TPUs vs. GPUs: the DevOps guide to AI hardware selection

Selecting the right hardware, specifically GPUs or TPUs, is crucial for managing the cost and efficiency of AI projects, with each offering distinct advantages based on specific workloads. GPUs, originating from the gaming industry, provide flexibility and broad support for frameworks like PyTorch, making them suitable for research, diverse workloads, and smaller models, while TPUs, custom-designed by Google for deep learning, offer specialized efficiency for large-scale, matrix-heavy training, particularly when integrated with TensorFlow/JAX on Google Cloud. The decision between the two hinges on factors such as the primary framework in use, the scale and duration of training jobs, and the operational context, with TPUs favored for high-volume, long-running tasks on Google Cloud and GPUs preferred for flexibility and varied workloads. Qovery simplifies the deployment of GPU/TPU infrastructure by abstracting complex Kubernetes configurations, allowing teams to focus on model development rather than infrastructure management, thereby optimizing resource utilization and reducing operational overhead. As AI models grow in size and complexity, choosing the appropriate hardware becomes a pivotal factor in the feasibility and economic efficiency of AI initiatives, with GPUs offering adaptability and ecosystem support, while TPUs deliver targeted performance benefits for specific matrix-centric tasks.