Which statements about QLoRA are true?

**QLoRA** is a recipe for fine-tuning a very large model on a single GPU. It bolts four memory tricks together, and the question is testing whether you know which trick does what. The frozen base model is stored in 4-bit using a format called NF4. NF4 buckets are spaced to match how weights are distributed, clustered near zero, so it loses less than plain 4-bit integers. That is option A, and it is true. Double quantisation then compresses the tiny scaling constants that NF4 needs, squeezing out roughly another 0.4 bits per parameter. That is option B, also true. Paged optimisers spill optimiser state to CPU when the GPU is about to run out of memory, then bring it back. That is option D, true. The two traps are easy to fall for. The LoRA adapters are NOT 4-bit; they stay in bf16 so gradients are stable, so option C is false. And a 4-bit base does not remove gradient checkpointing, because that saves activation memory, which is a separate thing, so option E is false.

**QLoRA** combines four ingredients, and three of the five options name them correctly. The correct set is A, B, and D. Option A is right. The frozen base is stored in 4-bit NF4, whose buckets follow a Normal distribution because trained weights are roughly Gaussian. This beats uniform INT4 at the same bit width. Option B is right too. Double quantisation re-quantises the per-block scaling constants, saving about 0.37 bits per parameter, which matters at large scale. Option D is right. Paged optimisers use NVIDIA unified memory to move optimiser moments to CPU when an out-of-memory spike looms, then page them back. Option C is the classic trap. The LoRA adapters stay in bf16, not 4-bit, so gradients flow cleanly into the low-rank factors. Option E is also false. Gradient checkpointing saves activation memory, which is independent of weight bit-width. QLoRA runs still use it, exactly like full fine-tuning does.