May 2026 monthly summary for yhyang201/sglang focusing on speculative decoding performance optimization. This month delivered improvements by refining page allocation logic and removing unnecessary calculations to reduce CPU overhead and improve runtime performance for speculative decoding.

April 2026: Delivered targeted features and stability improvements across sglang repositories, driving higher model output quality, memory efficiency, and deployment flexibility. Key outcomes include enabling log probabilities for accepted tokens in MultiLayerEagleWorkerV2, optimizing Mamba tracking indices for faster scheduler decisions, and improving spec decoding memory allocation accuracy. Major bug fixes tackled stability and correctness in MultiLayerEagleDraftWorker (token pool management and CUDA graph runner handling) and resolved critical issues in Mamba tracking calculations. The work resulted in more reliable sampling, reduced memory waste, and broader ARM device support for performance monitoring, enabling higher throughput and predictable deployments. Technologies demonstrated include CUDA graph handling, IPC/disk-based weight updates, memory estimation, and ARM portability.