Accelerated cloud computing
Scientists, artists, and engineers need access to massively parallel computational power. Google Cloud offers virtual machines with GPUs capable of up to 960 teraflops of performance per instance. Deep learning, physical simulation, and molecular modeling are accelerated with NVIDIA Tesla K80, P4, T4, P100, and V100 GPUs. Regardless of the size of your workload, GCP provides the perfect GPU for your job.
Speed up complex compute jobs
Increase the speed of complex processing such as machine learning, medical analysis, seismic exploration, video transcoding, graphic visualization, and scientific simulations. Provision Compute Engine instances with powerful GPUs to handle your most complex compute-intensive workloads.
GPUs in the cloud
Reduce capital costs — whether the task requires GPUs for hours or weeks, you can get exactly what you need. Precisely configure an instance with the ratio of processors, memory, and GPUs you need instead of modifying your workload to fit within limited system configurations.
Optimize time and cost
Thanks to per-second pricing, you can choose the GPU that best suits your workload and pay only for what you need.
Built on Google’s infrastructure
Access some of the same hardware that Google uses to develop high performance machine learning products. GPUs give you the power that you need to process massive datasets. The hardware is passed through directly to the virtual machine to provide bare metal performance.
Several GPU types available
NVIDIA Tesla K80, P100, P4, T4, and V100 GPUs are available today, depending on your compute or visualization needs.
Bare metal performance
GPUs are offered in passthrough mode, directly attached to the virtual machine to provide maximum performance.
All the benefits of Google Cloud
Run GPU workloads on Google Cloud Platform where you have access to industry-leading storage, networking, and data analytics technologies.
Virtual workstations in the cloud
Run graphics-intensive applications including 3D visualization and rendering with NVIDIA GRID Virtual Workstations, supported on P4, P100, and T4 GPUs.
Attach GPUs to any machine type
Optimally balance the processor, memory, high performance disk, and GPU power for your individual workload.
Flexible GPU counts per instance
Attach up to 8 GPU dies to your instance to get the power that you need for your applications.
GPU application frameworks
Whether your applications require OpenCL, CUDA, Vulkan, or OpenGL, Compute Engine provides the hardware that you need to accelerate your workloads.
Get the same per-second billing for GPUs that you do for the rest of Google Cloud Platform's resources. Pay only for what you need while you are using it.
For batch processing jobs, customers can save 70% from on-demand prices by using GPUs with preemptible instances. Together with preemptible GPU instances, managed instance groups can be used to create a large pool of affordable GPU capacity that runs as long as capacity is available.
For certain tasks, [NVIDIA] GPUs are a cost-effective and high-performance alternative to traditional CPUs. They work great with Shazam’s core music recognition workload, in which we match snippets of user-recorded audio fingerprints against our catalog of over 40 million songs. We do that by taking the audio signatures of each and every song, compiling them into a custom database format and loading them into GPU memory. Whenever a user Shazams a song, our algorithm uses GPUs to search that database until it finds a match. This happens successfully over 20 million times per day.Ben Belchak, Head of Site Reliability Engineering, Shazam
Leverage GPUs on Google Cloud for machine learning, scientific computing, and 3D visualization.
For information about GPU pricing for the different GPU types and regions that are available on Compute Engine, refer to the GPU pricing document.