arXiv:2605.23081v1 Announce Type: new Abstract: Efficient attention algorithms are critical to mitigate the quadratic cost of attention in long-context workloads. Prior work utilises block-scaled quantisation techniques on Blackwell GPUs to move attention computation to 4-bit precision to accelerate inference. However, these techniques result in significant quality degradation in long-context settings. We show that the output impact of quantisation error is highly non-uniform and increases with the importance of each query-key interaction, concentrating functionally relevant error in a small n
The continuous drive for more efficient AI compute coincides with the increasing demand for long-context models, pushing innovation in hardware and algorithmic optimization.
This development addresses a critical bottleneck in deploying large AI models by making long-context processing more efficient and potentially expanding the capabilities of existing hardware.
Attention mechanisms can now be run with significantly reduced precision while maintaining quality, leading to faster inference and lower memory footprint for long-context AI models.
- · AI hardware manufacturers
- · Cloud AI service providers
- · Developers of large language models
- · Edge AI computing
- · Developers of less efficient attention algorithms
- · Companies reliant on brute-force compute scaling
Increased accessibility and reduced cost for deploying long-context AI applications.
Faster development and iteration cycles for new AI models due to improved computational efficiency.
Broader adoption of AI in applications previously limited by computational overhead and real-time processing requirements.
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Read at arXiv cs.LG