arXiv:2510.01718v2 Announce Type: replace Abstract: Attention is a core operation in large language models (LLMs). We present BD Attention (BDA), a lossless algorithmic reformulation of attention. BDA is enabled by a simple matrix identity from Basis Decomposition (BD), which restructures multi-head projections into a compact form while preserving exact outputs. Unlike I/O-aware system optimizations such as FlashAttention, BDA provides a mathematically guaranteed acceleration that is architecture-agnostic. On DeepSeek-V2-Lite (16B, FP16), BDA requires only 4s of offline preparation with no ret
The continuous growth in LLM complexity and the associated computational demands make algorithmic efficiency improvements crucial for scaling and accessibility.
This development offers a mathematically guaranteed, architecture-agnostic acceleration for a core LLM operation, potentially lowering computational costs and democratizing access to advanced AI.
Attention mechanisms in LLMs can now be executed with significantly higher efficiency through a lossless algorithmic reformulation without requiring specialized hardware optimizations.
- · LLM developers
- · Cloud providers
- · Startups with limited compute budgets
- · AI researchers
- · Companies reliant solely on hardware-specific optimizations
- · Vendors of less efficient attention implementations
Reduced inference and training costs for large language models.
Faster development cycles and deployment of more complex LLMs across diverse hardware.
Potentially enables new LLM architectures or applications previously constrained by computational limits.
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Read at arXiv cs.LG