💡Use case plays a critical role in determining the cost of generative AI for the Enterprise.
🔍Model size affects pricing, with larger models requiring more compute resources.
🏭Pre-training an llm from scratch is costly and time-consuming for Enterprises.
💬Inferencing involves generating responses using the llm and can impact cost.
⚙️Tuning the model's parameters can optimize performance but also adds to the cost.
What factors should Enterprises consider when evaluating generative AI?
—Enterprises should consider use case, model size, pre-training, inferencing, tuning, and hosting costs.
Why is model size important in the cost of generative AI?
—Larger models require more compute resources and can result in higher pricing tiers.
What is the cost of pre-training an llm from scratch?
—Pre-training llms from scratch is expensive and requires significant compute time and resources.
How does inferencing impact the cost of generative AI?
—Inferencing involves the cost of generating responses using the llm, which depends on the number of tokens.
What is the purpose of tuning in generative AI?
—Tuning the model's parameters can optimize its performance for specific use cases but adds to the cost.
00:00Introduction: Discusses the true cost of using generative AI for the Enterprise.
04:30Use Case Evaluation: Highlights the importance of evaluating use cases for generative AI.
09:30Model Size Analysis: Explains how model size affects pricing and resource requirements.
12:45Pre-training Considerations: Discusses the cost and effort involved in pre-training llms.
16:20Inferencing Cost: Explains the cost of generating responses using the llm.
20:10Tuning Impact: Discusses how tuning the model's parameters can optimize performance and add to the cost.
23:50Hosting Evaluation: Covers the circumstances that require hosting a generative AI model.
28:15Conclusion: Summarizes the key cost considerations of generative AI for the Enterprise.
