Question: I need a system that provides high-performance AI workloads with parallelism, throughput, and data locality; do you know of any options?

If you need a system that offers high-performance AI workloads with parallelism, throughput and data locality, Anyscale is a top contender. Based on the open-source Ray framework, it offers the best performance and efficiency features, including workload scheduling, cloud flexibility, intelligent instance management and GPU/CPU fractioning. It supports a broad range of AI models and has native integrations with popular IDEs, persisted storage and Git integration, making it a great option for AI application development and deployment.

Another top contender is Zerve, which lets you run and manage GenAI and LLMs in your own environment. It speeds up data science and ML workflows by combining open models, serverless GPUs and your own data. Zerve has an integrated environment with notebook and IDE features, fine-grained GPU control, unlimited parallelization and collaboration tools. The platform can be self-hosted on AWS, Azure or GCP instances, giving you full control over data and infrastructure, and is a great option for balancing collaboration and stability.

RunPod is a cloud platform designed specifically for developing, training and running AI models. It offers a globally distributed GPU cloud with instant GPU pod spinning up and serverless ML inference. RunPod offers autoscaling and job queuing, instant hot-reloading for local changes, and support for frameworks like PyTorch and Tensorflow. With features like 99.99% uptime, real-time logs and a CLI tool for easy provisioning, it offers high availability and easy AI workload management.