MEDA introduces an intelligent approach for managing memory in multimodal large language models, dramatically improving efficiency for processing long text, images, and videos.

• Adapts memory allocation based on attention patterns across neural network layers
• Achieves 1.75-2.17x speedup for long-context inference without accuracy loss
• Eliminates the need for uniform or progressive reduction strategies common in existing methods
• Enables resource-efficient processing of complex multimodal inputs

This advancement directly addresses critical engineering challenges in deploying multimodal AI systems at scale, where memory constraints often limit practical applications in real-world scenarios.

MEDA: Dynamic KV Cache Allocation for Efficient Multimodal Long-Context Inference