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Starts 6 June 2025 09:24
Ends 6 June 2025
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Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architectures
Explore DeepSeek's latest research paper detailing their next model architecture with innovations in Multi-head Latent Attention, Mixture of Experts, FP8 training, and Multi-Plane Network Topology for enhanced AI infrastructure.
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Overview
Explore DeepSeek's latest research paper detailing their next model architecture with innovations in Multi-head Latent Attention, Mixture of Experts, FP8 training, and Multi-Plane Network Topology for enhanced AI infrastructure.
Syllabus
- Introduction to DeepSeek-V3
- Innovations in DeepSeek-V3
- Advanced Training Techniques
- Scaling Challenges in AI Architectures
- Reflections on Hardware for AI Architecture
- Conclusion and Future Directions
Overview of DeepSeek's latest research paper
Core objectives of the course
Multi-head Latent Attention
Concept and implementation
Advantages over traditional attention mechanisms
Mixture of Experts (MoE)
Role in the new architecture
Balancing performance with scalability
FP8 Training
Precision and computational advantages
Challenges and solutions in adopting FP8
Multi-Plane Network Topology
Design principles and structural insights
Impact on network efficiency and performance
Computational and architectural scaling
Energy efficiency considerations
Current hardware trends and influences on AI design
Case studies in deploying DeepSeek-V3
Critical assessment of DeepSeek-V3's impact
Future research directions and open questions
Subjects
Computer Science
