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Multi-Tenant Fine-Tuning Service

A fine-tuning service turns a tenant’s dataset into an evaluated model or adapter. Its pipeline must protect data while scheduling training and serving the result efficiently.

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RELATED CONCEPTS

Multi-LoRA Serving Multi-Tenancy, Quotas and Fair Share Model Memory Footprint GPU-Hours and Time to Train Evaluation and Data Pipeline Infrastructure Spot, Preemption and Capacity Strategies

PRACTICE THIS IN REAL QUESTIONS

Kubernetes, Slurm & GPU SchedulingDesign the scheduling and isolation for a multi-tenant fine-tuning service: hundreds of customers, a few base models, shared GPUs.→Open-Weights Models & Serving EnginesFifty teams have fine-tuned the same base model. How do you serve all of them?→LLM Inference & ServingHow would you serve hundreds of LoRA adapters on one base model, and what does it cost in throughput?→Napkin Math, Cost & CapacityHow much memory does it take to fine-tune a 70B model, full fine-tuning versus LoRA?→Coding for InfraImplement a LoRA forward pass and merge two adapters without inventing cross terms→Hardware, Cabling & Cluster Build-OutDesign the physical layer for a multi-tenant GPU cloud. What changes versus a single-tenant cluster?→