Show HN: KV-psi, using Linux PSI to to trim an LLM KV cache (github.com)

A new project called KV-psi has emerged, showcasing a reference implementation that utilizes Linux Pressure Stall Information (PSI) to optimize the key-value (KV) cache of large language models (LLMs) under memory pressure. This implementation is particularly valuable as it provides a method to efficiently manage memory, improving LLM performance, especially in resource-constrained environments. Developers can employ this tool using Python and a GGUF model, and it supports systems with PSI enabled, such as those utilizing cgroup memory pressure settings. The significance of KV-psi lies in its potential to enhance the operational efficiency of LLMs by intelligently trimming their KV caches based on real-time memory availability. This can lead to improved response times and reduced latency during inference. The project also includes benchmarks that demonstrate the impact of cache management under varying memory conditions, providing insights into trade-offs between performance metrics such as tokens per second and cache conservation. By optimizing memory use, KV-psi opens the door for deploying LLMs in more diverse environments where memory resources are limited, making this an intriguing development for the AI/ML community.