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.