arXiv:2605.27646v1 Announce Type: new Abstract: We propose \textbf{Hurwitz Quaternion Multiplicative Quantization (HQMQ)}, a \textbf{calibration-free} method for KV cache compression of large language models. HQMQ treats each 4-element chunk of K or V as a quaternion and quantizes its unit direction to the \emph{product} $q_p \cdot q_s$, where $q_p$ ranges over the 24-element Hurwitz group $2T$ (the 24 vertices of the 24-cell on $S^3$, pairwise angle $60^\circ$) and $q_s$ ranges over a per-(layer, head) secondary codebook of $S$ \emph{random} unit quaternions. The multiplicative composition yi
The continuous growth in size and complexity of large language models necessitates increasingly efficient methods for KV cache management to maintain performance and reduce resource consumption.
Efficient KV cache compression is crucial for scaling AI models, reducing memory footprints, and lowering computational costs, which directly impacts the accessibility and deployment of advanced AI.
This method offers a calibration-free approach to significantly reduce the memory and computational burden of KV caches in large language models, potentially enabling larger models or more efficient inference on existing hardware.
- · AI developers
- · Cloud providers
- · Hardware manufacturers (benefitting from increased demand for efficient chips)
Reduced operational costs for running large language models due to more efficient memory usage.
Acceleration in the development and deployment of even larger and more complex AI models.
Shift in AI infrastructure spending towards optimization technologies rather than solely raw compute, potentially impacting chip design priorities.
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Read at arXiv cs.LG