NCA-GENL Software Development Practice Question
A developer is using the NVIDIA NeMo framework to fine-tune a large language model. They want to reduce GPU memory usage during training without significantly sacrificing model quality. Which technique should they apply?
⚠ Common exam trap
The trap here is thinking that increasing batch size or disabling mixed precision would help memory, when they actually increase it.
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
✓
Enable gradient checkpointing
Gradient checkpointing reduces memory by storing only a subset of activations and recomputing the rest during backpropagation. This allows training larger models or using bigger batches on the same GPU. Other options either increase memory usage or do not affect it, making gradient checkpointing the correct choice for memory-constrained fine-tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the micro batch size
Why it's wrong here
Increasing micro batch size raises memory usage because more samples are processed in parallel. This is the opposite of what the developer wants. While it can improve throughput, it does not reduce memory footprint and may cause out-of-memory errors on limited hardware.
- ✗
Disable mixed precision training
Why it's wrong here
Disabling mixed precision (i.e., using full FP32) increases memory usage because parameters and activations take twice the space compared to FP16/BF16. This would worsen the memory situation. Mixed precision is a memory-saving technique, so turning it off is counterproductive.
- ✓
Enable gradient checkpointing
Why this is correct
Gradient checkpointing trades compute for memory by not storing all intermediate activations during the forward pass, recomputing them during backward. This significantly reduces memory usage, allowing larger models or batch sizes, with only a modest increase in training time and negligible impact on model quality.
- ✗
Use a higher learning rate
Why it's wrong here
Learning rate affects convergence speed and stability, not memory consumption. A higher learning rate might speed up training but could also cause divergence. It does not address the memory constraint and is irrelevant to the goal of reducing GPU memory usage during fine-tuning.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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