arXiv:2605.03562v3 Announce Type: replace Abstract: KV-cache quantizers usually optimize storage-space reconstruction, even though attention reads keys through logits and values through attention-weighted readout. We argue that persistent cache error should be measured in model-visible coordinates. For keys, the visible object is score error modulo constant shifts; this yields HeadQ, a key-side method that stores a low-rank residual side code in a calibration-learned query basis and applies it as an additive logit correction. For values, fixed-attention readout gives an $A^2$-weighted token-di
The continuous push for more efficient and performant large language models is driving innovation in foundational components like KV-cache quantization.
This research directly addresses a critical bottleneck in deploying larger and more capable AI models by reducing their memory footprint without significant performance degradation.
New methods for KV-cache quantization will allow for more efficient deployment of larger models, reducing the compute and memory requirements, and potentially enabling on-device AI for more complex tasks.
- · AI model developers
- · Cloud providers
- · Edge AI hardware manufacturers
- · Users of large language models
- · Inefficient quantization techniques
- · AI developers not optimizing for memory
Reduced operational costs for running large language models and increased accessibility for smaller organizations.
Acceleration of AI model deployment in memory-constrained environments, such as mobile or embedded devices.
Further democratization of advanced AI capabilities, potentially leading to new applications previously unfeasible due to resource limitations.
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Read at arXiv cs.LG