For 2025-12, delivered Mooncake Transfer Engine as a new kv_connector in jeejeelee/vllm to boost inference efficiency in distributed environments. The feature integrates with existing KV transfer mechanisms, enabling high-speed data handling and caching, and includes a usage guide to support adoption. No major bugs were reported or fixed this period. Overall impact: improved throughput and latency for distributed LLM inference, enabling scalable deployments and better resource utilization. Technologies demonstrated: distributed systems design, high-performance KV data paths, codebase integration, documentation, and collaboration (sign-offs).

November 2025 monthly summary for kvcache-ai/sglang: delivered a critical bug fix in the Priority Scheduling component to correct the running_bs calculation, ensuring accurate batch processing and more reliable scheduling. The change reduces mis-scheduling risk and improves throughput consistency across workloads.

September 2025 monthly summary focusing on key accomplishments for the Mooncake repository. Delivered a scalable External KV Connector for vLLM v1 enabling distributed KV cache management across scheduler and worker roles. Introduced a new Python module mooncake_connector_v1.py, updated the build system (CMakeLists.txt) to include the new component, and published usage guidance in a dedicated README. This work enhances scalability, performance, and operational consistency for vLLM workloads, reducing cache coordination bottlenecks and simplifying deployment.