Exam NCP-AII Topic 1 Question 216 Discussion

Actual exam question for NVIDIA's NCP-AII exam
Question #: 216
Topic #: 1
A user reports that their deep learning training job is crashing with a 'CUDA out of memory' error, even though 'nvidia-smi' shows plenty of free memory on the GPU. The job uses TensorFlow. What are the TWO most likely causes?

Suggested Answer: C,D Vote an answer

'CUDA out of memory errors, despite seemingly available GPU memory, often indicate memory fragmentation or improper GPU assignment. TensorFlow can fragment GPU memory, leading to allocation failures even if sufficient total memory is available. The variable controls which GPUs TensorFlow can access. If it's not set or is set incorrectly, TensorFlow might be trying to allocate memory on a non-existent or unavailable GPU. While TensorFlow version incompatibilities can cause issues, they are less likely to directly manifest as 'CUDA out of memory' errors. TensorFlow typically prioritizes GPU memory allocation if configured correctly.

by Kama at Jan 05, 2026, 12:22 AM

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