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MLA-C01 Practice Question: A team is deploying a model that requires GPU…
A team is deploying a model that requires GPU acceleration for inference. They are using an Amazon SageMaker real-time endpoint. The model is a large language model (LLM) that does not fit on a single GPU. Which configuration should they use to minimize latency while fitting the model?
⚠ Common exam trap
It's easy for candidates to confuse data parallelism (which replicates the model) with model parallelism (which shards the model), assuming any distributed approach works for large models, but only model parallelism solves the 'does not fit on a single GPU' constraint.
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
✓
Use SageMaker's model parallelism library to shard the model across multiple GPUs in a single instance.
SageMaker's model parallelism library allows you to shard a large language model across multiple GPUs within a single instance, enabling inference for models that exceed a single GPU's memory. This approach minimizes latency by keeping all GPUs in a single instance with high-speed interconnects (e.g., NVLink), avoiding the network overhead of distributing across separate instances.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use data parallelism with Horovod to distribute inference across GPUs.
Why it's wrong here
Data parallelism is for training; inference requires model parallelism.
- ✓
Use SageMaker's model parallelism library to shard the model across multiple GPUs in a single instance.
Why this is correct
Hardware and software support for large model inference.
- ✗
Optimize the model with SageMaker Neo to reduce its size.
Why it's wrong here
Neo may not reduce size enough for LLMs; model parallelism is needed.
- ✗
Deploy the model across multiple endpoints and use a load balancer.
Why it's wrong here
Cannot split a single model across endpoints.
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