Latency

Latency in AI is the time it takes for an AI system to respond after receiving an input. Most often, this refers to inference latency—how quickly a model processes a request and returns a result during real-world use.

Latency is a critical performance factor, especially for AI applications that demand real-time speed and responsiveness.

Key aspects of AI latency include:

• Inference Delay: The time between a user prompt and the model’s response.
• User Experience: Lower latency means faster, smoother interactions—crucial for chatbots, video tools, and autonomous systems.
• Model Complexity: Larger, more powerful models often have higher latency unless specifically optimized.
• Infrastructure Impact: High-performance GPUs (like NVIDIA H100s) and tuned inference engines can dramatically cut latency.
  Business Implications: In real-time products, even small delays can impact engagement, conversion, or customer satisfaction.

Reducing latency is essential to scaling AI products that feel immediate and intuitive. Teams that prioritize inference speed often unlock better performance and cost efficiency. Learn more about how we’re driving low-latency AI infrastructure here.