LMCache is particularly suitable for the following three typical application scenarios:
- interactive question and answer system: By caching key-value pairs in the history of a conversation, it can significantly reduce double-counting when users ask consecutive questions involving the same context (e.g., customer service bots).
- Retrieval Augmentation Generation (RAG): Key-value pairs of cached document encodings can be quickly responded to for similar queries on knowledge base documents, typical examples include enterprise intelligent search and document quiz systems.
- multimodal inference: For visual-verbal hybrid models, caching key-value pairs of image features and text features at the same time effectively reduces GPU memory usage (e.g., medical image report generation scenarios).
According to official tests, in scenarios where the input token repetition rate exceeds 30%, LMCache can usually bring more than 5 times throughput improvement.
This answer comes from the articleLMCache: A Key-Value Cache Optimization Tool for Accelerating Reasoning on Large Language ModelsThe
May not be reproduced without permission:AI productivity tools " In what specific scenarios is LMCache best optimized?
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