Efficiently scale ML and other compute workloads on NVIDIA’s T4 GPU, now generally available

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You can get up and running quickly, training ML models and serving inference workloads on NVIDIA T4 GPUs by using our Deep Learning VM images. These include all the software you’ll need: drivers, CUDA-X AI libraries, and popular AI frameworks like TensorFlow and PyTorch. We handle software updates, compatibility, and performance optimizations, so you don’t have to. Just create a new Compute Engine instance, select your image, click Start, and a few minutes later, you can access your T4-enabled instance. You can also start with our AI Platform, an end-to-end development environment that helps ML developers and data scientists to build, share, and run machine learning applications anywhere. Once you’re ready, you can use Automatic Mixed Precision to speed up your workload via Tensor Cores with only a few lines of code.

Performance at scale

NVIDIA T4 GPUs offer value for batch compute HPC and rendering workloads, delivering dramatic performance and efficiency that maximizes the utility of at-scale deployments. A Princeton University neuroscience researcher had this to say about the T4’s unique price and performance:

“We are excited to partner with Google Cloud on a landmark achievement for neuroscience: reconstructing the connectome of a cubic millimeter of neocortex. It’s thrilling to wield thousands of T4 GPUs powered by Kubernetes Engine. These computational resources are allowing us to trace 5 km of neuronal wiring, and identify a billion synapses inside the tiny volume.”

–Sebastian Seung, Princeton University

Quadro Virtual Workstations on GCP

T4 GPUs are also a great option for running virtual workstations for engineers and creative professionals. With NVIDIA Quadro Virtual Workstations from the GCP Marketplace, users can run applications built on the NVIDIA RTX platform to experience bring the next generation of computer graphics including real-time ray tracing and AI-enhanced graphics, video and image processing, from anywhere.

“Access to NVIDIA Quadro Virtual Workstation on the Google Cloud Platform will empower many of our customers to deploy and start using Autodesk software quickly, from anywhere. For certain workflows, customers leveraging NVIDIA T4 and RTX technology will see a big difference when it comes to rendering scenes and creating realistic 3D models and simulations. We’re excited to continue to collaborate with NVIDIA and Google to bring increased efficiency and speed to artist workflows.”

–Eric Bourque, Senior Software Development Manager, Autodesk

Get started today

Check out our GPU page to learn more about how the wide selection of GPUs available on GCP can meet your needs. You can learn about customer use cases and the latest updates to GPUs on GCP in our Google Cloud Next 19 talk, GPU Infrastructure on GCP for ML and HPC Workloads. Once you’re ready to dive in, try running a few TensorFlow inference workloads by reading our blog or our documentation and tutorials.

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