vLLM

vLLM is an open-source inference engine optimized for high-throughput, low-latency serving of large language models. It implements PagedAttention, a memory management technique that reduces VRAM fragmentation and enables efficient batching. Operators encounter vLLM when deploying models for production or multi-user scenarios where throughput matters more than single-request latency. It supports continuous batching, speculative decoding, and tensor parallelism across multiple GPUs. vLLM is commonly used with Hugging Face models and can serve OpenAI-compatible APIs.

vLLM's core innovation is PagedAttention, which manages key-value (KV) cache memory in fixed-size blocks (pages) rather than contiguous chunks. This eliminates fragmentation and allows sharing of KV cache across requests in a batch, dramatically increasing throughput. vLLM also supports prefix caching (reusing KV cache for common prefixes), chunked prefill (splitting long prompts to reduce latency), and various quantization methods (AWQ, GPTQ, FP8). It integrates with Hugging Face Transformers and can be deployed via Docker or directly. For operators, vLLM is the go-to choice when serving models to multiple users simultaneously, as it can achieve 10-20x higher throughput than naive implementations. However, it requires more VRAM per request due to the page table overhead, and setup is more complex than Ollama or LM Studio.

An operator serving Llama 3.1 70B to 10 concurrent users on a single A100 80GB GPU would use vLLM with tensor parallelism disabled. With continuous batching, vLLM can achieve ~2000 tokens/second total throughput, while a naive implementation would bottleneck at ~200 tokens/second. The operator would set --max-num-seqs 256 and --gpu-memory-utilization 0.95 to maximize VRAM usage.

To serve a model with vLLM, an operator runs: vllm serve meta-llama/Llama-3.1-8B-Instruct --dtype auto --api-key token-abc123. This starts an OpenAI-compatible API server on port 8000. The operator then sends requests via curl or a client library. For multi-GPU setups, they add --tensor-parallel-size 2. vLLM logs show request throughput, average latency, and GPU memory usage.