SGLang has developed a new Pipeline Parallelism (PP) system that addresses the challenges of processing ultra-long contexts in large language models (LLMs). The key innovation lies in integrating Chunked Pipeline Parallelism, Asynchronous Communication, and Dynamic Chunking. This approach allows for impressive performance improvements, achieving a 3.31× Prefill Throughput boost for the DeepSeek-V3.1 model on an H20 cluster when using a 12K chunk size. This performance surpasses the previous TP32 solution by 30.5%. The PP implementation not only enhances throughput but also cuts down Time to First Token (TTFT) by nearly 68% while maintaining strong scaling efficiency above 82%. The article highlights the limitations of existing strategies, specifically Tensor Parallelism (TP) and Context Parallelism (CP). TP tends to suffer from communication bottlenecks due to the need for frequent synchronization across layers, making it less scalable in multi-node scenarios. CP incurs its own penalties through extensive synchronization for Key-Value state aggregation. In contrast, PP minimizes data transfer to just the boundaries of pipeline stages, which significantly reduces communication volume. As a result, PP achieves a nearly order-of-magnitude improvement in communication efficiency compared to TP for large models. While PP has its advantages, it does introduce idle periods, known as pipeline bubbles, where devices wait for data. However, in scenarios with substantial workloads, the bubble ratio becomes negligible compared to the communication benefits. The article asserts that while pure PP configurations aren't always ideal, this new implementation provides a solid framework for scaling trillion-parameter models effectively. The performance metrics presented underscore SGLang's commitment to optimizing model inference for increasingly complex tasks.