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Merge branch 'gen-upd' into 'master'
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See merge request nvidia/cloud-native/cnt-docs!353
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mikemckiernan committed Dec 1, 2023
2 parents a56d5f5 + 7dd2864 commit 1acb1c2
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18 changes: 9 additions & 9 deletions gpu-operator/platform-support.rst
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Expand Up @@ -278,7 +278,7 @@ The GPU Operator has been validated in the following scenarios:
* - | Red Hat
| Enterprise
| Linux 8.4,
| 8.6, 8.7, 8.8
| 8.6---8.9
- 1.25---1.28
-
-
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| Engine 2
* - Ubuntu 20.04 LTS
- 1.21--1.27
- 1.22--1.28
-
- 7.0 U3c, 8.0 U2
- | 1.21, 1.22, 1.23,
- | 1.22, 1.23,
| 1.24, 1.25
* - Ubuntu 22.04 LTS
- 1.21--1.27
- 1.22--1.28
-
-
-
Expand All @@ -363,11 +363,11 @@ The GPU Operator has been validated in the following scenarios:
* - | Red Hat
| Enterprise
| Linux 8.4,
| 8.6---8.8
- 1.21---1.27
| 8.6---8.9
- 1.22---1.28
-
-
- 1.21---1.27
- 1.22---1.28


Supported Container Runtimes
Expand Down Expand Up @@ -405,8 +405,8 @@ Operating System Kubernetes KubeVirt OpenShift Virtual
\ \ | GPU vGPU | GPU vGPU
| Passthrough | Passthrough
================ =========== ============= ========= ============= ========
Ubuntu 20.04 LTS 1.21---1.27 0.36+ 0.59.1+
Ubuntu 22.04 LTS 1.21---1.27 0.36+ 0.59.1+
Ubuntu 20.04 LTS 1.22---1.28 0.36+ 0.59.1+
Ubuntu 22.04 LTS 1.22---1.28 0.36+ 0.59.1+
Red Hat Core OS 4.11, 4.12, 4.13
4.13
================ =========== ============= ========= ============= ========
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7 changes: 5 additions & 2 deletions kubernetes/index.rst
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Expand Up @@ -11,8 +11,11 @@
MIG Support in Kubernetes
#########################

The Multi-Instance GPU (MIG) feature allows the NVIDIA A100 GPU to be securely partitioned into up to
seven separate GPU Instances for CUDA applications, providing multiple users with separate GPU resources for
The Multi-Instance GPU (MIG) feature enables securely partitioning GPUs such as the NVIDIA A100 into
several separate GPU instances for CUDA applications.
For example, the NVIDIA A100 supports up to seven separate GPU instances.

MIG provides multiple users with separate GPU resources for
optimal GPU utilization. This feature is particularly beneficial for workloads that do not fully saturate the GPU's
compute capacity and therefore users may want to run different workloads in parallel to maximize utilization.

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