[2605.23081] ThriftAttention: Selective Mixed Precision for Long-Context FP4 Attention

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Computer Science > Machine Learning

arXiv:2605.23081 (cs)

[Submitted on 21 May 2026]

Title:ThriftAttention: Selective Mixed Precision for Long-Context FP4 Attention

Authors:Joe Sharratt<br>View a PDF of the paper titled ThriftAttention: Selective Mixed Precision for Long-Context FP4 Attention, by Joe Sharratt

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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 number of attention blocks that contain the most important tokens. We propose ThriftAttention, a low-bit attention variant that delivers near-FP16 long-context quality at FP4 inference efficiency. This approach proceeds in two stages. First, a heuristic rapidly selects a small number of important query-key block pairs for FP16 precision. Second, the selected blocks are computed in FP16 and the remaining blocks in FP4, with both paths merged via online softmax into a single output. We demonstrate across long-context benchmarks and model families that by computing only 5% of query-key blocks in FP16, ThriftAttention recovers on average 89.1% of the FP4-to-FP16 performance gap. We show ThriftAttention's advantage grows with sequence length, mitigating the systematic FP4 quality degradation observed at longer contexts. The code is available at this https URL.

Subjects:

Machine Learning (cs.LG)

Cite as:<br>arXiv:2605.23081 [cs.LG]

(or<br>arXiv:2605.23081v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.23081

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Joe Sharratt [view email]<br>[v1]<br>Thu, 21 May 2026 22:28:27 UTC (1,243 KB)

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