arXiv:2607.01065v1 Announce Type: new Abstract: The deployment of Large Language Models (LLMs) with extended context windows is increasingly constrained by the linear growth of Key-Value (KV) cache memory. Vector Quantization (VQ), particularly Residual Quantization (RQ), is a promising approach for pushing KV cache storage toward the sub-1-bit regime by progressively encoding residuals with small codebooks. However, most VQ methods still rely on standard $\ell_2$ $K$-means as the core codebook-learning primitive. We identify a subtle high-dimensional issue of this primitive: Euclidean centroi
The proliferation of Large Language Models and the increasing demand for extended context windows are driving urgent needs for more efficient KV cache management.
Efficient KV cache quantization directly impacts the cost and scalability of deploying advanced LLMs, influencing their widespread adoption and accessibility.
This research could lead to significantly reduced memory requirements for LLM inference, enabling larger models or longer contexts on existing hardware, or smaller models with comparable performance.
- · LLM developers and deployers
- · Cloud computing providers
- · AI hardware manufacturers
Memory costs for running LLMs decrease, making AI inference more accessible.
Larger and more complex LLMs become economically viable for broader applications.
Increased accessibility fuels innovation in AI applications, potentially leading to new business models and services.
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Read at arXiv cs.LG