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Multi-LoRA Serving
LoRA adapters are tiny weight deltas on top of a shared base model, so you can serve hundreds of fine-tuned variants from one set of base weights instead of one full model per tenant. The serving challenge is batching requests that use different adapters in the same forward pass, swapping adapters in and out of GPU memory on demand, and sharing the base model's KV cache machinery. Applied-AI interviews probe it because it is the economics behind per-tenant and per-task customization and the serving-side complement to LoRA training.
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RELATED CONCEPTS
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System Design for AI in ProductionDesign an LLM inference platform (vLLM-as-a-service) serving many models and teams.→LLM & GenAI FundamentalsCompare LoRA, prefix tuning, prompt tuning, and adapters. How do PEFT methods differ?→ML Infrastructure & GPUsYou have more models than GPUs. How do you share GPUs across many models and teams?→LLM & GenAI FundamentalsExplain LoRA, QLoRA, and parameter-efficient fine-tuning. Why train a fraction of the parameters?→ML Infrastructure & GPUsHow do you serve many fine-tuned model variants efficiently (multi-LoRA serving)?→LLM & GenAI FundamentalsWhat is QLoRA, and how does it make fine-tuning large models feasible on one GPU?→
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