Optimizing AI Processing: Exploring Innovative Methods and Expert Strategies
Streamlining AI Efficiencies: Maximizing Performance, Minimizing Costs, and Enhancing Privacy
Businesses looking to improve real-time AI applications, such as self-driving cars or healthcare monitoring, can benefit from optimizing inference processes. This optimization can not only boost AI efficiency, but also decrease energy consumption, reduce operational costs, and improve customer satisfaction.
Some of the common challenges faced by companies when managing AI efficiencies include:
- Underutilized GPU clusters due to uneven workflows
- Defaulting to large general-purpose models for tasks that could run on smaller, cheaper open-source models, due to a lack of knowledge and steep learning curve
- A lack of insight into the real-time cost for each request, leading to inflated bills
To address these issues, teams can consider the following strategies:
- Optimize batching, model size, and GPU utilization to cut inference bills by up to 80%
- Use tools like PromptLayer and Helicone to gain insights into real-time costs
- Switch to a serverless pay-as-you-go model for spiky workflows
Running larger language models (LLMs) requires significantly more power, with an average of 40-50% of data center energy used to power the computing equipment. To save energy and costs, it may be more beneficial for a company to consider an on-premises provider instead of a cloud provider.
Privacy and security are also essential considerations when optimizing AI efficiencies. Cisco's 2025 Data Privacy Benchmark Study found that 64% of respondents worry about sharing sensitive information publicly or with competitors when using generative AI tools, increasing the risk of non-compliance. To ensure privacy and security, businesses can opt for services deployed in their cloud rather than running models across different customer organizations on a shared infrastructure.
Customer satisfaction is another key factor for companies to consider. A delayed response can lead to users dropping off and reduced adoption of the application. Applications can also suffer from hallucinations and inaccuracy, limiting their impact and widespread adoption.
By optimizing inference, businesses can avoid these pitfalls and see significant improvements in their AI performance, energy usage, costs, privacy, and customer satisfaction. Cleanlab, a company that launched the Trustworthy Language Model, was able to reduce GPU costs, cut costs by 90%, and go live within two weeks without incurring additional engineering overhead costs by implementing serverless inference.
Optimizing model architectures, compressing model size, and leveraging specialized hardware are other strategies that can help businesses further optimize their AI performance. By taking these steps, companies can achieve significant cost savings, faster inference, and improved results.
- In order to address the challenges of managing AI efficiencies in data-and-cloud-computing, businesses can optimize batching, model size, and GPU utilization, which could potentially cut inference bills by up to 80%.
- As privacy and security are crucial when optimizing AI efficiencies, companies can consider deploying their models in their own cloud instead of running them on a shared infrastructure across different customer organizations, minimizing the risk of non-compliance and enhancing privacy.
