AMD EPYC Fuels Mahindra’s Scalable HPC Platform for Design and Simulation

Challenging the perception that high-performance computing (HPC) in the cloud is costly, Mahindra has modernized its HPC infrastructure by adopting AMD EPYC™ processor-powered virtual machines on Google Cloud. This move enhances compute efficiency across Mahindra’s HPC workloads, enabling Mahindra to drive new revenue streams while boosting efficiency and reducing total cost of ownership (TCO).

Mahindra built an on-demand HPC platform to support compute-intensive workloads such as product design and virtual product simulation activities. During the digital-first launch of the Thar ROXX five-door SUV, Mahindra processed approximately 200,000 vehicle reservations online in just 1.5 hours – a feat not possible through traditional showroom-based rollouts. AMD EPYC processors deliver the performance, memory bandwidth, and core density needed to run these workloads at scale in the cloud – achieving significant infrastructure and software licensing cost savings.

Key highlights include: 

• Cost Optimization: Achieved approximately 40 percent overall savings. 
• Performance and Efficiency: Improved performance per core while reducing licensing costs.
• Scalable Platform: Infrastructure supports HPC, design, and enterprise workloads.
• Faster Innovation: Enabled faster design processing and more responsive digital experiences.

"By moving our workloads to AMD EPYC CPU-based virtual machines, we have seen greater application performance and up to 40 percent cost savings. The performance and efficiency of AMD EPYC processors have strengthened our business case and accelerated our IT transformation," said Abhishek Sukhwal, Head of Infrastructure, Mahindra Group.

"AMD EPYC processors are engineered to help enterprises like Mahindra accelerate innovation, whether on-prem or in the cloud," said Vinay Sinha, Managing Director – India Sales, AMD. "By delivering a strong combination of performance, core density, and energy efficiency, EPYC processors empower organizations to scale compute-intensive workloads while optimizing TCO."

To learn more, visit:
www.amd.com/en/resources/case-studies/mahindra.html