KV Cache from scratch in nanoVLM

Source: HuggingFace Date: 2025-06-04 URL: https://huggingface.co/blog/kv-cache

Summary

Educational post: KV cache implementation walkthrough using nanoVLM (HF’s minimal VLM codebase in pure PyTorch). Explains the prefill/decode split, layer-wise cache tracking with positional awareness, and attention block modifications. Key result: 38% speedup in generation speed after adding KV caching. No new model or library release — this is an implementation reference for understanding modern LLM inference.

Implications

Transformers library trajectory. The nanoVLM project itself — a minimal VLM training codebase in pure PyTorch — is the more interesting signal than the KV cache tutorial. Having a clean, educational reference implementation for VLM training without framework abstractions is useful for teams building custom inference stacks or doing research that requires understanding internals the Transformers library abstracts away.

Open-weights ecosystem health. The 38% generation speedup from KV caching (with no model changes) demonstrates that inference optimization has compounding returns as model complexity increases. Teams running VLMs without KV caching enabled in their serving infrastructure are leaving substantial throughput gains on the table — a practical finding for any team that deployed a VLM before auditing their inference stack.