#10 Gen AI Interview 2026: LoRA vs QLoRA (Asked FAANG)

Fine-tuning a 70 billion parameter model requires over 140GB of VRAM - hardware that most engineers simply cannot access. Yet this is one of the most asked fine-tuning questions in AI Engineer and GenAI interviews at FAANG, MNCs, and top Indian startups in 2026. In this video, we break down LoRA vs QLoRA in a structured interview Q&A format so you can answer this with full confidence and depth. We cover why full fine-tuning is too expensive for most use cases, how LoRA reduces memory by training only small low-rank matrices while keeping original weights frozen, how QLoRA goes one step furthe…