This research introduces a novel framework that significantly improves how large multimodal models (LMMs) are deployed and served in production environments.

Key Innovations:

• EPD Disaggregation - Separates encoding, prefill, and decode stages to optimize resource allocation
• Dramatically improves time to first token (TTFT) and end-to-end throughput (E2ETP) metrics
• Reduces computational and memory overhead in multimodal encoding
• Enables more efficient serving of multimodal AI systems at scale

For engineering teams, this framework provides a practical solution to the resource bottlenecks currently limiting multimodal AI deployment, allowing for more responsive and cost-effective systems.

Efficiently Serving Large Multimodal Models Using EPD Disaggregation