LMCache's multimodal support feature optimizes the memory footprint of visual-linguistic models:
- Enable multimodal caching: Set in the vLLM configuration
mm_hashesparameter to identify the image token - hierarchical storage: store key-value pairs of visual features to disk or Redis, with the text portion retained in the GPU
- Batch optimization: Batch caching of similar image queries
- Monitoring Tools: Check the effectiveness of memory optimization using the performance analysis tool provided by LMCache
This approach significantly reduces GPU memory usage for multimodal inference while maintaining high responsiveness. It is recommended to refer to the official LMCache-Examples repository for examples of multimodal implementations.
This answer comes from the articleLMCache: A Key-Value Cache Optimization Tool for Accelerating Reasoning on Large Language ModelsThe
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