Co-Designing AI Model Attention for Long-Context Inference

Co-Designing AI Model Attention for Long-Context Inference With rising context lengths in agentic and long-context workloads, attention mechanisms dominate inference time, making architecture design critical to execution performance on GPUs. Attention consumes an increasing share of inference runtime as context lengths grow in agentic and long-context workloads. Inference performance is increasingly determined by the design of the attention mechanism itself, not solely its implementation.

围绕 GPU 执行特性协同设计注意力机制以优化长上下文推理 随着智能体与长上下文应用场景的增加，注意力机制逐渐主导推理时间成本，依据 GPU 的执行特性协同设计模型架构成为决定性能的关键。 长上下文和智能体工作负载使上下文长度增加，注意力计算逐渐占据推理时间的主要份额并主导成本。 AI 模型协同设计的核心前提是依据 GPU 的执行方式来构建和塑造模型架构，而不仅局限于代码实现层面。

As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1). Because attention now dominates that cost, how it is designed—not just how it is implemented—increasingly determines a model’s inference performance. Shaping model architecture around how GPUs execute it is the premise of AI model co-design.