February 2026 (2026-02) monthly summary for kvcache-ai/sglang. Key features delivered: Tensor Parallel (TP) now reuses the transformer's Shared Parallel (SP) group to improve resource sharing and efficiency during training and inference; Server-Side Video Output Saving added to reduce tensor transfer overhead and streamline workflows; Sequence Sharding enabled for multimodal and sequence-sharded models with configuration options and tensor-dimension adjustments to boost parallel processing; Parallel Decoding for WanVAE implemented to enhance efficiency and scalability for multimodal generation. Major bugs fixed: Cache-Refresh Bug in Server Cache-DIT fixed by adding transformer context refresh for single and dual transformers to ensure correct cache updates under dynamic requests; GPU Memory Management Bug under Distributed Init addressed redundant memory usage on GPU-0 by adding device ID checks, optimizing memory usage in distributed setups. Overall impact and accomplishments: Delivered notable performance and reliability gains across distributed inference and training—reduced cache invalidation risk, lowered memory waste on GPU-0, and increased throughput through shared parallelism, server-side outputs, and advanced sequence processing. Technologies/skills demonstrated: distributed inference/training, memory management optimization, transformer-based architectures, tensor and sequence parallelism, and data-plane enhancements (server-side video saving, parallel decoding).

Concise monthly summary for January 2026 for repository kvcache-ai/sglang, focusing on delivered features, impact, and technical achievements.

April 2025 focused on performance optimization of prefix hash computation and metadata corrections for LMCache/LMCache. The changes improve throughput for chunked data processing, enhance reliability through mask-alignment assertions, and correct kv_shape metadata descriptions.