Challenges
Mufinpay, a leading AI-powered Payment platform, required advanced high-performance computing to efficiently manage real-time analytics, AI-driven workout recommendations, and user engagement insights. Their existing infrastructure was facing performance bottlenecks and rising operational costs as the demand for AI computations continued to increase.
Solutions Provided
To boost compute performance and cut operational costs, we deployed AWS Graviton-powered EC2 instances optimized for AI-driven workloads.
We migrated all workloads to Graviton3-based EC2 C7g instances to achieve faster and more efficient AI model processing.
TensorFlow and PyTorch models were further optimized for the ARM64 architecture, resulting in significantly reduced inference times.
We also integrated AWS Lambda and AWS Fargate with Graviton to streamline background data processing and improve API responsiveness.
Overall, compute costs were reduced by nearly 40% through the effective use of AWS Graviton and Spot Instances.
Result Outcome
AI inference performance improved by 25%, enabling much faster workout recommendations for users.
Compute expenses were reduced by 40% compared to traditional x86-based instances.
Overall application stability and scalability increased significantly, allowing the platform to support a larger user base without any latency issues.
Conclusion
AI workloads gain substantial performance improvements when using ARM-optimized libraries.
AWS Graviton provides faster and more efficient compute processing while keeping infrastructure costs low.
However, migrating AI models to Graviton requires detailed performance testing and proper optimization to achieve the best results.
