Vision
When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving
Our short summary of NVIDIA’s engineering write-up. The full story, with all the detail, is on their blog.
Read the full write-up on developer.nvidia.com (opens in a new tab)Problem
Vision encoding blocks multimodal requests, causing slow first-token latency and hurting throughput.
Solution
They separate the vision encoder from LLM prefill/ decode into independent workers (EPD disaggregation), letting each stage batch and scale alone, which speeds up token generation.
Up to 5x faster TTFT and 7x faster end‑to‑end response
Research behind this, on AI Papers DailyFastAFD: When Does Attention-FFN Disaggregation Pay on Rack-Scale NVLink?
Want the architecture, code and numbers? They’re in the original post.
Read the full write-up on developer.nvidia.com (opens in a new tab)