Exploring GPU Networking Infrastructure Optimization for AI Cloud Platforms: A Climate-Conscious Approach

Join Yinko, Product Manager at Cruo, as he explores the infrastructure and networking challenges of AI computing, highlighting innovative solutions like PXN to optimize GPU communication and accelerate model training.

1. Yinko, product manager at Cruso, will discuss the infrastructure for supporting the newest GPUs and machine learning models on the AI platform.
2. Cruso is an AI Cloud platform with a mission to align the future of computing with the future of climate.
3. The platform aims to utilize trended, wasted energy sources and renewables to power data centers, reducing negative impacts on the climate.
4. Cruso's cloud-building approach focuses on three pillars: high performance, ease of use, and climate alignment.
5. High performance is crucial for customers as it directly affects their bottom line by optimizing infrastructure and minimizing delays or outages.
6. Ease of use is a priority for Cruso, which aims to provide a simpler user interface for AI engineers, hiding underlying infrastructure complexity.
7. Climate alignment means powering data centers with 100% renewable, stranded energy sources, ensuring net-zero emissions.
8. Cruso currently has several data centers in the US and is building one in Iceland using geothermal energy.
9. The platform offers three main products: compute (VMs with GPUs), CPU instances for general-purpose tasks, and storage solutions like FML and persistent block storage.
10. Cruso networking includes traditional VPC networking, firewalls, and load balancers, as well as optimized infiniband cluster networking for GPU communication.
11. The target user base consists of AI developers and machine learning engineers who need infrastructure but prefer not to focus on its complexities.
12. Cruso partners with other AI companies such as Together AI and Boson AI, allowing customers of those companies to use Cruso's infrastructure for various tasks.
13. Distributed training has specific challenges, particularly regarding networking and communication between GPUs during the training process.
14. Computation and communication overlap helps reduce network usage but does not eliminate it completely.
15. Cruso is working on optimizing its data fabric to provide better bandwidth and latency for GPU communication.
16. The traditional fat tree approach has limitations, such as introducing single choke points and fold domains when connecting servers to a single leaf.
17. Nvidia's recent PXN feature allows the use of internal EN switch communication across rails, reducing latency for cross-host GPU communication.
18. Using the internal NV switch for intra-server GPU communication can help minimize network usage.
19. Testing with sparse mixture of experts models showed a 50% improvement in small message latency and bandwidth for large messages using PXN.
20. The real value comes from faster model training times, which can significantly reduce costs associated with model training.
21. Cruso's optimized infiniband cluster networking leverages NV switch technology to enable more efficient GPU-to-GPU communication.
22. By using internal EN switches for cross-host GPU traffic, latency and bandwidth usage are improved without requiring additional hops.
23. Cruso's climate alignment goal involves powering data centers with renewable energy sources and reducing carbon emissions to net zero.
24. The platform aims to provide a simpler user experience for AI engineers while ensuring high performance and climate alignment, allowing users to focus on their work without worrying about infrastr

Source: AI Engineer via YouTube

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