Efficient Multimodal Inference
Overview
Efficient multimodal inference studies how to make models process mixed inputs with less computation, memory, latency, and energy. Common approaches prune or compress visual and audio tokens, select useful video frames or input modalities, compress KV caches, and adapt reasoning depth to query difficulty. Systems work also schedules inference across heterogeneous GPUs and edge devices, with evaluations spanning image and video question answering, long-context tasks, and real-time applications. Many methods preserve accuracy at substantial compression, but an open tension is whether they retain fine-grained evidence and grounding across tasks while improving end-to-end latency and cost.
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