Capacity without conflict: A guide to multi-tenant GPU cluster design for AI-native teams

AI Impact Summary

AI-native companies struggle with GPU sprawl due to the inherent need for multiple teams to train and experiment with models, leading to significant idle capacity and wasted resources. This design outlines a multi-tenant GPU cluster approach that pools capacity across teams while maintaining strong isolation through dedicated nodes, storage, and billing visibility. Together AI’s implementation demonstrates a practical approach to this challenge, offering a shared infrastructure layer with tenant-specific environments, enabling teams to operate with predictable economics and avoid the chaos of traditional shared clusters.