Question: I'm looking for a scalable solution to handle large-scale AI computing tasks, any recommendations?

If you need a scalable system to tackle big AI computing jobs, Anyscale is a good choice. The service is designed to build, deploy and scale AI applications. It's got workload scheduling, cloud flexibility, automated instance management and heterogeneous node control to try to optimize resource use. Anyscale is built on the open-source Ray framework, supports a variety of AI models and has features like persisted storage, Git integration and streamlined workflows. It's geared for enterprise use and has a variety of pricing options, including a free tier.

Another option is Lambda, a cloud computing service geared for AI developers. You can provision on-demand and reserved NVIDIA GPU instances and clusters, a good choice for training and inferencing large AI models. It's got a range of GPU options, preconfigured ML environments and scalable file systems. The service is geared for quick provisioning and management of GPU instances, with pay-by-the-second pricing for on-demand instances and reserved cloud pricing if you commit to longer-term usage.

RunPod is another option. The cloud service is notable for its globally distributed GPU cloud, which lets you run any GPU workload without a hassle. RunPod lets you spin up GPU pods instantly, offers a range of GPU options and lets you run serverless ML inference with autoscaling and job queuing. It's got more than 50 preconfigured templates for frameworks like PyTorch and TensorFlow, and a CLI tool for easy provisioning and deployment.

For businesses that want to build AI into their operations, the NVIDIA AI Platform is a more comprehensive option. It's got multi-node training at scale, speeds up the data science pipeline and makes it easier to develop and deploy production AI applications. With accelerated infrastructure, AI platform software and generative AI abilities, it lets businesses scale AI applications while lowering total cost of ownership.