We've been approached by Microsoft to consider Provisioned Throughput Units (PTUs) licensing for Azure OpenAI capacity. My understanding is that this provides dedicated capacity to the organisation and is offered at Microsoft's discretion (possibly only to larger organisations with sufficient demand). Does anyone have any experience or learnings around the PTU licensing model compared to the existing/traditional licencing model? Has this been worthwhile for you? What should we be aware of?
Sort By:
Oldest
CIO in Educationa month ago
Your understanding is correct. We are considering as well and are weighing the pros and cons. You should try and figure out what your usage may (or may not) be that would make it more favorable for you to consider the cost and performance of the application(s) you're building in the environment.Global Intelligent Automation & GenAI Leader in Healthcare and Biotecha month ago
While PTUs offer significant advantages in managing cost, performance, and energy efficiency, they also introduce a new constraint that could complicate the prediction of an AI project's total cost and value. This model is likely to become a crucial tool for enterprises as they transition from deployment to ongoing AI operations, helping to measure and control costs more effectively while aligning with sustainability goals.But let's dive into the why that is:
Provisioned Throughput Units (PTU) in Azure OpenAI represent a strategic approach to managing the performance, cost, and energy consumption of AI models, particularly large language models (LLMs). PTUs allow for the reservation of specific computational resources, ensuring predictable and stable performance, which is crucial for high-throughput tasks.
Key Benefits:
Guaranteed Performance:
Stable performance and minimal latency for consistent workloads.
Cost Efficiency:
Potential for cost savings by reserving capacity upfront, especially for large-scale AI deployments.
Energy Management:
In an increasingly energy-conscious world, PTUs help align computational needs with available power, optimizing resource usage and reducing waste.
Considerations:
Added Complexity:
While PTUs offer predictability, they introduce another layer of complexity that could make it harder to accurately forecast the overall cost and value of an AI project. This model might shift some of the cost assessment to post-deployment, where PTUs become a key metric for ongoing operations.
Enterprise Implications:
Enterprises have recognized that without careful management, the cost of AI services like GPT chat can escalate rapidly, potentially reaching tens of thousands of dollars without a clear correlation to the value generated. PTUs aim to mitigate this risk by providing a more controlled and measurable way to manage these costs.