MLA-C01 ML Model Development Practice Question
A company is training a large Transformer model on SageMaker and wants to use model parallelism to fit the model into memory. The model has 10 billion parameters. Which instance type is MOST cost-effective for this task while supporting SageMaker's model parallelism?
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
✓
ml.p4d.24xlarge
The ml.p4d.24xlarge instances are optimized for large-scale distributed training with high memory and support SageMaker's model parallelism. ml.trn1 instances are designed for training with AWS Trainium, but they use a different chip architecture and may require specific SDKs. ml.g4dn instances are for inference and light training. ml.c5 instances are compute-optimized but lack GPU memory for large models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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ml.trn1.32xlarge
Why it's wrong here
Trn1 uses Trainium chips, which are cost-effective but may require additional setup for model parallelism.
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ml.c5.18xlarge
Why it's wrong here
C5 instances are CPU-only, not suitable for GPU-accelerated training.
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ml.g4dn.12xlarge
Why it's wrong here
G4dn instances are suitable for inference and light training, not for large model parallelism.
- ✓
ml.p4d.24xlarge
Why this is correct
P4d instances have high GPU memory and support model parallelism for large models.
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