Large language models (LLMs) are accelerating in capability—but their infrastructure is falling behind. Despite massive advances in generative AI, current serving architectures are inefficient at inference time, especially when forced to handle highly asymmetric compute patterns. Disaggregated inference, the separation of input processing and output generation, offers a hardware-aware architecture that can dramatically improve performance, […] The post Disaggregating LLM Infrastructure: Solving the Hidden Bottleneck in AI Inference appeared first on IEEE Computer Society.