A formula and Python one-liner are provided to calculate the exact memory usage of KV cache for large language models (LLMs), revealing significant storage requirements that can exceed 100GB for some models. This calculation is crucial for developers optimizing resource allocation in LLM deployments, especially as it highlights the need for compression techniques like NexusQuant to reduce memory consumption and enable single-GPU operation.
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