Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference
Source
Tanya Lenz
Author
Tanya Lenz
Published
Why it matters
As agent context grows, attention becomes the dominant cost term, which changes both model choice and serving design.
As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1).
Because...
Shaping model architecture around how G
Transcript
As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1). Because... 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. Source
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