vLLM Deep Dive — Architecture, Features, and Production Best Practices
Introduction vLLM is an open-source, production-focused inference engine for large language models (LLMs) that prioritizes high throughput, low latency, and efficient GPU memory usage. This post provides a deep technical dive into vLLM’s architecture, core innovations (especially PagedAttention), quantization and model support, scheduling and batching strategies, distributed and multi-GPU operation, practical deployment patterns, benchmarks and trade-offs, and troubleshooting tips for production systems. Table of contents Introduction What is vLLM and when to use it Core innovations PagedAttention and KV memory management Micro-batching and continuous batching Kernel and CUDA optimizations Model support and quantization Supported model families and formats Quantization: GPTQ, AWQ, INT4/INT8/FP8 Scheduling, batching, and token routing Multi-GPU and distributed inference Tensor and pipeline parallelism MoE and expert routing considerations Integration and developer experience Hugging Face and OpenAI-compatible APIs Example: simple Python server invocation Production deployment patterns Cost and utilization considerations Scaling strategies and failure isolation Benchmarks, comparisons, and trade-offs vLLM vs alternatives (TensorRT‑LLM, LMDeploy, SGLang, Transformers) Common issues and operational tips Conclusion What is vLLM and when to use it vLLM is a high-performance inference engine designed to serve transformer-based LLMs with high concurrency and long context windows while keeping GPU memory usage efficient. Use vLLM when you need to serve many concurrent users or large contexts with good throughput, when you want easy integration with Hugging Face models, and when maximizing GPU utilization (through micro-batching and efficient KV caching) is a priority[4][1]. ...