If you\u2019ve ever wondered why your AI chatbot suddenly slows down when you feed it a massive 50-page PDF, you\u2019ve encountered a fundamental bottleneck in modern AI infrastructure.<\/p>\n\n\n\n
For years, we\u2019ve served LLMs like a one-person kitchen: the same chef (GPU) does all the prep work and<\/em> all the cooking. But as companies start deploying models at massive scales, we\u2019re moving to a restaurant model: the Disaggregated Inference<\/strong> model.<\/p>\n\n\n\n <\/p>\n\n\n\n Disaggregated inference<\/strong> is an AI serving architecture in which different stages of model inference\u2014such as request routing, prompt processing (prefill), KV-cache storage, and token generation (decode)\u2014are executed on separate hardware resources or services rather than on the same accelerator or server.<\/p>\n\n\n\n <\/p>\n\n\n\n Disaggregated inference is primarily a datacenter-scale architecture. While it can be implemented on multi-GPU workstations or specialized local systems, the performance and operational benefits are usually too small to justify the added complexity for a single-user PC. Its biggest advantages emerge when serving many concurrent users, where separating prefill and decode workloads improves utilization, fairness, and throughput.<\/p>\n\n\n\n <\/p>\n\n\n\n To understand disaggregation, you have to realize that generating text happens in two very different stages:<\/p>\n\n\n\nThe Anatomy of an LLM: Prefill vs. Decode<\/h3>\n\n\n\n
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