Core Insights - The article discusses the rapid development of large model applications and the focus on making inference faster and more efficient, highlighting the emergence of vLLM as a high-performance inference framework specifically optimized for large language models [1][4]. Inference Engine Basics - The vLLM framework includes fundamental processes such as input/output request handling, scheduling, paged attention, and continuous batching [4]. - Advanced features of vLLM include chunked prefill, prefix caching, guided decoding, speculative decoding, and decoupled prefill/decoding [4]. Performance Measurement - The performance of the inference system is measured through metrics such as latency (including time to first token, iteration latency, end-to-end latency, and throughput time) and throughput, along with GPU performance roofline models [4]. Architecture and Components - The LLM engine is the core module of vLLM, capable of achieving high throughput inference in offline scenarios [8]. - Key components of the engine include the engine core, processor, output processor, model executor, and scheduler, each playing a critical role in the inference process [15][16]. Scheduling Mechanism - The scheduling mechanism prioritizes decode requests over prefill requests, allowing for more efficient processing of inference tasks [38][39]. - The vLLM V1 scheduler can intelligently mix prefill and decode requests within the same step, enhancing overall efficiency [39]. Advanced Features - Chunked prefill allows for processing long prompts by breaking them into smaller chunks, preventing resource monopolization [57]. - Prefix caching avoids redundant computations for shared tokens across multiple prompts, significantly speeding up prefill requests [69][73]. Guided and Speculative Decoding - Guided decoding utilizes a finite state machine to constrain logits based on grammar rules, ensuring only syntactically valid tokens are sampled [93][95]. - Speculative decoding introduces a draft model to quickly generate candidate tokens, reducing the time required for each forward pass in autoregressive generation [106][110]. Distributed System Deployment - vLLM can be deployed across multiple GPUs and nodes, utilizing tensor and pipeline parallelism to manage large models that exceed single GPU memory limits [146][150]. - The architecture supports both data parallelism and load balancing, ensuring efficient handling of incoming requests [130][156].