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

Together AI · 2 months ago

Multi-tenant GPU clusters allow AI companies to share compute infrastructure across teams while maintaining isolation through dedicated nodes, storage, and self-serve scheduling. The architecture requires three elements: pooled capacity at the infrastructure layer, per-tenant isolation with dedicated resources, and quota-based allocation with hard limits enforced at the scheduler level. Together AI's implementation demonstrates that this approach eliminates idle capacity waste while preventing cross-team resource conflicts and maintaining billing visibility per tenant.