Run GPU intensive AI/ML workloads at faster performance and lower cost
—on migration to AWS
GPU workloads are critical for AI/ML, deep learning, and high performance computing—but running them on legacy infrastructure or multicloud setups often means:
• High capital & operational costs
• Underutilized GPU capacity
• Limited scalability during peak demand
AWS solves these challenges. With the broadest choice of GPU instances (G4, G5, P4, P5), elastic scaling, and AI optimized services, you can right-size GPU resources, improve performance, and pay only for what you use. With AWS, you gain:
🔹Elastic scaling for unpredictable workloads
🔹Access to the latest GPU technology without hardware refresh cycles
🔹Optimized cost controls with usage-based pricing
🔹Simplified management via cloud-native orchestration tools
For organizations in Malaysia, Singapore, and APAC, AWS ensures low latency regional hosting and reliable GPU availability without upfront hardware investment.
Why Move GPU Workloads to AWS?
✔ Scale Without Limits
Easily expand or reduce GPU capacity on demand—no upfront hardware investment needed.
✔ Lower Infrastructure Costs
Pay only for the resources you use, avoiding the expense of idle GPU clusters.
✔ Accelerate Performance
Access the latest high-performance GPU technology to speed up AI/ML training and inference.
✔ Simplify Management
Use cloud-native tools to deploy, monitor, and optimize workloads with less operational overhead.
Register now!
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What we delivered - with Impact
A retail AI company moved its training clusters from an on-prem setup to AWS GPU instances, cutting model training time by 50% and saving 45% on GPU infrastructure costs, while seamlessly handling traffic spikes during seasonal demand.
Our 5‑Step GPU Migration Path
We ensure your GPU workloads migrate safely and efficiently with minimal downtime:
Benchmark
We profile your existing workloads, identify compute requirements, and measure performance gaps.
Right Size
Select the ideal AWS GPU instance family (G4 to P5) based on your AI/ML needs.
Containerize & Prepare
Package workloads into containers or AMIs for seamless deployment.
Deploy & Integrate
Run workloads on EKS, ECS, or SageMaker—integrated with your data pipeline.
Monitor & Optimize
Set up CloudWatch & performance dashboards for ongoing cost performance tuning.
Services Offered
GPU Workload Assessment
Understand current usage, performance, and cost baseline
Migration Architecture Design
Define optimal AWS GPU deployment for your workloads
Automation & Orchestration
Setup auto-scaling, pipelines, and container orchestration
Cost & Performance Optimization
Implement dashboards and usage policies
Security & Compliance Configuration
Apply VPC, IAM, encryption, and regional governance
Training & Ongoing Support
Hands-on enablement and continuous monitoring
What You Can Achieve
Migrating GPU workloads to AWS enables faster innovation and better resource utilization:
🔹2X faster ML/DL training and inference cycles
🔹40–60% cost savings compared to static on-premises or multi-cloud setups
🔹On-demand elastic bursting for unpredictable AI/ML workloads
🔹Regional GPU availability for low-latency deployments
Why 1CloudHub?
We bring cloud-native GPU expertise backed by AWS partnerships:
✅ AWS Advanced Consulting Partner with deep AI/ML specialization
✅ Experience with GPU migrations for BFSI, ISVs & ML startups
✅ End-to-end services—from workload assessment to managed optimization
