At the recent KVCache Meetup Korea, co-hosted by PyTorchKR and XCENA, speakers from XCENA, SK hynix, FuriosaAI, and LMCache dove deep into the architectural trade-offs powering large language model serving. Talks spanned CXL memory pooling with Maru, roofline analysis of GPU compute and HBM bandwidth during prefill and decode, batch scheduling on RNGD, and process-isolated cache serving with LMCache MP Mode. Real-world optimization requires collaboration across every tier of the stack, from accelerators and memory architectures to runtime engines. Thank you to PyTorch Foundation Ambassador Junghwan Park, co-host XCENA, and sponsor @tensormesh for bringing this local developer community together. Catch the full event recap here: