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].

Core Viewpoint - The article discusses the transformative shift from traditional software programming to AI software modeling, highlighting the implications for processing hardware and the development of dedicated AI accelerators. Group 1: Traditional Software Programming - Traditional software programming is based on writing explicit instructions to complete specific tasks, making it suitable for predictable and reliable scenarios [2] - As tasks become more complex, the size and complexity of codebases increase, requiring manual updates by programmers, which limits dynamic adaptability [2] Group 2: AI Software Modeling - AI software modeling represents a fundamental shift in problem-solving approaches, allowing systems to learn patterns from data through iterative training [3] - AI utilizes probabilistic reasoning to make predictions and decisions, enabling it to handle uncertainty and adapt to changes [3] - The complexity of AI systems lies in the architecture and scale of the models rather than the amount of code written, with advanced models containing hundreds of billions to trillions of parameters [3] Group 3: Impact on Processing Hardware - The primary architecture for executing software programs has been the CPU, which processes instructions sequentially, limiting its ability to handle the parallelism required for AI models [4] - Modern CPUs have adopted multi-core and multi-threaded architectures to improve performance, but still lack the massive parallelism needed for AI workloads [4][5] Group 4: AI Accelerators - GPUs have become the backbone of AI workloads due to their unparalleled parallel computing capabilities, offering performance levels in the range of petaflops [6] - However, GPUs face efficiency bottlenecks during inference, particularly with large language models (LLMs), where theoretical peak performance may not be achieved [6][7] - The energy demands of AI data centers pose sustainability challenges, prompting the industry to seek more efficient alternatives, such as dedicated AI accelerators [7] Group 5: Key Attributes of AI Accelerators - AI processors require unique attributes not found in traditional CPUs, with batch size and token throughput being critical for performance [8] - Larger batch sizes can improve throughput but may lead to increased latency, posing challenges for real-time applications [12] Group 6: Overcoming Hardware Challenges - The main bottleneck for AI accelerators is memory bandwidth, often referred to as the memory wall, which affects performance when processing large batches [19] - Innovations in memory architecture, such as high bandwidth memory (HBM), can help alleviate memory access delays and improve overall efficiency [21] - Dedicated hardware accelerators designed for LLM workloads can significantly enhance performance by optimizing data flow and minimizing unnecessary data movement [22] Group 7: Software Optimization - Software optimization plays a crucial role in leveraging hardware capabilities, with highly optimized kernels for LLM operations improving performance [23] - Techniques like gradient checkpointing and pipeline parallelism can reduce memory usage and enhance throughput [23][24]