-

Wallaroo.AI and Ampere Computing Collaborate to Bring Energy-Efficient, Low-Cost Machine Learning Inferencing to the Cloud

Joint Solution Helps Support Artificial Intelligence Growth with Breakthrough Performance, Reduced Infrastructure Requirements, Path to Sustainability Goals

NEW YORK--(BUSINESS WIRE)--Wallaroo.AI, the leader in scaling production machine learning (ML) from the cloud to the edge, today announced a strategic collaboration with Ampere® Computing to create optimized hardware/software solutions that provide reduced energy consumption, greater efficiency, and lower cost per inference for cloud artificial intelligence (AI).

Ampere processors are inherently more energy efficient than traditional AI accelerators. Now, with an optimized low-code/no-code ML software solution and customized hardware, putting AI into production in the cloud has never been easier or more cost-effective (even at cost-per-inference measure) or used less energy.

“This Wallaroo.AI/Ampere solution allows enterprises to deploy easily, improve performance, increase energy efficiency, and balance their ML workloads across available compute resources much more effectively,” said Vid Jain, chief executive officer of Wallaroo.AI, “all of which is critical to meeting the huge demand for AI computing resources today also while addressing the sustainability impact of the explosion in AI.”

“Through this collaboration, Ampere and Wallaroo.AI are combining Cloud Native hardware and optimized software to make ML production within the cloud much easier and more energy-efficient,” said Jeff Wittich, Chief Product Officer at Ampere. “That means more enterprises will be able to turn AI initiatives into business value more quickly.”

Breakthrough Cloud AI Performance

One of the key advantages of the collaboration is the integration of Ampere's built-in AI acceleration technology and Wallaroo.AI's highly-efficient Inference Server, part of the Wallaroo Enterprise Edition platform for production ML.

Benchmarks have shown as much as a 6x improvement over containerized x86 solutions on certain models like the open source ResNet-50 model. Tests were run using an optimized version of the Wallaroo Enterprise Edition on Dpsv5-series Azure virtual machines using Arm64 Azure virtual machines using Ampere® Altra® 64-bit processors; however, the optimized solution will also be available for other cloud platforms.

Benefits of Energy-Efficient AI

Reduced Hardware Needs/Costs - With a $15.7 trillion (U.S.) potential contribution to the global economy by 2030 (PwC), demand for AI has never been higher. However, the graphics processing units (GPUs) used to train AI models are in high demand. The quantities required for AI – and especially for large ML models like ChatGPT and other large language models (LLMs) – mean they are often not a cost-effective solution for AI/ML. For many enterprises, it is a better alternative to run software like the highly optimized Wallaroo.AI inference server, which can cost-efficiently run many AI/ML workloads with similar performance using currently available, advanced CPUs.

Supporting Sustainability/ESG Goals - The MIT Technology Review states that one AI training model uses more energy in a year than 100 U.S. homes. This means facility costs (power, cooling, etc.) of running GPUs can severely impact cloud providers as well as the power grid. Many clients of cloud providers also have environmental, social, governance (ESG) or sustainability initiatives that would be negatively impacted by large-scale adoption of AI with GPUs. Using optimized inference solutions on CPUs like the Ampere® Altra® Family of processors allows organizations to realize greater efficiency for inference workloads advancing both their need for AI/ML performance while simultaneously addressing their ESG goals for greater sustainability.

About Wallaroo.AI

Wallaroo.AI empowers enterprise AI teams to operationalize Machine Learning (ML) to drive positive outcomes with a software platform that enables deployment, observability, optimization and scalability of ML in the cloud, in decentralized networks, and at the edge. The unified Wallaroo.AI platform enables enterprise AI teams to easily deploy ML in seconds with no fuss or engineering overhead. They can then observe and optimize in real-time from a self-service operations center. Enterprises can run at scale with 80% less infrastructure and turbocharge their Databricks and Cloud ML production workflows using familiar dev tools. Wallaroo.AI is backed by Microsoft’s venture fund, M12, and leading VCs including Boldstart, Contour Ventures, Eniac, Greycroft, and Ridgeline. Learn more at www.wallaroo.ai.

About Ampere

Ampere is a modern semiconductor company designing the future of cloud computing with the world's first Cloud Native Processors. Built for the sustainable Cloud with the highest performance and best performance per watt, Ampere processors accelerate the delivery of all cloud computing applications. Ampere Cloud Native Processors provide industry-leading cloud performance, power efficiency and scalability. For more information visit Ampere Computing.

Contacts

Wallaroo Public Relations
Matt Stubbs
mattstubbs@voxuspr.com

Wallaroo.AI


Release Versions

Contacts

Wallaroo Public Relations
Matt Stubbs
mattstubbs@voxuspr.com

More News From Wallaroo.AI

Wallaroo.AI Selected by US Space Force SDA TAP Lab Apollo Accelerator Program

NEW YORK--(BUSINESS WIRE)--Wallaroo.AI, a leader in scalable AI inference solutions, is proud to announce its selection for the prestigious US Space Force (USSF) SDA TAP Lab Apollo Accelerator program in Colorado Springs. A two-time SpaceWERX Orbital Prime awardee, Wallaroo.AI’s participation in this program solidifies its position at the forefront of technological innovation to enhance the security and functionality of aerospace and defense systems critical to the United States, its allies, an...

Wallaroo.AI Introduces New ML Production Best Practices Workshop and Certification

NEW YORK--(BUSINESS WIRE)--Wallaroo.AI, the leader in scaling production machine learning (ML) from the cloud to the edge, is pleased to announce the launch of its new ML Production Best Practices Workshops. This workshop is designed to empower ML practitioners, including data scientists and ML engineers, with an understanding of step-by-step requirements to successfully transition ML projects from prototype to production environments quickly and at scale. The ML Production Best Practices Build...

New Research Identifies Scale and Automation as Common Keys to Successful AI

NEW YORK--(BUSINESS WIRE)--A new survey report commissioned by Wallaroo.AI, the leader in scaling production machine learning (ML) from the cloud to the edge, identifies the common characteristics among organizations that have found success in their artificial intelligence (AI) initiatives. The survey, “Lessons from Leading Edge: Machine Learning Best Practices and Warnings from Chief Data Officers,” provides a clear picture of how leading-edge organizations find business value from ML, how the...
Back to Newsroom