AWS Certified Machine Learning – SpecialtyMachine Learning Implementation and OperationsHard
A data science team is building a large language model (LLM) for a natural language processing application. The model is too large to fit into the memory of a single GPU instance or to process efficiently on a single instance during inference. The team needs to deploy this LLM on Amazon SageMaker, optimizing for memory utilization and inference latency across multiple instances. Which SageMaker feature is specifically designed to handle the deployment of very large models by distributing them across multiple devices or instances?
- ASageMaker Neo
- BSageMaker Model Parallelism (Inference)
- CSageMaker Elastic Inference
- DSageMaker Multi-Model Endpoints
Show answer & explanationAnswer & explanation
Correct answer: B. SageMaker Model Parallelism (Inference)
SageMaker Model Parallelism (Inference) is specifically designed to deploy very large models, like LLMs, by automatically splitting the model across multiple GPU instances or devices to overcome memory limitations and improve inference latency.
Why the other options are wrong
- A. SageMaker Neo optimizes models for specific hardware but does not inherently provide model parallelism for distributing large models across multiple instances for inference.
- C. SageMaker Elastic Inference attaches fractional GPU acceleration to CPU instances, primarily for cost-effective acceleration on smaller models, not for splitting very large models across multiple instances.
- D. SageMaker Multi-Model Endpoints host multiple independent models on a single endpoint, reducing operational overhead, but they do not split a single large model across instances.
SageMaker Model Parallelism (Inference)
A SageMaker capability that enables the deployment of very large deep learning models by distributing the model's layers or components across multiple GPU instances or devices for efficient inference.
- Distributes large models across multiple GPUs/instances.
- Overcomes single-device memory limitations.
- Optimizes inference latency and throughput for LLMs.
Memory trick: Parallelism breaks big models into small, fast pieces.