Tensor Processing Units (TPUs) are custom-built ASIC to train and execute deep neural networks. Train and run more powerful and accurate models cost-effectively with faster speed and scale.
A range of NVIDIA GPUs to help with cost-effective inference or scale-up or scale-out training. Leverage RAPID and Spark with GPUs to execute deep learning. Run GPU workloads on Google Cloud where you have access to industry-leading storage, networking, and data analytics technologies.
Access CPU platforms when you start a VM instance on Compute Engine. Compute Engine offers a range of both Intel and AMD processors for your VMs.
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.