Inference
Disaggregated Serving
An LLM serving architecture that runs the prefill and decode phases on separate GPUs and passes the KV cache between them.
In depth
An LLM serving architecture that runs the prefill and decode phases on separate GPUs and passes the KV cache between them. Running both phases on the same hardware causes interference, where long prefills stall in-progress decodes, and forces one parallelism plan onto two very different workloads. The DistServe paper showed that splitting them lets each pool be sized for its own target, time to first token for prefill and time per output token for decode, serving up to 7.4x more requests within those limits.
Primary source: arxiv.org
Example
In practice, developers reach for Disaggregated Serving when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Disaggregated Serving?
An LLM serving architecture that runs the prefill and decode phases on separate GPUs and passes the KV cache between them.
Why does Disaggregated Serving matter for AI developers?
Disaggregated Serving sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Disaggregated Serving?
Developers Digest publishes tutorials and videos that cover Inference topics including Disaggregated Serving. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Decode Phase
The second phase of LLM inference, where the model generates output tokens one at a time, each step reading the KV cache built so far and adding to it.
Inference
Speculative Decoding
An inference technique where a small, fast draft model proposes several tokens ahead and the large target model checks them all in one parallel forward pass.
Inference
Prefill
The first phase of LLM inference, where the model processes the entire input prompt in parallel and builds the KV cache before producing any output.
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