficiency for zero operational burden). The tradeoffs to weigh: vLLM is self-hosted, so you own the GPU infrastructure, capacity planning, scaling, and operational complexity — you get excellent cost-efficiency and control at the price of running it yourself, the classic build-vs-buy decision applied to inference. Throughput-oriented optimizations also involve latency tradeoffs (batching favors aggregate throughput, which can affect single-request latency), so tuning for your workload’s latency-vs-throughput priorities matters. And realized performance depends heavily on hardware, model size, quantization, and request patterns — the headline throughput numbers assume favorable conditions. The recurring mistakes: assuming self-hosting vLLM is automatically cheaper than managed APIs without accounting for GPU costs and operational effort, and not tuning batching/memory settings for the actual latency requirements.

What people get wrong

• Underestimating operational cost — vLLM is self-hosted; you own the GPUs, scaling, and ops, so “cheaper than a managed API” only holds if you actually run it efficiently at good utilization.
• Ignoring the latency/throughput tradeoff: batching maximizes throughput but can raise single-request latency — tune for whether your workload prioritizes cost-per-token or response time.
• Expecting headline throughput unconditionally — real gains depend on model, hardware, quantization, and traffic shape; PagedAttention helps most when memory/concurrency is the bottleneck.

Primary source: vLLM documentation