KV Cache Explained: Why AI Inference Runs Out of Memory

The key-value cache, or KV cache, helps large language models reuse information as they generate responses. But longer context windows and more simultaneous users can make the cache a major memory consideration for AI inference. In this episode of AI-in-5, Brandon Seymour explains what the KV cache does, how it affects GPU memory and inference capacity, and several ways to manage it, including cache optimization, prefix reuse, quantization, and offloading. We also look at Penguin Solutions’ MemoryAI™ KV Cache Server, a CXL-based system designed to store and reuse KV cache data outside GPU memory. As with any infrastructure solution, test it with your own models and workloads. Learn more: NVIDIA on KV cache: https://developer.nvidia.com/blog/how... Penguin Solutions MemoryAI: https://www.penguinsolutions.com/en-u... Scaling the AI memory wall: https://www.penguinsolutions.com/en-u... Find more information on my blog: https://www.virtualizationvelocity.com/ CHAPTERS 00:00 Welcome to AI-in-5 00:18 What the KV cache does 01:00 Why context and concurrency use memory 01:50 Ways to manage KV cache 02:45 Penguin Solutions MemoryAI 03:40 What to measure in a workload test 04:25 The takeaway #AIInference #KVCache #AIin5