Reducing GPU Costs for Production AI

There are several ways to reduce GPU costs for production AI, including using cost-effective GPU options, using cloud providers, using containerization, using GPU acceleration selectively, using model compression, and using auto-scaling. Another way to reduce GPU costs is by using MLOps tools.

Ways to reduce GPU costs:

1. Use cost-effective GPU options: Instead of using high-end GPUs, consider using cost-effective options such as the NVIDIA T4 or the AMD Radeon VII. These GPUs offer good performance at a lower cost.
2. Use cloud providers: Many cloud providers offer GPU instances at a lower cost compared to purchasing GPUs outright. Additionally, you can scale up or down the number of GPUs as needed, which can help you save on costs.
3. Use containerization: Containerization allows you to package your AI applications and dependencies into a single package, which can be easily deployed on any machine with the necessary dependencies. This can help you save on costs by allowing you to use lower-cost machines for running your AI applications.
4. Use GPU acceleration selectively: Instead of using GPUs for all AI tasks, consider using them only for the most compute-intensive tasks. This can help you save on costs by allowing you to use lower-cost CPUs for the rest of the tasks.
5. Use model compression: Model compression techniques such as pruning and quantization can help you reduce the size of your AI models, which can in turn help you reduce the number of GPUs needed to run your applications.
6. Use auto-scaling: Many cloud providers offer auto-scaling options that allow you to automatically scale up or down the number of GPUs based on the workload. This can help you save on costs by ensuring that you only pay for the resources you need.

MLOps tools like Modzy can help automate the end-to-end process of deploying, and managing machine learning models in production. By automating these tasks, MLOps tools can help reduce the cost of running AI applications by reducing the time and resources needed for manual tasks such as deployment and monitoring. Additionally, MLOps tools can help optimize resource utilization by automatically scaling up or down the number of GPUs based on workload, which can help reduce GPU costs.

This tech talk explores how you can efficiently use GPU resources for production inferences. We walk through some of the common approaches and potential pitfalls with using GPUs, and help you identify the most efficient and cost effective method to meet your team's needs and resources.

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References:

Blog post on getting better performance out of CPUs

AWS cost information

Server build comparable to AWS’s g4dn.8xlarge

Serverless architecture in Azure

AI Infrastructure Alliance Landscape

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