Question: Do you know of a system that can manage and deploy AI and high-performance computing clusters across different workloads?

NVIDIA is a giant in AI computing and has a broad portfolio of products and platforms to accelerate AI adoption and maximize investments. Among its tools are NVIDIA Base Command Manager for deploying and managing AI and HPC clusters, NVIDIA EGX Platform for accelerated computing, and NVIDIA DGX Platform for accelerating data science pipelines. That spans a broad range of customers, from data scientists to gamers and content creators.

Another top contender is Anyscale, a platform for building, deploying and scaling AI applications. It's got a strong focus on performance and efficiency with features like workload scheduling, cloud flexibility, smart instance management and GPU and CPU fractioning. Anyscale supports many different AI models and can cut costs by up to 50% with spot instances, so it's a good choice for enterprise use cases.

dstack is an open-source engine that automates infrastructure provisioning for AI model development, training and deployment on multiple cloud providers and data centers. It makes it easier to set up and run workloads, so you can focus on data and research instead of infrastructure. dstack supports a broad range of cloud providers and on-prem servers, so you can deploy wherever you need.

Last, RunPod offers a cloud platform for developing, training and running AI models. It's got a globally distributed GPU cloud with the ability to spin up GPU pods instantly and flexible billing. It's got features like serverless ML inference, autoscaling and real-time logs and analytics, so it's a good choice for large-scale AI workloads.