Professional AV
AI Development Navigates The Latency Sensitivity Spectrum: Training Allows For Slow Processing, But Real-Time Tasks Require Lightning-Fast Inference
AI systems have different speed requirements for training versus real-time tasks. Training allows for slower processing due to its offline nature, whereas live tasks require quick inference for effectiveness. Understanding the latency sensitivity spectrum in AI development is crucial for optimizing performance.
This story was produced through MarketScale. See how Professional AV teams put it to work with Customer Stories & Case Studies.
Promoted content from Applied Digital on MarketScale.
Key takeaways
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.
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, the CEO of Applied Digital joins David Liggitt, the Founder and CEO of datacenterHawk 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.
Part of this channel
Applied Digital
News, updates, and expert insights from Applied Digital.
About the author
