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Core
Multi-LoRA Serving
LoRA adapters are tiny weight deltas layered on a shared base model, so you can serve hundreds of fine-tuned variants from one set of base weights rather than one full model per tenant. The serving challenge is batching requests that use different adapters in the same forward pass, moving adapters in and out of GPU memory on demand, and reusing the base model's KV cache machinery. AI, ML, and GenAI engineer 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
PRACTICE THIS IN REAL QUESTIONS
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 FundamentalsYou must choose between LoRA and full fine-tuning for a domain assistant. How do you decide?→LLM & GenAI FundamentalsHow do you merge or compose multiple LoRA adapters, and when does it break?→LLM & GenAI FundamentalsExplain LoRA, QLoRA, and parameter-efficient fine-tuning. Why train a fraction of the parameters?→
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