vLLM is a high-throughput, memory-efficient inference engine developed by UC Berkeley's Sky Computing Lab that has become the industry standard for production LLM deployments. The core innovation is PagedAttention, a revolutionary memory management technique that achieves 14-24x higher throughput than traditional serving methods by eliminating memory fragmentation. This technology enables continuous batching, allowing new sequences to be added immediately as others complete, maximizing GPU utilization. vLLM provides full OpenAI API compatibility, making it a seamless drop-in replacement for cloud-based services without code changes.

Key features include multi-GPU support via tensor parallelism, wide model compatibility (LLaMA, Mistral, Mixtral, Qwen, Phi, Gemma), and advanced capabilities like LoRA adapters and prefix caching. The engine excels in production scenarios requiring high concurrency, cost optimization, and Kubernetes deployments. While alternatives like Ollama offer simpler setup for development, vLLM's performance advantages make it essential for serving production APIs to many concurrent users.

The 4x memory efficiency from PagedAttention translates directly to infrastructure cost savings, enabling organizations to serve the same traffic with fewer GPUs.