Question: Can you suggest a platform that supports multiple GPU types and provides infrastructure as code for easy environment creation and deployment of machine learning models?

If you need a platform that can handle different types of GPUs and that offers infrastructure as code to set up an environment and deploy machine learning models, Cerebrium is a good option. It offers a serverless GPU infrastructure for training and deploying ML models with support for GPU variety, infrastructure as code, volume storage and real-time monitoring. Cerebrium's pay-per-use pricing is designed to be economical, so you can scale without worrying about latency or high failure rates.

Another good option is Anyscale, which offers a platform for building, deploying and scaling AI applications. It supports a variety of AI models and offers heterogeneous node control, smart instance management and workload scheduling. Built on the open-source Ray framework, Anyscale supports multiple clouds and on-premise environments, offering cost savings up to 50% on spot instances.

RunPod is another option. It offers a globally distributed GPU cloud with a variety of GPU options, including serverless ML inference and autoscaling. The platform offers more than 50 preconfigured templates for popular frameworks and real-time logs and analytics, making it easy to deploy and manage AI workloads.

If you prefer an open-source option, dstack automates infrastructure provisioning on a variety of cloud providers and data centers. It supports a variety of cloud services and on-prem servers, making it easier to set up AI workloads and letting you focus on data and research while cutting costs.