# AI Development Navigates The Latency Sensitivity Spectrum: Training Allows For Slow Processing, But Real-Time Tasks Require Lightning-Fast Inference

By Software And Technology · Published 2024-05-20 · Updated 2026-06-22 · Professional AV on MarketScale
Canonical: https://www.marketscale.com/industries/pro-av/ai-training-and-inference-navigating-the-latency-sensitivity-spectrum
Creator hub: Applied Digital

> The speed requirements for AI shift dramatically depending on whether systems are learning or performing live tasks

## Key points

- AI training can afford slower processing times.
- Real-time AI tasks demand fast inference speeds.
- Appropriate handling of latency is key in AI system development.

Block Field

Latency sensitivity in AI processes varies significantly between training and inference. Training operations, which involve processing large datasets over extended periods, are generally very tolerant of high latency. This tolerance allows training tasks to be performed with minimal concern for immediate responsiveness.

> Training operations are generally very tolerant of high latency.

[Wes Cummins](https://www.linkedin.com/in/wes-cummins-63114481/), the CEO of [Applied Digital](https://applieddigital.com/) joins [David Liggitt](https://www.linkedin.com/in/davidliggitt/), the Founder and CEO of [datacenterHawk](https://datacenterhawk.com/) to talk about the spectrum of latency sensitivity within AI inference tasks. Mission-critical inference applications require ultra-low latency and high reliability, often needing to operate in cloud regions with five-nines reliability. Conversely, batch inference tasks, such as those involving generative AI for text-to-image or text-to-video conversions, can afford much higher latency. Chatbots and similar applications fall somewhere in between, with reasonable tolerance for latency variations.

> Mission-critical inference applications require ultra-low latency and high reliability.

Tags: AI inference tasks, AI processes, Applied Digital, datacenterHawk, David Liggitt, inference applications, latency sensitivity, Wes Cummins

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