Large Language Models (LLMs) have become more prevalent in long-context applications such as interactive chatbots, document analysis, and agent workflows, but it is challenging to serve long-context requests with low latency and high throughput. Speculative decoding (SD) is a widely used technique to reduce latency losslessly, but the conventional wisdom suggests that its efficacy is limited to small batch sizes. In MagicDec, we show that surprisingly SD can achieve speedup even for a high throughput inference regime for moderate to long sequences. More interestingly, an intelligent drafting strategy can achieve better speedup with increasing batch size based on our rigorous analysis. MagicDec first identifies the bottleneck shifts with increasing batch size and sequence length, and uses these insights to deploy SD more effectively for high throughput inference. We leverage draft model with sparse KV cache to address the KV bottleneck, which scales with both sequence length and batch size. Additionally, we propose a theoretical model to select the optimal drafting strategy for maximum speedup. Our work highlights the broad applicability of speculative decoding in long-context serving, as it can enhance throughput and reduce latency without compromising accuracy. For moderate to long sequences, we demonstrate up to 2.51x speedup for Llama3.1-8B when serving batch sizes ranging from 32 to 256 on various types of hardware and tasks.

翻译：大型语言模型（LLM）在交互式聊天机器人、文档分析和智能体工作流等长上下文应用中日益普及，但以低延迟和高吞吐量服务长上下文请求仍具挑战性。推测解码（SD）是一种广泛用于无损降低延迟的技术，但传统观点认为其有效性仅限于小批量处理。在MagicDec中，我们出人意料地发现，对于中长序列的高吞吐量推理场景，SD仍能实现加速。更有趣的是，基于我们的严格分析，智能草拟策略能够随着批量增大获得更好的加速效果。MagicDec首先识别了随批量与序列长度增加的瓶颈转移现象，并利用这些洞见在高吞吐量推理中更有效地部署SD。我们采用具有稀疏KV缓存的草拟模型来解决随序列长度和批量大小同时扩增的KV瓶颈。此外，我们提出了一个理论模型来选择最优草拟策略以实现最大加速。本工作凸显了推测解码在长上下文服务中的广泛适用性——它能在不损失准确性的前提下同时提升吞吐量与降低延迟。针对中长序列，我们在多种硬件和任务上为Llama3.1-8B模型实现了最高2.51倍的加速，服务批量范围覆盖32至256。