When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving
Mirrored from NVIDIA Developer Blog for archival readability. Support the source by reading on the original site.
Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill...
Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill and decode stages. It is most effective for image-heavy prompts, short-to-medium outputs, and quantized mixture-of-experts (MoE) models. This post shows when and how to use EPD disaggregation with NVIDIA Dynamo to achieve up to 5x…
More from NVIDIA Developer Blog
-
How to Rewrite Apache Spark UDFs for GPU Acceleration with AI Agents
Oct 13
-
NVIDIA Run:ai Is Now Open Source as Part of NVIDIA DSX AI Factory Platform
Oct 13
-
ReplaySSM: Rethinking State Storage for Faster Hybrid-Model Decoding
Oct 12
-
5 Steps to Create SimReady Assets for Robotics with Frontier AI Models
Oct 8
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.