update

Author: mrbullwinkle

articles/ai-services/openai/how-to/quota.md

@@ -53,6 +53,10 @@ Post deployment you can adjust your TPM allocation by selecting and editing your
 > [!IMPORTANT]
 > Quotas and limits are subject to change, for the most up-date-information consult our [quotas and limits article](../quotas-limits.md).
 
+## Request more quota
+
+Quota increase requests can be submitted via the [quota increase request form](https://aka.ms/oai/stuquotarequest). Due to high demand, quota increase requests are being accepted and will be filled in the order they're received. Priority is given to customers who generate traffic that consumes the existing quota allocation, and your request might be denied if this condition isn't met.
+
 ## Model specific settings
 
 Different model deployments, also called model classes have unique max TPM values that you're now able to control. **This represents the maximum amount of TPM that can be allocated to that type of model deployment in a given region.** 
@@ -71,7 +75,7 @@ For an all up view of your quota allocations across deployments in a given regio
 - **Deployment**: Model deployments divided by model class.
 - **Quota type**: There's one quota value per region for each model type. The quota covers all versions of that model.  
 - **Quota allocation**: For the quota name, this shows how much quota is used by deployments and the total quota approved for this subscription and region. This amount of quota used is also represented in the bar graph.
-- **Request Quota**: The icon navigates to a form where requests to increase quota can be submitted.
+- **Request Quota**: The icon navigates to [this form](https://aka.ms/oai/stuquotarequest) where requests to increase quota can be submitted.
 
 ## Migrating existing deployments
 

articles/ai-services/openai/quotas-limits.md

@@ -72,7 +72,7 @@ The following sections provide you with a quick guide to the default quotas and
 >
 > This is particularly important for programmatic model deployment as this change in RPM/TPM ratio can result in accidental under allocation of quota if one is still assuming the 1:1000 ratio followed by older chat completion models.
 >
-> There is a known issue with the [quota/usages API](/rest/api/aiservices/accountmanagement/usages/list?view=rest-aiservices-accountmanagement-2024-06-01-preview&tabs=HTTP&preserve-view=true) where it assumes the old ratio applies to the new o1-series models. The API returns the correct base capacity number, but does not apply the correct ratio for the accurate calculation of TPM.
+> There is a known issue with the [quota/usages API](/rest/api/aiservices/accountmanagement/usages/list?view=rest-aiservices-accountmanagement-2024-06-01-preview&tabs=HTTP&preserve-view=true) where it assumes the old ratio applies to the new o1-series models. The API returns the correct base capacity number, but doesn't apply the correct ratio for the accurate calculation of TPM.
 
 ### o1 & o1-mini global standard
 
@@ -134,12 +134,12 @@ M = million | K = thousand
 
 #### Usage tiers
 
-Global standard deployments use Azure's global infrastructure, dynamically routing customer traffic to the data center with best availability for the customer’s inference requests. Similarly, Data zone standard deployments allow you to leverage Azure global infrastructure to dynamically route traffic to the data center within the Microsoft defined data zone with the best availability for each request. This enables more consistent latency for customers with low to medium levels of traffic. Customers with high sustained levels of usage might see more variability in response latency.
+Global standard deployments use Azure's global infrastructure, dynamically routing customer traffic to the data center with best availability for the customer’s inference requests. Similarly, Data zone standard deployments allow you to leverage Azure global infrastructure to dynamically route traffic to the data center within the Microsoft defined data zone with the best availability for each request. This enables more consistent latency for customers with low to medium levels of traffic. Customers with high sustained levels of usage might see greater variability in response latency.
 
 The Usage Limit determines the level of usage above which customers might see larger variability in response latency. A customer’s usage is defined per model and is the total tokens consumed across all deployments in all subscriptions in all regions for a given tenant.
 
 > [!NOTE]
-> Usage tiers only apply to standard, data zone standard, and global standard deployment types. Usage tiers do not apply to global batch and provisioned throughput deployments.
+> Usage tiers only apply to standard, data zone standard, and global standard deployment types. Usage tiers don't apply to global batch and provisioned throughput deployments.
 
 #### GPT-4o global standard, data zone standard, & standard