ThriftAttention: Selective Mixed Precision for Long-Context FP4 Attention
The paper introduces ThriftAttention, a method designed to improve the efficiency of attention algorithms in long-context workloads. It utilizes selective mixed precision to maintain quality while reducing computational costs. The approach shows significant performance recovery compared to traditional FP4 methods, especially as sequence lengths increase.
- ▪ThriftAttention employs a two-stage process to enhance attention computation efficiency.
- ▪By computing only 5% of query-key blocks in FP16, the method recovers an average of 89.1% of the performance gap between FP4 and FP16.
- ▪The technique addresses the quality degradation typically observed in long-context settings.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2605.23081 |
| Publication time | Tue, 26 May 2026 06:22:05 +0000 |
| Retrieval time | 2026-05-26T06:37:45.433Z |
| Last seen | 2026-05-26T06:37:45.433Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | XEDKh0iHtBaL |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
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| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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 View a PDF of the paper titled ThriftAttention: Selective Mixed Precision for Long-Context FP4 Attention, by Joe Sharratt View PDF HTML (experimental) 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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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