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Multi-Tenant Fine-Tuning Service
A fine-tuning service takes a customer's dataset and a base model and returns a model, and the design problem is that many customers want this at once, cheaply, without seeing each other's data, on GPUs that must not sit idle between jobs. LoRA changes the shape: an adapter is a few hundred megabytes rather than a copy of the base, so many jobs can share a base in memory and many adapters can be served from one replica. This page designs the service end to end: the pipeline, the LoRA arithmetic that sets memory and cost, the isolation, the scheduler that packs jobs, and the serving path.
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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?→Hardware, Cabling & Cluster Build-OutDesign the physical layer for a multi-tenant GPU cloud. What changes versus a single-tenant cluster?→Distributed Training & ParallelismFine-tune a 70B on one 80 GB card. What do NF4 and double quantization actually buy, and where does DoRA change the arithmetic?→