Graphics Processing Unit (GPU)

Leverage GPUs on Google Cloud for machine learning and scientific computing

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Accelerated Cloud Computing

Scientists, artists, and engineers need access to massively parallel computational power. Google Cloud now offers virtual machines with GPUs capable of tens of teraflops of performance. Deep learning, physical simulation, and molecular modeling take hours instead of days on NVIDIA® Tesla® K80 and P100s GPUs. Regardless of the size of your workload, GCP provides the perfect GPU for your job.

Accelerated Cloud Computing

Speed Up Complex Compute Jobs

Increase the speed of complex processing such as machine learning, medical analysis, seismic exploration, video transcoding and scientific simulations. Provision Compute Engine instances with powerful GPUs to handle your most complex compute-intensive workloads.

Speed Up Complex Compute Jobs

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.

GPUs in the Cloud

Optimize Time and Cost

Thanks to per minute pricing, you can choose the GPU that best suits your workload and pay only for what you need.

Optimize Time and Cost

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.

Built on Google’s Infrastructure

Graphics Processing Unit (GPU) Features

Leverage GPUs on Google Cloud Platform for machine learning and scientific computing

Several GPU types available
NVIDIA® Tesla® K80 and P100 GPUs are available today, depending on your compute.
Bare metal Performance
GPUs are offered in passthrough mode, directly attached to the virtual machine to provide maximum performance.
All the Benefits of the Google Cloud
Run GPU workloads on Google Cloud Platform where you have access to industry-leading storage, networking, and data analytics technologies.
Fast Video Transcoding
Experience faster than real-time digital video file conversion on GPUs for GCE.
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.
Per-Minute Billing
Get the same per-minute 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 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

Graphics Processing Unit (GPU) Pricing

Leverage GPUs on Google Cloud for machine learning and scientific computing

Price by location

SKU - LOCATION ON DEMAND PRICE DIE / HOUR (USD) SUSTAINED USE DISCOUNTS DIE / HOUR (USD) (Up to 30% Off)
P100 - US $2.30 As low as $1.61
P100 - EUROPE/ASIA $2.53 As low as $1.771
K80 - US $0.70 As low as $0.49
K80 - EUROPE/ASIA $0.77 As low as $0.54
Beta: This is a Beta release. This feature is not covered by any SLA or deprecation policy and may be subject to backward-incompatible changes.

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