MPIC introduces a novel position-independent caching system that dramatically improves efficiency in multimodal AI inference, especially for applications with interleaved text and images.

• Eliminates redundant computations by enabling flexible reuse of cached content regardless of position
• Particularly valuable for multimodal retrieval-augmented generation workflows
• Achieves significant speedups in serving multimodal large language models
• Reduces computational overhead when handling similar prompts with different prefixes

This engineering breakthrough directly enhances real-world MLLM deployment by addressing a critical bottleneck in multimodal serving platforms, making advanced AI applications more practical and cost-effective to operate at scale.

MPIC: Position-Independent Multimodal Context Caching System for Efficient MLLM Serving