⚡ KV Cache Optimization
Accelerate transformer inference with efficient key-value caching
Your Progress
0 / 5 completed←
Previous Module
Layer Normalization
Why KV Cache?
🎯 The Problem
In autoregressive generation (GPT-style), models generate one token at a time. Without caching, each new token requires recomputing attention for all previous tokens – wasteful since those computations don't change!
💡
Key Insight
KV cache stores previously computed key and value matrices. For each new token, we only compute K and V for that token and append to cache – massive speedup!
❌ Without KV Cache
Token 1: "The"
Compute Q, K, V for position 0
Token 2: "cat"
Recompute Q, K, V for positions 0-1
Token 3: "sat"
Recompute Q, K, V for positions 0-2
⚠️ O(n²) redundant computation!
✅ With KV Cache
Token 1: "The"
Compute & cache K₀, V₀
Token 2: "cat"
Compute only K₁, V₁, append to cache
Token 3: "sat"
Compute only K₂, V₂, append to cache
✓ O(n) linear computation!
📊 Performance Impact
⚡
Speed
10-100x
Faster generation for long sequences
💾
Memory
2x
Overhead for caching K,V tensors
🎯
FLOPs
95%
Reduction in computation