May 2026: Delivered DeepEP Waterfill-based routing optimization and EPLB mapping fixes in the sgLang project, with targeted test coverage to ensure correctness and stability. The work focused on consolidating waterfill load balancing for shared dispatch, enabling Waterfill support in TopK/HashTopK, and refining shared expert fusion to reduce redundant computation, improving routing efficiency and latency. Additionally, EPLB mapping correctness was addressed with a new test validating biased TopK mapping. Overall, these changes improve performance, reliability, and maintainability of the dispatch and routing subsystem.

2026-04 Monthly Summary Key features delivered - bytedance-iaas/sglang: Mixture of Experts: Fuse shared experts into MoE dispatch under DeepEP to improve routing efficiency and management in distributed settings. Commit: 57ffc55fb647bfc241d8c4766b846f4243b9c81d (feat: [1/2] [DeepEP] Fuse shared expert into MoE dispatch under EP). Co-authored by Claude Sonnet 4.6 and AichenF. Major bugs fixed - sgl-project/sglang: Robust EPLB Dispatch for Shared Experts Fusion. Fixed out-of-bounds in EPLB dispatch when shared experts fusion is enabled; restrict remapping to routed expert columns to prevent crashes and incorrect routing, improving model stability and reliability. Commit: 3cb3f7c01814c90f3f4aacde83f6f2cfcd20ed35 (fix: EPLB dispatch OOB under DeepEP). Overall impact and accomplishments - Stabilized MoE routing under DeepEP across distributed settings; enables safer experimentation with shared-experts at scale; improved routing efficiency, reliability, and maintainability for MoE features. Technologies/skills demonstrated - Mixture of Experts (MoE), DeepEP, EPLB; distributed systems thinking; performance and stability focus; collaborative development and code review; cross-repo feature integration.

March 2026: Two high-impact feature deliveries across sgLang and FlashInfer that improved inference performance and memory efficiency for modern GPU workloads. Implemented K-last SSM layout support for GDN prefill/decode, and introduced pool-indexed (zero-copy) state access for the GDN decode kernel, enabling efficient integration with SGLang's state pool. These changes reduce latency, boost throughput for linear-attention models, and strengthen production readiness for SGLang+FlashInfer deployments on Hopper-era GPUs.

February 2026 performance snapshot focused on low-level performance optimizations and kernel fusion to boost inference throughput and scalability in FlashInfer and SGLang. The work emphasizes reducing CPU-GPU overhead and consolidating kernel launches for critical paths.

June 2025 monthly summary for kvcache-ai/sglang: Delivered FP8-optimized DeepGEMM integration into the EPMoE path, including new Triton kernels for data reordering and computation and a forward-pass refactor to streamline FP8 data paths. This work establishes a robust FP8 data-path foundation and sets the stage for targeted performance tuning; no major bugs fixed this period.

May 2025 monthly summary for kvcache-ai/sglang: Major bug fix to MoE forward pass memory safety and correctness, addressing illegal memory access and preventing potential out-of-bounds errors. The fix enhances stability for expert-parallel MoE forwards under large-scale workloads and improves reliability of production deployments.

March 2025 monthly summary focused on performance optimization for DeepEP Mixture-of-Experts in kvcache-ai/sglang. Delivered a permute kernel optimization by refactoring Triton kernels and adjusting data flow for expert processing, optimizing permutation and un-permutation steps. This work enhances throughput and reduces latency in Mixture-of-Experts routing and data distribution.