Part 7
Completed
KV Cache
Implemented KV caching for autoregressive transformer inference, reducing complexity from O(n²) to O(n) per token. Optimized cache layout for memory bandwidth, implemented cache eviction strategies, and profiled memory usage across sequence lengths.
What I Built
Implemented KV caching for autoregressive transformer inference, reducing complexity from O(n²) to O(n) per token. Optimized cache layout for memory bandwidth, implemented cache eviction strategies, and profiled memory usage across sequence lengths.
Key Concepts
KV CacheAutoregressive InferenceMemory BandwidthCache LayoutEviction StrategyMemory Profiling
Architecture
1
Key Cache Store2
Value Cache Store3
Cache Manager4
Eviction Policy5
Memory ProfilerResults
Reduced per-token latency from 45ms to 8ms for 2048-token sequences. Memory footprint scales linearly with sequence length as expected.
Key Learnings
- KV cache transforms transformer inference from quadratic to linear
- Memory bandwidth, not compute, is the bottleneck for inference
- Cache layout (interleaved vs. contiguous) significantly impacts performance
Challenges
- Managing growing cache memory over long conversations
- Optimizing cache for batched inference
- Handling cache invalidation on context switches