STEPQuant: When and Where Errors Matter in Delta-Rule Recurrent State Quantization

In 30 Sekunden

Was passiert ist

STEPQuant compresses recurrent states in linear attention models to 6-bit precision by allocating precision based on error magnitude and memory lifetime across spatial and temporal dimensions.

Warum es zählt

Matters for engineers deploying large language models with linear attention in memory-constrained serving environments where KV cache compression is critical.

Achtung

Results demonstrated on specific models; generalization to other architectures and real-world deployment stability under production load remains unvalidated.