Create a Managed Kubernetes cluster with GPUs
When creating a Managed Kubernetes cluster, you can add GPUs to it — to do so, select a fixed GPU node configuration as the configuration.
Nodes with GPUs have pre-installed drivers from NVIDIA®.
You can see the availability of GPUs in regions in the [GPUs for Managed Kubernetes] availability matrix(/control-panel-actions/availability-matrix.mdx#gpu-for-cloud-and-kubernetes).
For GPU node groups, cluster autoscaling is not available.
Create a cluster with GPUs
- In Control Panel, go to Cloud Platform → Kubernetes.
- Click Create Cluster.
- Select the fixed configuration of the GPU node group.
- Select the rest of the cluster settings (more details in the Create Managed Kubernetes cluster instructions) and click Create.
Available GPUs
You can view an up-to-date list of GPUs in dashboard under Cloud Platform → Kubernetes → click Create Cluster → block Node Configuration → Fixed with GPU.
You can see the availability of GPUs in regions in the [GPUs for Managed Kubernetes] availability matrix(/control-panel-actions/availability-matrix.mdx#gpu-for-cloud-and-kubernetes).
NVIDIA® A100
Has maximum performance for AI, HPC, and data processing. Suitable for deep learning, scientific research and data analytics.
Ampere® based, up to 2 TB/s throughput. Refer to NVIDIA® documentation for detailed specifications.
In fixed Managed Kubernetes cluster configurations, 1 to 8 GPUs × 40 GB are available, with vCPUs from 6 to 48, RAM from 87 to 704 GB.
NVIDIA® Tesla T4
Suitable for Machine Learning and Deep Learning, inference, graphics work and video rendering. Works with most AI frameworks and is compatible with all types of neural networks.
Based on Turing®, up to 300 GB/s throughput. Refer to NVIDIA® documentation for detailed specifications.
In fixed Managed Kubernetes cluster configurations, 1 to 4 GPUs × 16 GB are available, with vCPUs from 4 to 24, RAM from 32 to 320 GB.
NVIDIA® A30
Suitable for AI-inference, HPC, language processing, conversational artificial intelligence, recommender systems.
Ampere® based, up to 933 GB/s bandwidth. Refer to NVIDIA® documentation for detailed specifications.
In fixed Managed Kubernetes cluster configurations, 1 to 2 GPUs are available, with vCPUs from 16 to 48, RAM from 64 to 320 GB.
NVIDIA® A2
Entry-level GPU. Suitable for simple inference, video and graphics, Edge AI (edge computing), Edge video, mobile cloud gamification.
Ampere® based, up to 200 GB/s bandwidth. Refer to NVIDIA® documentation for detailed specifications.
In fixed Managed Kubernetes cluster configurations, 1 to 4 GPUs × 16 GB are available, with vCPUs from 12 to 48, RAM from 32 to 320 GB.
NVIDIA® GTX 1080
Performance and power-efficient GPU. The solution is implemented with FinFET technology and GDDR5X memory. Dynamic load balancing helps to separate tasks so that resources don't sit idle waiting. Featuring maximum performance for information display, VR, ultra-high resolution settings and data processing.
Pascal® based, up to 320.3 Gb/s throughput. Refer to NVIDIA® documentation for detailed specifications.
In fixed Managed Kubernetes cluster configurations, 1 to 8 GPUs × 8 GB are available, with vCPUs from 8 to 28, RAM from 24 to 96 GB.
NVIDIA® A2000
Power-efficient GPU for compact workstations. Suitable for AI, graphics and video rendering.
Ampere® based, up to 288 GB/s bandwidth. Refer to NVIDIA® documentation for detailed specifications.
In fixed Managed Kubernetes cluster configurations, 1 to 4 GPUs × 6 GB are available, with vCPUs from 6 to 24, RAM from 16 to 320 GB.
NVIDIA® A5000
A versatile GPU, suitable for any task within its performance limits.
Ampere® based, up to 768 GB/s bandwidth. Refer to NVIDIA® documentation for detailed specifications.
In fixed Managed Kubernetes cluster configurations, 1 to 2 GPUs × 24 GB are available, with vCPUs from 8 to 48, RAM from 32 to 320 GB.