LMCache requires specific runtime environment support, requiring a Linux operating system, Python version no less than 3.10, and must be configured with NVIDIA CUDA 12.1 and above development environment. It is officially recommended to use Miniconda to create an isolated Python virtual environment to manage dependencies. Installation is flexible: you can either install the stable version directly via PyPI (pip install lmcache), or you can choose to compile and install from source to get the latest features. It is worth noting that LMCache must be used in conjunction with the vLLM inference engine, so an additional installation of the vLLM component is required. For containerized deployment scenarios, the project also provides pre-built Docker images that integrate vLLM and related dependencies to simplify the deployment process.
This answer comes from the articleLMCache: A Key-Value Cache Optimization Tool for Accelerating Reasoning on Large Language ModelsThe
