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articles/machine-learning/concept-compute-target.md

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@@ -76,7 +76,6 @@ There are a few exceptions and limitations to choosing a VM size:
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* Some VM series aren't supported in Azure Machine Learning.
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* There are some VM series, such as GPUs and other special SKUs, which may not initially appear in your list of available VMs. But you can still use them, once you request a quota change. For more information about requesting quotas, see [Request quota increases](how-to-manage-quotas.md#request-quota-increases).
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See the following table to learn more about supported series.
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| **Supported VM series** | **Category** | **Supported by** |

articles/machine-learning/how-to-manage-quotas.md

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| Workspaces per resource group | 800 |
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| Nodes in a single Azure Machine Learning Compute (AmlCompute) **cluster** setup as a non communication-enabled pool (i.e. cannot run MPI jobs) | 100 nodes but configurable up to 65000 nodes |
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| Nodes in a single Parallel Run Step **run** on an Azure Machine Learning Compute (AmlCompute) cluster | 100 nodes but configurable up to 65000 nodes if your cluster is setup to scale per above |
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| Nodes in a single Azure Machine Learning Compute (AmlCompute) **cluster** setup as a communication-enabled pool | 300 nodes but configurable up to 4000 nodes |
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| Nodes in a single Azure Machine Learning Compute (AmlCompute) **cluster** setup as a communication-enabled pool on an RDMA enabled VM Family | 100 nodes |
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| Nodes in a single Azure Machine Learning Compute (AmlCompute) **cluster** set up as a communication-enabled pool | 300 nodes but configurable up to 4000 nodes |
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| Nodes in a single Azure Machine Learning Compute (AmlCompute) **cluster** set up as a communication-enabled pool on an RDMA enabled VM Family | 100 nodes |
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| Nodes in a single MPI **run** on an Azure Machine Learning Compute (AmlCompute) cluster | 100 nodes but can be increased to 300 nodes |
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| Job lifetime | 21 days<sup>1</sup> |
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| Job lifetime on a low-priority node | 7 days<sup>2</sup> |

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