Cost-Effective GPU Solutions for AI Workloads with OpenStack - Optimization Strategies

Overview

Discover how to optimize AI workload costs using affordable GPUs and desktop workstations for cloud instances, with practical insights from ArcFusion AI and NIPA Cloud's successful implementation.

Syllabus

• Introduction to AI Workloads and GPU Cost Optimization
Overview of AI workload demands
Importance of cost-effective GPU solutions
• Understanding OpenStack for AI Workloads
Introduction to OpenStack cloud platform
Key features relevant to AI workloads
• Identifying Affordable GPU Options
Overview of GPU types and pricing
Selection criteria for cost-effective GPUs
• Setting Up a Desktop Workstation for Cloud Instances
Hardware requirements
Configurations for optimal performance
• Deployment Strategies on OpenStack
Creating and managing instances
Integrating desktop GPUs with cloud instances
• Optimization Strategies for AI Workloads
Enhancing GPU utilization
Cost management techniques
• Case Study: ArcFusion AI and NIPA Cloud Implementation
Project background and objectives
Workflow and architectural design
Results and cost savings
• Practical Insights and Best Practices
Technical challenges and solutions
Tips for sustaining cost-effective operations
• Hands-On Lab: Setting Up a Test Environment
Step-by-step guide to configure a test environment
Simulating AI workloads with different configurations
• Course Wrap-Up and Future Trends
Summary and key takeaways
Emerging trends in GPU optimization for AI workloads

Subjects

Programming