Pipeline Parallelism in SGLang: Scaling to Million-Token Contexts and Beyond (lmsys.org)

SGLang has unveiled its optimized Pipeline Parallelism (PP) implementation, designed to enhance efficiency in ultra-long context inference, specifically targeting scenarios with up to a million tokens. This innovation incorporates Chunked Pipeline Parallelism, Asynchronous P2P Communication, and a Dynamic Chunking mechanism that not only minimizes latency but also significantly boosts efficiency. In a multi-node deployment, the implementation achieved a remarkable 3.31× increase in Prefill Throughput for the DeepSeek-V3.1 model compared to Tensor Parallelism (TP), showcasing a 67.9% reduction in Time to First Token (TTFT) while maintaining an 82.8% scaling efficiency. The significance of this development lies in its ability to overcome the traditional limitations encountered with pure TP, particularly the communication bottlenecks in large-scale, multi-node environments. By effectively managing the communication volume and reducing idle periods known as pipeline bubbles, SGLang's PP architecture provides a robust pathway for scaling trillion-parameter models. This optimized approach not only enhances the throughput and efficiency of processing long-context prompts but also establishes a versatile open-source framework that can be integrated with existing parallel strategies, making it a critical advancement for the AI/ML community focused on large language models.