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What is the main difference between open source and paid models in the generative AI field?
What is the main difference between open source and paid models in the generative AI field?
- Paid models are dependent on cloud services while open source models are not
- Paid models offer better accuracy compared to open source models
- Open source models are more scalable than paid models
- Open source models have limited capabilities compared to paid models (correct)
Which statement accurately reflects the deployment requirements of open source and paid LLM models?
Which statement accurately reflects the deployment requirements of open source and paid LLM models?
- Paid LLM models have limited deployment options compared to open source models
- Open source LLM models require cloud dependency for deployment, while paid models do not (correct)
- Paid LLM models can only be deployed on specific clouds like AWS
- Both open source and paid LLM models require cloud dependency for deployment
What distinguishes AWS Bedrock from some paid LLM models?
What distinguishes AWS Bedrock from some paid LLM models?
- AWS Bedrock is a free model, while paid models require payment
- AWS Bedrock offers limited capabilities compared to some paid models (correct)
- AWS Bedrock has better scalability than paid models
- AWS Bedrock can only be used by developers experienced in NLP
What is a key advantage of integrating paid LLM models with specific clouds like AWS?
What is a key advantage of integrating paid LLM models with specific clouds like AWS?
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Why are open source and paid models crucial considerations for developers in generative AI engineering?
Why are open source and paid models crucial considerations for developers in generative AI engineering?
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