LMCache is an open source key-value (KV) caching tool optimized for reasoning about Large Language Models (LLMs), and its core features include:
- Key-Value Cache Reuse: By caching the intermediate computation results (key-value pairs) of LLMs, avoiding repeated computation of the same text or context, significantly reducing reasoning time and GPU resource consumption.
- Multi-storage back-end support: Supports multiple storage methods such as GPU, CPU DRAM, disk and Redis to flexibly cope with memory constraints.
- Integration with vLLM: Seamless access to the vLLM inference engine, providing 3-10x latency optimization.
- distributed cache: Supports shared caching across multiple GPUs or containerized environments for large-scale deployments.
- multimodal support: Cacheable key-value pairs of images and text to optimize multimodal model inference.
These features make it particularly suitable for long context scenarios such as multiple rounds of Q&A, Retrieval Augmented Generation (RAG), etc.
This answer comes from the articleLMCache: A Key-Value Cache Optimization Tool for Accelerating Reasoning on Large Language ModelsThe
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