Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference

The article shows that dense‑attention performance in long‑context inference is governed by group size (query heads per KV head), head dimension, and sequence length, with prefill being compute‑bound by sequence length and decode driven by arithmetic intensity scaling with group size. It recommends using large group sizes for decoding, head dimensions of 128 or 256 to match GPU tile alignment, and minimizing the effective KV state via cache compression, sparse or sliding‑window attention, or hybrid architectures such as NVIDIA Nemotron 3. Parallelism should be guided by KV head count—tensor parallelism must not exceed the number of KV heads, while models with few heads benefit from Attention Data Parallelism, KV Parallelism, or hybrid schemes (e.g., Wide EP, Helix) as implemented in TensorRT‑LLM to achieve optimal throughput and GPU utilization on NVIDIA hardware.

Source: NVIDIA Developer | 2026-07-31