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Starts 7 June 2025 18:46
Ends 7 June 2025
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Memory Expansion Requirements for AI Systems in Hyperscale Data Centers
Explore evolving memory architecture requirements for AI systems in hyperscale data centers, focusing on GPU acceleration and infrastructure optimization for next-generation applications.
Open Compute Project
via YouTube
Open Compute Project
2544 Courses
33 minutes
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Overview
Explore evolving memory architecture requirements for AI systems in hyperscale data centers, focusing on GPU acceleration and infrastructure optimization for next-generation applications.
Syllabus
- Introduction to AI Systems in Hyperscale Data Centers
- Memory Architecture Fundamentals
- GPU Acceleration in AI Systems
- Memory Requirements for Next-Generation AI Applications
- Infrastructure Optimization
- Case Studies
- Future Trends and Challenges
- Conclusion and Best Practices
Overview of AI systems and hyperscale data centers
Importance of memory architecture in AI workloads
Traditional vs. modern memory architectures
Role of memory in AI and data-driven applications
Basics of GPU architecture and design
GPU vs. CPU: Performance and memory requirements
Deep learning models and memory consumption
Memory bandwidth and latency considerations
Scaling memory for hyperscale environments
Network infrastructure and its impact on memory usage
Real-world applications and their memory architecture challenges
Success stories in optimizing memory for AI workloads
Emerging memory technologies (e.g., HBM, GDDR)
Quantum computing and its implications for memory systems
Key takeaways for designing memory architectures
Best practices for implementing robust memory solutions in AI-driven data centers
Subjects
Programming
