Introduction to Disaggregated Inference: Why It Matters
The explosive growth of generative AI has placed unprecedented demands on GPU infrastructure. Enterprises and GPU cloud providers are deploying large language models at scale, but the underlying inference serving architecture often can't keep up.
As context windows grow into the millions of tokens, as reasoning models think longer before responding, and as agentic workflows chain multiple model calls together, a fundamental architectural shift is taking hold: disaggregated inference.
In this first blog post on disaggregated inference, we will discuss how it differs from traditional serving, why it matters for platform teams managing GPU infrastructure, and how the ecosystem—from NVIDIA Dynamo to open-source frameworks—is making it production-ready.
