Scalable, high performance, and cost effective infrastructure for every ML 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.
Innovate faster with State of the Art AI
Drive more value from ML with access to state of the art AI from Google Research, DeepMind, and partners.
Using GPUs for training models in the cloud
GPUs can accelerate the training process for deep learning models for tasks like image classification, video analysis, and natural language processing.
Using TPUs to train your model
TPUs are Google's custom-developed ASICs used to accelerate machine learning workloads. You can run your training jobs on AI Platform Training, using Cloud TPU.
What makes TPUs fine tuned for deep learning?
Learn about the computational requirements of deep learning and how CPU, GPU, and TPUs handle the task.
Deep Learning VM
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.
AI Platform Deep Learning Containers
AI Platform Deep Learning Containers are performance-optimized, consistent environments to help you prototype and implement workflows quickly. They work with GPUs.