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Multi-LoRA Serving

A LoRA adapter is a few hundred megabytes of low-rank matrices that turn a base model into a fine-tuned variant, and multi-LoRA serving runs hundreds of them on one copy of the base weights by keeping the adapters in memory and applying the right one per request inside the batch. It is how a platform serves a thousand customers' fine-tunes without a thousand deployments. The costs are an extra small matmul per layer, adapter memory and loading, and a scheduler that has to batch across adapters without starving any of them.

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RELATED CONCEPTS

Continuous Batching PagedAttention Model Memory Footprint Multi-Tenant Fine-Tuning Service Quantization for Inference Serving Engines: vLLM, SGLang and TensorRT-LLM

PRACTICE THIS IN REAL QUESTIONS

LLM Inference & ServingHow would you serve hundreds of LoRA adapters on one base model, and what does it cost in throughput?→Open-Weights Models & Serving EnginesFifty teams have fine-tuned the same base model. How do you serve all of them?→AI Infrastructure System DesignDesign serving for 100 fine-tuned variants of one 70B base. Multi-LoRA on shared replicas or a replica per variant?→Napkin Math, Cost & CapacityHow much memory does it take to fine-tune a 70B model, full fine-tuning versus LoRA?→Kubernetes, Slurm & GPU SchedulingDesign the scheduling and isolation for a multi-tenant fine-tuning service: hundreds of customers, a few base models, shared GPUs.→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?→