AppliedAIPrep logoAppliedAI/Prep
← ML Infrastructure & GPUs / 23
hardNVIDIAOpenAIMicrosoft

What is disaggregated (prefill/decode) serving for LLM inference?

LLM inference has two phases with opposite resource profiles, and co-locating them makes a long prompt stall everyone else's tokens. The signal is knowing why prefill and decode fight, and what separating them costs. Here is the answer.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

LLM inference has two phases with opposite resource profiles, and co-locating them makes a long prompt stall everyone else's tokens. The signal is knowing why prefill and decode fight, and what separating them costs. Here is the answer.

Unlock the other 754 answers · ₹2,000 / $25includes both full courses · progress stays saved · 6 months · one payment · no auto-renew
UP NEXT ON YOUR JOURNEY
Next in this trackWhat do Ray, Horovod, Spark, and Dask do, and when do you use each for distributed ML?Next in this trackHow does batch size affect training (speed, memory, generalization), and how do you scale it?Next in this trackWhat do model-serving frameworks (Triton, TorchServe, vLLM, TGI) provide, and how do you choose?
DISCUSSION · 0

No comments yet — be the first to share your approach.