Learn Editor: Update provisioned-throughput.md

Author: sydneemayers

articles/ai-services/openai/concepts/provisioned-throughput.md

@@ -58,7 +58,7 @@ For a full list see the [Azure OpenAI Service in Azure AI Foundry portal calcula
 
 
 > [!NOTE]
-> Global provisioned deployments are only supported for gpt-4o, 2024-08-06 and gpt-4o-mini, 2024-07-18 models at this time. Data zone provisioned deployments are only supported for gpt-4o, 2024-08-06, gpt-4o, 2024-05-13, and gpt-4o-mini, 2024-07-18 models at this time. For more information on model availability, review the [models documentation](./models.md).
+> Global provisioned deployments are only supported for gpt-4o and gpt-4o-mini models at this time. Data zone provisioned deployments are only supported for gpt-4o and gpt-4o-mini models at this time. For more information on model availability, review the [models documentation](./models.md).
 
 ## Key concepts
 
@@ -73,11 +73,11 @@ az cognitiveservices account deployment create \
 --name <myResourceName> \
 --resource-group  <myResourceGroupName> \
 --deployment-name MyDeployment \
---model-name gpt-4 \
---model-version 0613  \
+--model-name gpt-4o \
+--model-version 2024-08-06  \
 --model-format OpenAI \
---sku-capacity 100 \
---sku-name ProvisionedManaged
+--sku-capacity 15 \
+--sku-name GlobalProvisionedManaged
 ```
 
 ### Quota
@@ -132,7 +132,7 @@ If an acceptable region isn't available to support the desire model, version and
 
 ### Determining the number of PTUs needed for a workload
 
-PTUs represent an amount of model processing capacity. Similar to your computer or databases, different workloads or requests to the model will consume different amounts of underlying processing capacity. The conversion from call shape characteristics (prompt size, generation size and call rate) to PTUs is complex and nonlinear. To simplify this process, you can use the [Azure OpenAI Capacity calculator](https://oai.azure.com/portal/calculator) to size specific workload shapes.
+PTUs represent an amount of model processing capacity. Similar to your computer or databases, different workloads or requests to the model will consume different amounts of underlying processing capacity. The conversion from throughput needs to PTUs can be approximated using historical token usage data or call shape estimations (input tokens, output tokens, and requests per minute) as outlined in our [performance and latency](../how-to/latency.md) documentation. To simplify this process, you can use the [Azure OpenAI Capacity calculator](https://oai.azure.com/portal/calculator) to size specific workload shapes.
 
 A few high-level considerations:
 - Generations require more capacity than prompts