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37 lines
2.3 KiB
Plaintext
37 lines
2.3 KiB
Plaintext
// Module included in the following assemblies:
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//
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// * hardware_accelerators/about-hardware-accelerators.adoc
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:_mod-docs-content-type: CONCEPT
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[id="nvidia-gpu-csps_{context}"]
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ifndef::openshift-dedicated,openshift-rosa[]
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= GPUs and CSPs
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endif::openshift-dedicated,openshift-rosa[]
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ifdef::openshift-rosa,openshift-dedicated[]
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= GPUs and {product-title}
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endif::openshift-rosa,openshift-dedicated[]
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ifndef::openshift-dedicated,openshift-rosa[]
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You can deploy {product-title} to one of the major cloud service providers (CSPs): {aws-first}, {gcp-full}, or Microsoft Azure.
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Two modes of operation are available: a fully managed deployment and a self-managed deployment.
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* In a fully managed deployment, everything is automated by Red{nbsp}Hat in collaboration with CSP. You can request an OpenShift instance through the CSP web console, and the cluster is automatically created and fully managed by Red{nbsp}Hat. You do not have to worry about node failures or errors in the environment. Red{nbsp}Hat is fully responsible for maintaining the uptime of the cluster. The fully managed services are available on {aws-short}, {azure-short}, and {gcp-short}. For {aws-short}, the OpenShift service is called (Red{nbsp}Hat OpenShift Service on AWS). For Azure, the service is called Azure Red{nbsp}Hat OpenShift. For {gcp-short}, the service is called OpenShift Dedicated on {gcp-short}.
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* In a self-managed deployment, you are responsible for instantiating and maintaining the OpenShift cluster. Red{nbsp}Hat provides the OpenShift-install utility to support the deployment of the OpenShift cluster in this case. The self-managed services are available globally to all CSPs.
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endif::openshift-dedicated,openshift-rosa[]
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ifdef::openshift-dedicated,openshift-rosa[]
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You can deploy {product-title} on NVIDIA GPU instance types.
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endif::openshift-dedicated,openshift-rosa[]
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It is important that this compute instance is a GPU-accelerated compute instance and that the GPU type matches the list of supported GPUs from NVIDIA AI Enterprise. For example, T4, V100, and A100 are part of this list.
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You can choose one of the following methods to access the containerized GPUs:
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* GPU passthrough to access and use GPU hardware within a virtual machine (VM).
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* GPU (vGPU) time slicing when the entire GPU is not required.
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