A guide to GPU server architecture, NVMe storage, and network optimization for AI/ML model training and inference.
Cloud Infrastructure for AI/ML
AI and machine learning workloads present different requirements from traditional cloud infrastructures. In this article, we explore the design principles of cloud infrastructure optimized for AI/ML projects.
GPU Server Architecture
- NVIDIA A100/H100: Best GPU options for large language models (LLM) and deep learning training
- Multi-GPU Configuration: High bandwidth between GPUs with NVLink and NVSwitch
- CPU-GPU Balance: Intel Xeon Platinum for adequate data feeding to GPUs
- Memory Capacity: DDR5 ECC memory for processing large datasets
Storage Strategy
Training Data: High throughput and low latency with NVMe SSDs. Parallel file systems (BeeGFS, Lustre) for large datasets.
Model Artifacts: Model versioning and sharing with S3-compatible object storage.
Checkpoints & Logs: High write performance storage for training checkpoints.
Network Optimization
- RDMA/RoCE: Low-latency network communication for distributed training
- 100GbE+: High bandwidth between GPU servers
- Spine-Leaf Topology: Non-blocking network architecture
CloudRUX AI Infrastructure
CloudRUX is working on a GPU service for AI/ML projects. When it launches, GPU models, pricing and data center location will be announced on our product page. You can already plan your architecture with our engineers.
Conclusion
Proper infrastructure design for AI/ML workloads is critical for both performance and cost. Accelerate your projects with CloudRUX's AI-ready infrastructure.