We believe Forrester’s recognition of Google Cloud as a Leader in The Forrester Wave™: AI Infrastructure Solutions, Q1 2024, underscores our decades-long investment in AI research and development. Our investments have propelled Google Cloud beyond the fundamentals of computation, networking, and storage. We provide comprehensive software, frameworks, optimized AI workload orchestration, and platforms explicitly tailored for training and serving a broad range of AI models.
“Doing AI efficiently at Google-scale is a feat that few other companies in the world are capable of. Google brings that experience and infrastructure to Google Cloud AI Infrastructure. Google’s early and ongoing investments in AI for its other businesses drives its vision of “where the puck is going to be” for enterprise AI. Google’s superior roadmap and innovation is to make Google-scale accessible to all customers whether a bright tiny startup or large global enterprises whilst at the same time abstracting the complexity with easy-to-use tools.” - The Forrester Wave™: AI Infrastructure Solutions, Q1 2024
This decades-long dedication to building a comprehensive, AI-optimized stack has culminated in
AI Hypercomputer – a supercomputing architecture that combines a suite of technologies optimized for modern AI workloads. AI Hypercomputer is composed of:
- Performance-optimized hardware: Compute, storage, and networking built over an ultrascale data center infrastructure, leveraging a high-density footprint, liquid cooling, and our Jupiter data center network. All of this is predicated on technologies that are built with performance and efficiency at their core; leveraging clean energy and a deep commitment to sustainability, to help us move toward a carbon-free future.
- Open software: AI Hypercomputer enables developers to access our performance-optimized hardware through the use of open software like Cloud TPU Multislice Training and Multihost Inference to tune, manage, and dynamically orchestrate AI training and inference workloads on top of performance-optimized AI hardware. It is also equipped with extensive support for popular ML frameworks such as JAX, TensorFlow, and PyTorch are available right out of the box. For customers looking for flexibility to architect their own AI platform, Google Kubernetes Engine (GKE) streamlines training and serving for the latest foundation models, with built-in support for autoscaling, workload orchestration, and automatic upgrades.
- Flexible consumption: AI Hypercomputer offers a wide range of flexible and dynamic consumption choices. In addition to traditional options, such as committed use discounts (CUD), on-demand pricing, and spot pricing, it also provides consumption models tailored for AI workloads via Dynamic Workload Scheduler, which provides consumption options for utilizing computing power quickly, as well as for higher predictability on job start times.
Finally, for customers seeking the simplest way to train, use, and deploy AI models,
Vertex AI provides an end-to-end platform for building production AI applications. Whether training a model from scratch, leveraging AutoML, or using one of the 130+ available foundation models from Google, third-party partners, and the open-source community, Vertex AI makes it simple to use Google Cloud’s AI Infrastructure to power your enterprise grade AI applications. No matter the level of technical sophistication, customers can find the right tooling to use, train, and deploy a model with minimal configuration and fully managed MLOps and governance, allowing you to focus on your model's application rather than the management overhead of underlying infrastructure.
“Google offers the whole package for AI workloads. AI continues to be a core capability of Google’s many consumer and business services such as internet search and advertising. So, to say Google has a head-start is an understatement.“ - The Forrester Wave™: AI Infrastructure Solutions, Q1 2024