19. KV Caching

19. KV Caching#

Optimizing Transformer Inference with Key-Value Caching#

KV (Key-Value) caching is a critical optimization technique for transformer models that dramatically speeds up autoregressive generation. In this module, you’ll learn how to implement KV caching to avoid redundant attention computations during inference.

What You’ll Build#

  • KV Cache: Key-Value caching for attention mechanisms

  • Feature Cache: Reuse computed features across requests

  • Gradient Cache: Efficient gradient accumulation

  • Model Cache: Multi-level model weight caching

Why This Matters#

Caching is essential for production ML systems:

  • Transformer models recompute attention for every token

  • Feature extraction is often the bottleneck

  • Redundant computations waste resources

  • Smart caching can provide 10-100x speedups

Learning Objectives#

By the end of this module, you will:

  • Implement KV caching for transformer attention layers

  • Understand how KV caching reduces O(n²) to O(n) complexity

  • Build efficient cache management for multi-turn generation

  • Measure the memory-speed tradeoff in production systems

Prerequisites#

Before starting this module, you should have completed:

  • Module 13: Attention (for KV cache understanding)

  • Module 14: Transformers (for practical application)

  • Module 15: Profiling (to measure improvements)

Real-World Applications#

Caching is critical in production ML:

  • ChatGPT: KV caching for multi-turn conversations

  • Search Engines: Feature caching for ranking

  • Recommendation Systems: User embedding caches

  • Computer Vision: Intermediate feature caching

Coming Up Next#

After mastering caching, you’ll explore:

  • Module 20: Benchmarking - Measuring the full impact of optimizations

  • Capstone Project: Building TinyGPT with all optimizations

This module is currently under development. The implementation will cover practical caching strategies used in production ML systems.

Contents