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Merge pull request #259941 from ChrisHMSFT/main
Update provisioned-throughput.md
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articles/ai-services/openai/concepts/provisioned-throughput.md

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@@ -20,7 +20,7 @@ The provisioned throughput capability allows you to specify the amount of throug
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- **Predictable performance:** stable max latency and throughput for uniform workloads.
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- **Reserved processing capacity:** A deployment configures the amount of throughput. Once deployed, the throughput is available whether used or not.
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- **Cost savings:** High throughput workloads will result in cost savings vs token-based consumption.
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- **Cost savings:** High throughput workloads may provide cost savings vs token-based consumption.
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An Azure OpenAI Deployment is a unit of management for a specific OpenAI Model. A deployment provides customer access to a model for inference and integrates additional features like Content Moderation ([See content moderation documentation](content-filter.md)).
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Quota is specific to a (deployment type, mode, region) triplet and isn't interchangeable. Meaning you can't use quota for GPT-4 to deploy GPT-35-turbo. Customers can raise a support request to move the quota across deployment types, models, or regions but we can't guarantee that it will be possible.
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While we make every attempt to ensure that quota is always deployable, quota does not represent a guarantee that the underlying capacity is available for the customer to use. The service assigns capacity to the customer at deployment time and if capacity is unavailable the deployment will fail with an out of capacity error.
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While we make every attempt to ensure that quota is always deployable, quota does not represent a guarantee that the underlying capacity is available for the customer to use. The service assigns capacity to the customer at deployment time and if capacity is unavailable the deployment will fail with an out of capacity error.

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