Multi-Head Latent Attention and the Compression of the KV-Cache
Low-rank latent projection of key and value vectors slashes inference memory footprints by over ninety percent, freeing transformer architectures from the physical bandwidth bottlenecks of multi-head attention.
★ KEY OPERATIONAL TAKEAWAYS ★
- [ANALYSIS] Compressing key and value projections into compact low-rank latent vectors reduces the physical memory overhead of cached tokens by up to ninety-three percent.
- [ANALYSIS] Decoupled rotary position embeddings preserve full positional awareness while keeping the primary attention keys and values compressed.