AWS Certified AI Practitioner AIF-C01 Practice Question
A startup plans to run a text-generation foundation model on small Amazon EC2 instances with limited memory. When comparing models available in Amazon Bedrock, which model attribute should they examine first to estimate whether the model will fit within the instance's compute and memory limits?
The total number of parameters (model size) directly influences how much GPU or CPU memory and compute the model requires during inference. A model with fewer parameters has a smaller memory footprint and lower computational demand, making it more suitable for resource-constrained instances. Temperature, top-K sampling, and supported stop-sequence syntax affect response style or control but have little impact on the base hardware resources needed to load the model.
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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