What is the most likely reason for varying AI model performance across different cloud providers?

The most likely reason for varying AI model performance across different cloud providers lies in the variations in cloud provider-specific optimizations and software stack. Different cloud providers often utilize proprietary optimizations, libraries, and configurations that can significantly influence how AI models are executed and perform.

These optimizations may include customized data handling and preprocessing techniques, optimized machine learning algorithms, and unique integration with the underlying hardware. Consequently, variations in how these elements are implemented can lead to differences in speed, efficiency, and the overall performance of AI models running on different platforms.

Factors such as GPU architecture and framework versions, while relevant, do not fully encompass the broader impact of how the entire software stack and optimizations are tailored to leverage the specific infrastructure of each provider. Additionally, cooling systems, while essential for hardware performance and reliability, do not directly affect the computational performance of AI models.