Project 3: Build a KV Cache and Measure the Speedup

Implement a key-value cache for autoregressive decoding and quantify the latency improvements.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 10–16 hours
Language Python
Prerequisites Transformer internals, decoding basics
Key Topics KV cache, autoregressive decoding, latency profiling

Learning Objectives

By completing this project, you will:

  1. Implement KV caching for decoder layers.
  2. Measure speedups vs recomputing attention.
  3. Quantify memory tradeoffs of caches.
  4. Verify correctness against non-cached output.
  5. Profile per-token latency growth.

The Core Question You’re Answering

“Why does autoregressive inference scale poorly without caching?”

KV cache turns quadratic recomputation into linear updates.

Concepts You Must Understand First

Concept Why It Matters Where to Learn
Autoregressive decoding Token-by-token generation LLM inference guides
Key/value reuse Avoid recomputation Transformer internals
Latency profiling Measure performance Profiling basics

Theoretical Foundation

With vs Without Cache

No Cache: O(n^2) attention per token
Cache:    O(n) update per token

Caching reuses past keys/values so you only compute for the new token.

Project Specification

What You’ll Build

A decoder-only model that supports KV caching and reports speedup metrics.

Functional Requirements

  1. Cache structure per layer
  2. Incremental decoding using cache
  3. Latency benchmarks for 50/100/500 tokens
  4. Correctness checks vs baseline
  5. Memory usage reporting

Non-Functional Requirements

  • Deterministic benchmarks
  • Clear speedup reports
  • Safe fallback when cache disabled

Real World Outcome

Example output:

Tokens: 500
Baseline: 2400 ms
With cache: 680 ms
Speedup: 3.5x

Architecture Overview

┌──────────────┐   token   ┌──────────────┐
│ Decoder      │────────▶│ KV Cache     │
└──────────────┘         └──────┬───────┘
                                ▼
                         ┌──────────────┐
                         │ Benchmark    │
                         └──────────────┘

Implementation Guide

Phase 1: Cache Structure (3–4h)

  • Store per-layer keys/values
  • Checkpoint: cache grows with tokens

Phase 2: Incremental Decoding (4–6h)

  • Use cache for next-token computation
  • Checkpoint: outputs match baseline

Phase 3: Benchmarking (3–6h)

  • Measure latency and memory
  • Checkpoint: speedup reported

Common Pitfalls & Debugging

Pitfall Symptom Fix
Wrong cache order incorrect output append in correct sequence
No speedup same latency ensure no recompute
Memory blowup OOM cap context or batch

Interview Questions They’ll Ask

  1. Why does KV cache reduce attention cost?
  2. What is the memory tradeoff of caching?
  3. How do you validate cached output correctness?

Hints in Layers

  • Hint 1: Start with a single-layer cache.
  • Hint 2: Compare output with cache disabled.
  • Hint 3: Benchmark for long sequences.
  • Hint 4: Plot latency per token.

Learning Milestones

  1. Cache Works: output matches baseline.
  2. Faster: measurable speedup.
  3. Profiled: memory tradeoffs documented.

Submission / Completion Criteria

Minimum Completion

  • KV cache implementation

Full Completion

  • Benchmarks + correctness validation

Excellence

  • Sliding window cache
  • Comparison with HF cache

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/HUGGINGFACE_TRANSFORMERS_ML_INFERENCE_ECOSYSTEM.md.