Hardware
When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving
Encode‑Prefill‑Decode (EPD) disaggregation is an inference optimization in NVIDIA Dynamo that separates vision encoding from LLM prefill–decode stages for multimodal requests. On image‑heavy prompts,
Encode‑Prefill‑Decode (EPD) disaggregation is an inference optimization in NVIDIA Dynamo that separates vision encoding from LLM prefill–decode stages for multimodal requests. On image‑heavy prompts, short outputs or quantized Mixture‑of‑Experts models it can cut time‑to‑first‑token by up to 5× and end‑to‑end response time by 7×, with performance tied to traffic mix, model size, and precision. Three deployment topologies are available—aggregated, colocated, or disaggregated encoder workers—while best practices include quantizing LLM weights to NVFP4, caching embeddings, and using multimodal KV routing.
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Source: NVIDIA Developer | 2026-09-09