In the rapidly evolving landscape of AI, generative LLMs stand at the forefront, revolutionizing how we interact with our data. However, the computational intensity and memory consumption of deploying these models present substantial challenges in terms of serving efficiency, particularly in scenarios demanding low latency and high throughput. This survey addresses the imperative need for efficient LLM serving methodologies from a machine learning system (MLSys) research perspective, standing at the crux of advanced AI innovations and practical system optimizations. We provide in-depth analysis, covering a spectrum of solutions, ranging from cutting-edge algorithmic modifications to groundbreaking changes in system designs. The survey aims to provide a comprehensive understanding of the current state and future directions in efficient LLM serving, offering valuable insights for researchers and practitioners in overcoming the barriers of effective LLM deployment, thereby reshaping the future of AI.
The paper provides a comprehensive survey of strategies for efficient serving of generative LLMs, focusing on both algorithmic innovations and system-level optimizations to address computational and memory challenges.
It categorizes LLM serving techniques into algorithmic innovations (such as decoding algorithms and model compression) and system optimizations (like low-bit quantization and parallel computation) for reducing latency and improving throughput.
Several open-source LLM serving systems are compared, and future research directions are proposed, including hardware co-design, advanced decoding algorithms, and deployment in diverse environments.
The paper titled "Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems," authored by Xupeng Miao et al., from Carnegie Mellon University, offers a detailed exploration of efficient serving methodologies for generative LLMs. This overview aims to summarize key insights, methodologies, and findings discussed in the paper, catering to an audience of experienced researchers in the domain.
Generative LLMs, fueled by Transformer-based architectures like GPT, LLaMA, and others, have advanced significantly, showcasing superior performance across various NLP tasks. Despite their success, the serving of these models poses profound computational and memory challenges, particularly concerning low-latency and high-throughput requirements in practical applications. The paper methodically addresses these challenges by exploring algorithmic modifications and system-level optimizations.
The paper categorizes the strategies for efficient LLM serving into two primary classes: Algorithmic Innovations and System Optimizations. This structured approach highlights the diverse methodologies aimed at optimizing LLM inference.
Decoding Algorithms:
Architecture Design:
Model Compression:
Low-bit Quantization:
Parallel Computation:
Memory Management:
Request Scheduling:
Kernel Optimization:
The paper also presents a comparative analysis of several cutting-edge open-source LLM serving systems, such as FasterTransformer, vLLM, and TensorRT-LLM, along with their specific optimizations and areas of focus. These frameworks encapsulate various algorithmic and system-level techniques discussed, serving as practical implementations for efficient LLM deployment.
The paper acknowledges the ongoing evolution of LLM technologies and proposes several future research directions:
This comprehensive survey by Miao et al. provides valuable insights into the current methodologies and future directions for efficient generative LLM serving. By systematically analyzing both algorithmic and system-level strategies, the paper offers a robust foundation for ongoing research and development, aimed at overcoming the inherent challenges of deploying large-scale language models in real-world applications. The continuous integration of these optimizations will be pivotal in enhancing system performance, facilitating broader accessibility and practical use of advanced AI technologies.