Provisioned Throughput Units (PTUs) in Azure OpenAI Service provide predictable costs and performance by allocating a specific throughput capacity. The token quota limit for fine-tuned models is typically measured in Tokens Per Minute (TPM) rather than a fixed number of tokens. As for deploying multiple fine-tuned models, the pricing is based on the total throughput capacity allocated. You can deploy multiple models as long as the combined throughput remains within the reserved capacity. It's advisable to monitor usage and adjust the capacity as needed to optimize costs.
If we are doing it on test server on different subscription for client and want to transfer/share to main subscription, is it possible, on same region?
Provisioned Throughput Units (PTUs) in Azure OpenAI Service provide predictable costs and performance by allocating a specific throughput capacity. The token quota limit for fine-tuned models is typically measured in Tokens Per Minute (TPM) rather than a fixed number of tokens. As for deploying multiple fine-tuned models, the pricing is based on the total throughput capacity allocated. You can deploy multiple models as long as the combined throughput remains within the reserved capacity. It's advisable to monitor usage and adjust the capacity as needed to optimize costs.
If we are doing it on test server on different subscription for client and want to transfer/share to main subscription, is it possible, on same region?
OpenAI Service Provisioned Throughput Units (PTUs) are designed to provide predictable costs and performance by allocating a specific throughput capacity. There isn't a specific token quota limit mentioned for finetuned model deployments under PTUs, but the throughput is measured in Tokens Per Minute (TPM).
You can use the Azure capacity planner to estimate the required PTUs based on your expected TPM usage.
You can deploy multiple finetuned models under the Provisioned capacity plan. The pricing will be affected by the total throughput required for all models combined. Each model's deployment will consume a portion of the allocated PTUs, and you need to ensure that the total TPM usage across all models does not exceed the provisioned capacity.
could you share more about your expected usage patterns? and how many models do you plan to deploy?
Great explanation, Abdulrhman. To add, when deploying multiple finetuned models under the Provisioned capacity plan, it's important to monitor the total throughput to ensure it aligns with the allocated PTUs. This helps avoid exceeding the provisioned capacity and ensures optimal performance. Keep in mind that each model's deployment will contribute to the overall throughput usage, so planning and monitoring are key.
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Hello Team,
We are building a Azure OpenAI based finetuned model making use of GPT 4o-mini for long run.
Wanted to understand the costing, here we came up with the following question over Azure OpenAI Service Provisioned Reservations plan PTU units.
Need to understand how it works:
Is there any Token Quota Limit Provisioned finetuned model deployment?
How many finetuned model with Provisioned capacity can be deployed under the plan, How will the pricing affect if we deploy multiple finetune model?
Model Deployment - GPT 4o-mini finetuned
Region - North Central US
We are doing it for our enterprise customer, kindly help us to resolve this issue.
","body@stringLength":"824","rawBody":"
Hello Team,
We are building a Azure OpenAI based finetuned model making use of GPT 4o-mini for long run.
Wanted to understand the costing, here we came up with the following question over Azure OpenAI Service Provisioned Reservations plan PTU units.
Need to understand how it works:
Is there any Token Quota Limit Provisioned finetuned model deployment?
How many finetuned model with Provisioned capacity can be deployed under the plan, How will the pricing affect if we deploy multiple finetune model?
Model Deployment - GPT 4o-mini finetuned
Region - North Central US
We are doing it for our enterprise customer, kindly help us to resolve this issue.
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Wanted to understand the costing, here we came up with the following question over 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If we are doing it on test server on different subscription for client and want to transfer/share to main subscription, is it possible, on same region?
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Provisioned Throughput Units (PTUs) in Azure OpenAI Service provide predictable costs and performance by allocating a specific throughput capacity. The token quota limit for fine-tuned models is typically measured in Tokens Per Minute (TPM) rather than a fixed number of tokens. As for deploying multiple fine-tuned models, the pricing is based on the total throughput capacity allocated. You can deploy multiple models as long as the combined throughput remains within the reserved capacity. It's advisable to monitor usage and adjust the capacity as needed to optimize costs.
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If we are doing it on test server on different subscription for client and want to transfer/share to main subscription, is it possible, on same region?
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Hi Sachins
OpenAI Service Provisioned Throughput Units (PTUs) are designed to provide predictable costs and performance by allocating a specific throughput capacity. There isn't a specific token quota limit mentioned for finetuned model deployments under PTUs, but the throughput is measured in Tokens Per Minute (TPM).
You can use the Azure capacity planner to estimate the required PTUs based on your expected TPM usage.
You can deploy multiple finetuned models under the Provisioned capacity plan. The pricing will be affected by the total throughput required for all models combined. Each model's deployment will consume a portion of the allocated PTUs, and you need to ensure that the total TPM usage across all models does not exceed the provisioned capacity.
could you share more about your expected usage patterns? and how many models do you plan to deploy?
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Great explanation, Abdulrhman. To add, when deploying multiple finetuned models under the Provisioned capacity plan, it's important to monitor the total throughput to ensure it aligns with the allocated PTUs. This helps avoid exceeding the provisioned capacity and ensures optimal performance. Keep in mind that each model's deployment will contribute to the overall throughput usage, so planning and monitoring are key.
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