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

DevFeed: [Co-Designing AI Model Attention for Fast, Interactive Long-Context Inference](<https://devfeed.tech/articles/co-designing-ai-model-attention-for-fast-interactive-long-context-inference-6779.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/co-designing-ai-model-attention-for-fast-interactive-long-context-inference/>)

Author: Tanya Lenz

Published: 2026-07-31T22:16:17Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [GPU optimization](<https://devfeed.tech/topics/gpu-optimization.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [memory](<https://devfeed.tech/tags/memory.md>), [model-architecture](<https://devfeed.tech/tags/model-architecture.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [performance](<https://devfeed.tech/tags/performance.md>)

## AI overview

This article examines how co-designing dense attention with GPU execution can improve throughput and interactivity for long-context inference. It analyzes group size, head dimension, sequence length, and the different compute and memory behavior of prefill and decode, including the effects of speculative decoding and prefix caching.

## Source excerpt

As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1). Because...