When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving
Source
Tanya Lenz
Author
Tanya Lenz
Date
Terms in this piece · Glossary
multimodal — A model that works with more than text — reading images, audio, or video, and sometimes generating them too.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
Why it matters
Gives teams serving multimodalA model that works with more than text — reading images, audio, or video, and sometimes generating them too.Full definition → models concrete numbers and topology choices for when disaggregating the vision encoder is worth the added infrastructure complexity.
Key quotes
“Vision encoding can take hundreds of milliseconds or longer.”
“As shown in Figure 1, 43% of TTFT was spent before ViT even started.”
“Without it, every image looks like the same placeholder token to the router.”
“Encode disaggregation can reduce TTFT and increase same-SLO goodput only in certain scenarios.”
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