AI inference refers to the capability of a trained computer model to make predictions or draw conclusions from new, unseen data based on its prior training. As the model processes new information, it evolves and adapts, reducing the need for continuous human oversight.

For example, an AI model trained to recognize Golden Retrievers can accurately identify the breed in previously unseen images by recognizing patterns learned during its training phase.

AI inference represents the actionable outcome of artificial intelligence, where conclusions and predictions are drawn from learned data. It plays a critical role in various real-world applications, including:

• Healthcare: Diagnosing diseases through the analysis of medical images.
• Finance: Detecting fraud and identifying anomalies in financial data.
• Automotive: Enabling self-driving cars to navigate and make informed decisions.
• Manufacturing: Conducting rapid visual inspections on production lines. R
• Robotics: Empowering robots to perceive, predict, and plan actions.
• Research: Assisting researchers in data analysis and deriving conclusions.

• Ampere AI Compute: High Performance, Energy Efficient Processors for AI Inference https://amperecomputing.com/solutions/ampere-ai
• A Comprehensive Guide to Understanding AI Inference on the CPU https://armkeil.blob.core.windows.net/developer/Files/pdf/ebook/arm-guide-to-ai-inference-on-cpus-2024.pdf
• LLMs: Bigger is Not Always Better https://amperecomputing.com/blogs/llms-bigger-is-not-always-better