1. Executive Summary
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- Overview of NVIDIA’s GPU Market for Training vs Inference
- Key Findings on GPU Demand from 2025 to 2035
- Competitive Landscape and Future Challenges
2. NVIDIA GPU Demand: Training vs Inference
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- Underlying Demand for NVIDIA GPUs for Training vs Inference (Today & Future Outlook)
- Estimated FLOPS Required for Training and Inference
- Balancing Installed Capacity and Avoiding Overcapacity
- Breakdown of GPU Demand: Hyperscalers vs. Enterprise Purchases
- Impact of Mega Cap Tech Companies (Google, Microsoft, Amazon, Meta) on GPU Demand
- Future Predictions for GPU Orders from Hyperscalers (2025-2035)
3. Training Large AI Models: GPU vs. ASICs
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- Why NVIDIA GPUs Are Used for Inference Instead of ASICs
- Cost Considerations: GPUs vs. ASICs for Inference
- Performance and Scalability Benefits of GPUs Over ASICs for Inference Tasks
4. Future of NVIDIA GPUs: Comparing B100 with H100/H200
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- Technological Advantages of B100 Over Previous Generations
- Performance, Power Efficiency, and Use Case Improvements
- Market Implications of B100’s Launch (2025 and Beyond)
5. NVIDIA vs. AMD GPUs: Customer Choices and Trade-offs
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- Competitive Differences Between NVIDIA and AMD GPUs
- How Far is AMD’s ROCm from NVIDIA’s CUDA in Terms of Performance and Compatibility
- Key Sacrifices and Benefits of Choosing AMD Over NVIDIA GPUs
6. GPU Cluster Installation: From Setup to Model Deployment
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- Step-by-Step Process of Installing GPU Clusters and Related Equipment
- Time Required to Install a Fully Operational AI GPU Cluster
- Bottlenecks and Difficult Components in Setting Up AI Datacenters (Beyond GPUs)
- Complete Training and Inference Workflow for Large-Scale AI Models
7. Challenges in LLMs (Large Language Models): Generalization vs. Accuracy
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- Performance of LLMs in Generalization vs. Accuracy
- Methods for Improving Accuracy in LLMs Beyond Scaling Model Size
- Exploring Breakthrough Ideas in Research for LLMs and Model Accuracy (2025-2035)
8. Cutting-Edge Research in AI Model Development
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- Emerging Trends and Key Research Areas in AI Model Design
- Potential Breakthroughs in AI, Similar to the Introduction of the Transformer (2017)
- Future Directions for AI Models in High-Performance Computing and GPU Usage
9. Future Market Outlook: NVIDIA GPUs (2025-2035)
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- Expected Demand Growth for Training and Inference GPUs
- Shifts in GPU Demand Due to Emerging AI Technologies
- Impact of New Competitors and Potential Market Disruptions
10. Conclusion
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- Summary of Key Findings on NVIDIA GPUs for Training and Inference
- Strategic Recommendations for Enterprises and Hyperscalers
- Long-Term Outlook for GPU Demand and Technological Advancements
11. Appendices
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- Glossary of Technical Terms Related to GPUs, AI Training, and Inference
- Charts and Data on GPU Demand, Capacity, and Performance Comparison
- References and Industry Sources for GPU Market Analysis
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