Title: Quiz 7 — QLoRA and Training on Consumer GPUs with Unsloth
Course: AIINFRA 201: Model Adaptation — Fine-Tuning & Quantization > Section: Week 07 — QLoRA and Training on Consumer GPUs with Unsloth

Q: In QLoRA, the frozen base model's weights are stored in 4-bit precision, and gradients are backpropagated through that frozen 4-bit model into trainable LoRA adapters.
- True
- False
A: True

Q: What was the headline hardware result demonstrated in the original QLoRA paper?
- Fine-tuning a 7B model on a laptop CPU
- Fine-tuning a 65B-parameter model on a single 48GB GPU, matching full 16-bit fine-tuning performance
- Training a model from scratch in 4-bit precision
- Eliminating the need for LoRA adapters entirely
A: Fine-tuning a 65B-parameter model on a single 48GB GPU, matching full 16-bit fine-tuning performance

Q: What does the "NF" in NF4 stand for, and why is it better suited to neural network weights than standard INT4?
Feedback: Sample/expected answer: NormalFloat. Neural network weights are approximately normally distributed, and NF4's quantization buckets are placed to match a normal distribution (denser near zero), making it information-theoretically optimal for this data — unlike INT4's uniformly spaced buckets, which waste precision where weights rarely occur.

Q: 4-bit quantization in QLoRA makes the actual forward and backward pass computations run faster, because the numbers being multiplied are smaller.
- True
- False
A: False

Q: Which of the following best describes what 4-bit quantization in QLoRA actually accomplishes?
- It speeds up matrix multiplication during training
- It mainly reduces the memory footprint of the frozen base model's weights, while forward/backward math and gradients still run at 16-bit
- It removes the need for an optimizer
- It converts activations to 4-bit for the entire training run
A: It mainly reduces the memory footprint of the frozen base model's weights, while forward/backward math and gradients still run at 16-bit

Q: According to 2026 practitioner consensus, which GPU is widely considered the budget "sweet spot" for QLoRA fine-tuning of 7B–13B models?
- RTX 3060 (12GB)
- RTX 4090 (24GB)
- A100 (80GB)
- Any 4GB laptop GPU
A: RTX 3060 (12GB)

Q: Name two concrete benefits Unsloth provides over a stock Hugging Face + PEFT QLoRA setup, according to this week's lecture.
Feedback: Sample/expected answer: Roughly 2.5x faster training, and 30%+ less VRAM usage (with no loss in final model quality); it achieves this by patching transformer math and using custom GPU kernels rather than changing the QLoRA algorithm itself.

Q: With Unsloth's optimizations, it is possible to train a small model like Qwen3-4B on as little as 3GB of VRAM.
- True
- False
A: True