Rapid advancements in AI have led to an extraordinary demand for GPUs, which are notoriously expensive and known for their high energy needs and tendency to sit idle. However, there are tools to enable faster AI innovation through accessibility and scalability.

A roadmap to assess GPU utilization

Prior to launching an AI project, it’s important to evaluate GPU usage to help establish a baseline for understanding how costs are accruing. Failure to do so can result in a significant blow to a company’s bottom line. Improved performance and resource allocation, enhanced workflows, and getting rid of inefficiencies are just a few of the benefits of an evaluation that addresses:

• Utilization rate
• Inference latency
• Cold start times
• Throughput
• Memory usage
• GPU idle time

Value assessment

To minimize GPU downtime, improve gross margins, and more accurately plan a budget, a cost/benefit analysis that uses monitoring tools can help to track GPU usage.

• Minimize GPU downtime: Downtime is an unnecessary and preventable cost. Analysis tools, like Nvidia’s run.ai, can give businesses insights into how their GPUs are being utilized and can identify inactive - and costly - GPUs.
• Optimize gross margins: Static provisioning or overprovisioning means that many AI inference rollouts run without meeting their capacity - typically below 30 percent. There are ways to streamline rollouts and save money: batch processing improves production by up to 70 percent without adding hardware to the mix.
• Accurate inference budgeting: A better understanding of usage patterns can help teams identify waste and save money. Machine learning teams should regularly run audits to get a better sense of their inference spending by connecting costs to usage patterns.

Autoscaling and optimization

Businesses can improve efficiencies in capital allocation by utilizing autoscaling and load balancing services, which can add or remove GPUs and other power sources automatically based on their task levels. This allows teams to focus on building and marketing products and meeting customer needs rather than worrying about the potential impact of their work on GPU usage.

Additionally, implementing techniques like quantization and model optimization allows for model simplification, thereby using less power and increasing efficiencies. As the interest for AI products drives demand for GPUs, developers must continue to develop efficiencies to allow for innovation.

Sharing resources

Sharing GPU pools enables more than one model to run on a GPU, thereby increasing utilization by up to 90 percent. Cloud providers like AWS and Google Cloud Platform can help with this.

As resources are reallocated, sharing them offers deployment flexibility without additional work.

Tools with GPU scheduling capabilities can be implemented to eliminate manually assigning GPU tasks; rather, these tools can automatically assign GPUs to tasks on an as-needed basis.

You can simplify job scheduling by assigning GPU resources using quotas, queuing, or multi-tenancy, or ensure that GPU resources are used when necessary by allowing workloads to share one GPU. Dashboards can help to track GPU utilization, identify where resources are needed, monitor them continuously and prevent GPU underutilization.

Autoscaling can free up GPUs when they’re not in use and can manage them in response to greater demand. Finally, teams can focus on development by relying on managed service providers that manage virtualization, monitoring, and autoscaling.

The bottom line is that it behooves businesses to optimize their GPU utilization by taking the necessary steps to minimize energy consumption, costs, and time.