arXiv:2605.23751v1 Announce Type: new Abstract: We revisit the I/O complexity of attention in large language models. Given query-key-value matrices $Q,K,V\in\mathbb{R}^{n\times d}$, and a machine with fast memory size $M$, the goal is to compute the "attention matrix" $A=\text{softmax}(Q K ^{\top}/\sqrt{d}) V$ with the minimal number of data transfers between fast and slow memory. Existing methods in the literature, most notably FlashAttention and its variants, incur an I/O cost that depends quadratically on $n$, while a trivial lower bound only requires $\Omega(nd)$ I/O's to read the inputs a
The continuous scaling of large language models necessitates ongoing research into optimizing their core computational components like attention mechanisms to enhance efficiency and reduce I/O bottlenecks.
Improved I/O optimality for approximate attention directly translates to more efficient training and inference for large language models, impacting the cost and speed of AI development and deployment.
This research suggests a potential pathway to significantly reduce the computational cost and resource requirements for training and operating large AI models, moving beyond current state-of-the-art like FlashAttention.
- · AI model developers
- · Cloud infrastructure providers
- · Compute hardware manufacturers
- · Data center operators
- · Inefficient AI architectures
- · Organizations relying on brute-force compute scaling without optimization
Reduced operational costs for large AI models, making them more accessible and deployable.
Acceleration of research into even larger and more complex AI models due to loosened computational constraints.
Increased competition in AI development as the barrier to entry for training advanced models is lowered by efficiency gains.
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Read at arXiv cs.LG