AI0-001 Implementing AI Solutions Practice Question
A company wants to fine-tune a 70B-parameter LLM for a specialized domain but has limited GPU memory (e.g., 24 GB VRAM). Which technique allows fine-tuning with minimal memory footprint?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
QLoRA (Quantized Low-Rank Adaptation)
QLoRA (Quantized Low-Rank Adaptation) uses 4-bit quantization and low-rank adapters, reducing memory requirements dramatically while preserving fine-tuning quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Instruction tuning with a smaller dataset
Why it's wrong here
Dataset size does not solve the memory bottleneck; the model itself is too large to load.
- ✓
QLoRA (Quantized Low-Rank Adaptation)
Why this is correct
QLoRA quantizes the base model to 4-bit and uses LoRA adapters, fitting a 70B model in 24 GB VRAM.
- ✗
LoRA (Low-Rank Adaptation) on the base model
Why it's wrong here
LoRA reduces trainable parameters but still requires loading the full model in memory, which is too large for 24 GB.
- ✗
Full fine-tuning with gradient checkpointing
Why it's wrong here
Even with gradient checkpointing, a 70B model requires far more than 24 GB VRAM for full fine-tuning.
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