RECORD / 2F3CCC / REVIEWED
KV Cache Transform Coding
KV Cache Transform Coding applies learned decorrelation, adaptive quantization, and entropy coding to reduce storage and transfer costs for reusable language-model KV caches.
- State
- research only
- Layer
- infrastructure
- Reviewed
- 2026-10-17
Why it matters
Use it when multi-turn or shared-prefix serving is limited by the memory footprint or movement of inactive KV state.
What to check first
This is a research method that needs calibration and systems integration; compression and accuracy results vary by model, workload, and target bit rate.
2 related records
- 01AirLLM →AirLLM lowers peak accelerator memory by loading model layers sequentially instead of keeping the full checkpoint resident during inference.repository or library
- 02Cache-to-Cache →Cache-to-Cache learns projections that fuse a source model's KV cache into a receiver, letting paired language models exchange latent state without generating intermediate text.research paper