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AWS Graviton Case Study
AWS Graviton
AI Workloads
FinTech
Accelerating AI
Workloads with
AWS Graviton
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.
25%
AI Inference Faster
40%
Compute Cost Reduced
ARM64
Graviton3 Optimized
Graviton3 Performance Dashboard
AI Inference Speed
+25%
↑ vs x86
Compute Cost
−40%
↓ vs x86
Instance Type
C7g
↑ Graviton3
Architecture
ARM64
↑ Optimized
25%
AI Faster
↑ Inference
40%
Cost Saved
↓ Compute
C7g
EC2 Instance
↑ Graviton3
Challenges
Performance bottlenecks and rising costs driving infrastructure transformation
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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.
Performance bottlenecks as the demand for AI computations continued to increase on the existing infrastructure.
Rising operational costs from compute-intensive AI workloads running on traditional x86-based instances.
Need for advanced high-performance computing to efficiently manage real-time analytics, AI-driven recommendations, and user engagement insights.
Solutions Provided
AWS Graviton-powered EC2 instances optimized for AI-driven workloads
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.
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Result Outcome
AI performance, cost efficiency, and platform scalability — all delivered
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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.
25%
AI Inference Performance Improved
Enabling much faster workout recommendations for users
40%
Compute Expenses Reduced
Compared to traditional x86-based instances
↑
Platform Stability & Scalability
Larger user base supported without latency issues
AI Inference Performance
+25%
Faster workout recommendations for users
Compute Cost Reduction
−40%
Through AWS Graviton and Spot Instances
Transformation
Before vs After: x86 to AWS Graviton3
✕ Before — x86 Infrastructure
Performance bottlenecks under high AI computation demand
Rising operational costs from x86-based compute
TensorFlow and PyTorch not optimized for ARM64 architecture
Slower AI inference — delayed workout recommendations
Background data processing creating API latency
✓ After — AWS Graviton3 (C7g)
25% improvement in AI inference performance
40% compute cost reduction via Graviton and Spot Instances
TensorFlow and PyTorch optimized for ARM64 — reduced inference times
Faster workout recommendations — enhanced user experience
Lambda and Fargate with Graviton streamlining background processing
Conclusion
Key learnings from the AWS Graviton migration
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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.
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Technology Stack
AWS Services & Technologies Deployed
EC2 C7g Instances
Graviton3 Compute
TensorFlow (ARM64)
AI Model Framework
PyTorch (ARM64)
AI Model Framework
AWS Lambda
Serverless Processing
AWS Fargate
Container Compute
EC2 Spot Instances
Cost Optimization
ARM64 Architecture
Graviton3 Platform
Amazon CloudWatch
Performance Monitoring
Accepting New Enterprise Clients
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