
Developed a major architectural enhancement for the LMCache/LMCache repository by integrating MaruBackend to enable zero-copy key-value cache sharing for large language model inference using CXL shared memory. Leveraging Python and asynchronous programming, this work improved inference throughput and reduced memory pressure by allowing efficient memory management across processes. Addressed a memory leak by ensuring correct handling of store() return values, preventing erroneous success on server-side rejection. Enhanced maintainability through targeted documentation updates, including clarifications for batch operations and RPC support, and improved code readability with formatting fixes. The contributions focused on backend development and robust shared memory integration.
In April 2026, delivered a major architectural enhancement to LMCache/LMCache: MaruBackend integration enabling zero-copy KV cache sharing for LLM inference via CXL shared memory, along with memory leak remediation and documentation/code quality improvements. This work improves inference throughput, reduces memory pressure, and enhances maintainability across the codebase.
In April 2026, delivered a major architectural enhancement to LMCache/LMCache: MaruBackend integration enabling zero-copy KV cache sharing for LLM inference via CXL shared memory, along with memory leak remediation and documentation/code quality improvements. This work improves inference throughput, reduces memory pressure, and enhances maintainability across the codebase.

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