Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference
NVIDIA, Friday, July 31st, 2026
NVIDIA covers co-designing attention mechanisms with hardware as long contexts dominate inference time.
As agentic and long-context workloads become common, context lengths grow and attention consumes an increasing share of inference time.
NVIDIA argues that the answer is co-designing the attention mechanism alongside the hardware rather than optimizing either in isolation.
The post covers which attention variants map well onto current accelerators and where the memory bandwidth limits bite.
It quantifies the share of inference latency attributable to attention at long context lengths. The work is aimed at model architects and inference engineers.