nanoVLM: KV Caching Implementation for 38% Generation Speedup

AI Impact Summary

This blog post details the implementation of KV Caching from scratch in nanoVLM, resulting in a 38% speedup in generation. The core innovation is using a per-layer KV cache to avoid redundant computations in the self-attention mechanism, significantly improving efficiency. This technique is particularly effective for autoregressive language models generating long sequences, and the implementation demonstrates a practical approach to optimizing LLM performance.