Understanding KV Cache Implementation in a Minimal Vision-Language Model

This article provides a comprehensive guide to implementing the KV (Key-Value) cache from scratch in nanoVLM, a minimal vision-language model. The KV cache is a critical optimization for autoregressive decoding in transformer-based models, enabling efficient text generation by caching intermediate representations. The write-up covers the fundamentals of how the cache works, step-by-step implementation details in PyTorch, and integration into the nanoVLM architecture. By understanding this mechanism, developers can improve inference speed and reduce redundant computations in vision-language tasks.