解决大模型在云端无服务器推理的冷启动延迟问题
Enabling Efficient Serverless Inference Serving for LLM (Large Language Model) in the Cloud
- 采用多级检查点加载，利用闲置显存与存储加速启动
- 启动时间比现有方法快6到8倍，显著降低延迟
- 适合需要快速响应的大模型服务场景
本文讨论了无服务器推理中的冷启动延迟问题及现有解决方案。重点回顾了ServerlessLLM系统，该系统针对大语言模型在无服务器环境下的冷启动难题提出改进。传统无服务器方式因大型模型检查点和GPU资源初始化开销，导致高延迟。ServerlessLLM引入多层级检查点加载机制，利用未充分利用的GPU内存与存储，使启动时间相比现有方法缩短6至8倍。同时提出实时推理迁移与启动时间优化调度器，实现高效资源分配并最小化延迟。该系统显著提升了无服务器环境下大模型负载的性能与可扩展性。此外，本文还综述了近期研究中的其他方法，如Rainbowcake。进一步探讨了函数即服务（FaaS）提供商应对冷启动的策略及未来发展方向。
原文摘要 · Abstract (English)
This review report discusses the cold start latency in serverless inference and existing solutions. It particularly reviews the ServerlessLLM method, a system designed to address the cold start problem in serverless inference for large language models. Traditional serverless approaches struggle with high latency due to the size of LLM checkpoints and the overhead of initializing GPU resources. ServerlessLLM introduces a multitier checkpoint loading system, leveraging underutilized GPU memory and storage to reduce startup times by 6--8x compared to existing methods. It also proposes live inference migration and a startup-time-optimized model scheduler, ensuring efficient resource allocation and minimizing delays. This system significantly improves performance and scalability in serverless environments for LLM workloads. Besides ServerlessLLM, several other methods from recent research literature, including Rainbowcake, are reviewed in this paper. Further discussions explore how FaaS providers tackle cold starts and the possible future scopes.
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