NEWS · MODELS · #79
When to Use Encode-Prefill-Decode (EPD) Disaggregation to Speed Multimodal Model Serving
An article on NVIDIA Developer describes encode-prefill-decode (EPD) disaggregation, an inference optimization that separates the vision-encoder stage from prefill/decode stages for multimodal models. It outlines scenarios, trade-offs and implementation considerations for using EPD to improve throughput and latency in model serving.
KEY POINTS
- An article on NVIDIA Developer describes encode-prefill-decode (EPD) disaggregation, an inference optimization that separates the vision-encoder stage from prefill/decode stages for multimodal models.
- It outlines scenarios, trade-offs and implementation considerations for using EPD to improve throughput and latency in model serving.
- Guidance on EPD matters because separating encoder and decoding stages can materially affect latency, throughput and resource utilization when deploying multimodal AI systems.
WHY IT MATTERS
Guidance on EPD matters because separating encoder and decoding stages can materially affect latency, throughput and resource utilization when deploying multimodal AI systems.