Scalable, high performance, and cost effective infrastructure for every AI workload.
Optimize performance and cost at scale
With Google Cloud, you can choose from GPUs, TPUs, or CPUs to support a variety of use cases including high performance training, low cost inference, and large-scale data processing.
Deliver results faster with managed infrastructure
Scale faster and more efficiently with managed infrastructure provided by Vertex AI. Set up ML environments quickly, automate orchestration, manage large clusters, and set up low latency applications.
Develop with software that’s purpose-built for AI
Improve AI development productivity by leveraging GKE to manage large-scale workloads. Train and serve the foundation models with support for autoscaling, workload orchestration, and automatic upgrades.
AI Infrastructure Tools on GKE
Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities.
Deep Learning VM Images
Deep Learning VM Images are optimized for data science and machine learning tasks. They come with key ML frameworks and tools pre-installed, and work with GPUs.
Deep Learning Containers
Deep Learning Containers are performance-optimized, consistent environments to help you prototype and implement workflows quickly on CPUs or GPUs.
How are Tensor Processing Units optimized for AI/ML?
Learn about the computational requirements of machine learning, and how TPUs were purpose-built to handle the task.
TPU System Architecture
TPUs are Google's custom-developed ASICs used to accelerate machine learning workloads. Learn about the underlying system architecture of TPUs from the ground up.