arXiv:2604.21335v3 Announce Type: replace Abstract: Transformer inference often requires a large KV cache, especially for long-context language modeling and multimodal generation. Existing compression methods usually reduce cache cost by selecting, evicting, quantizing, or compressing cached tokens, or by reducing the visual-token sequence before language-model inference. We introduce sub-token routing, a KV-compression method that adds a finer control axis inside retained tokens. It splits each retained value vector into groups and keeps only selected groups, while leaving query and key state
The increasing demand for larger context windows and multimodal generation in Transformer models is pushing the limits of current KV cache management, creating an urgent need for more efficient compression techniques.
Efficient KV cache compression directly impacts the operational cost and scalability of large language models, enabling more complex applications and broader adoption without proportional increases in computational resources.
The introduction of sub-token routing offers a novel, finer-grained approach to KV cache compression, potentially improving inference efficiency beyond existing methods like token selection or quantization.
- · AI model developers
- · Cloud providers offering AI services
- · Users of long-context AI applications
- · Hardware manufacturers specializing in memory
- · Inefficient AI inference optimization methods
- · Organizations relying solely on brute-force memory scaling
Reduced memory footprint and increased throughput for large language model inference.
Enables the development of more sophisticated AI applications with longer context windows and multimodal capabilities at lower operational costs.
Accelerates the widespread deployment of advanced AI agents by making them more economically viable and performant, influencing various industries.
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Read at arXiv cs.LG