AI at Full Throttle
白皮书
This white paper discusses the challenges and solutions related to high-speed Ethernet networks in the context of rapidly growing AI applications. It highlights the critical need for advanced networking technologies to support the increasing demands of AI workloads.
- Surge in AI Demand: The rapid adoption of AI applications is driving an unprecedented demand for high-speed networks, pushing current infrastructure to its limits.
- Performance Impact of AI Workloads: AI workloads, particularly deep learning and machine learning, require high throughput and low latency for efficient data handling, directly impacting performance.
- Data Center Traffic Growth: McKinsey reports that 70% of the growing demand for data center capacity is attributed to advanced AI workloads, with an expected rise of 33% per year from 2023 to 2030.
- Bandwidth Requirements: A survey indicates that data center interconnect bandwidth demand will increase sixfold over the next five years due to AI workloads, necessitating terabit-scale networks.
- Technical Challenges: Key challenges include latency, bandwidth limitations, and network congestion, which can severely degrade AI performance and increase costs.
- Innovative Networking Solutions: Advanced Ethernet technologies, such as 800GE and co-packaged optics (CPO), are emerging as vital solutions for overcoming these challenges.
- Testing and Validation Needs: As new technologies like 800GE and 1.6T Ethernet are introduced, comprehensive testing methods are essential to ensure reliability and performance in AI data centers.
Conclusion on Future Infrastructure: The paper emphasizes the importance of proactive testing and advanced technologies in building next-generation AI-optimized networks to meet escalating demands.