Language Model Costs

Synthesis of IBM's Generative AI Cost Factors and Operational Considerations

Functional Breakdown and User Actions

Use Case Definition

• Function: Determine the specific business problem or opportunity generative AI will address.

• User Action: Identify and document the desired outcomes and requirements for the AI application.

• Related Factors: Influences model selection, tuning, and deployment strategies.

Model Size

• Function: Select the appropriate model size based on the complexity of the task.

• User Action: Evaluate different models (e.g., 11B, 13B, 70B parameters) to match the use case requirements.

• Related Factors: Larger models require more computational resources, impacting pre-training, inferencing, and hosting costs.

Pre-training

• Function: Train the model from scratch using large datasets.

• User Action: Decide whether to invest in pre-training or leverage pre-trained models.

• Related Factors: High computational cost and time-intensive, typically only feasible for large enterprises.

Inferencing

• Function: Generate responses or predictions using the trained model.

• User Action: Optimize the model for efficient inferencing to reduce costs.

• Related Factors: Costs are influenced by the number of tokens processed, including both prompt and completion.

Tuning

• Function: Adjust the model's parameters to improve performance for specific tasks.

• User Action: Choose between fine-tuning and parameter-efficient fine-tuning methods.

• Related Factors: Tuning costs vary based on the method and data volume required.

Hosting

• Function: Deploy and maintain the model in a production environment.

• User Action: Select hosting options (cloud, on-premises, hybrid) based on regulatory and operational needs.

• Related Factors: Hosting costs depend on the infrastructure and the level of interaction with the model.

Deployment

• Function: Integrate the model into enterprise applications and workflows.

• User Action: Determine the deployment strategy (SaaS, on-premises) that aligns with business requirements.

• Related Factors: Deployment costs include infrastructure, scalability, and maintenance.

Matrix of Common Generative AI Use Cases and Evaluation Criteria

To provide a comprehensive evaluation, we use the following variables to cover the majority of costs and processes for running generative AI models:

1. Pre-training: Initial training of the model using large datasets.

2. Inferencing: Generating predictions or responses using the trained model.

3. Tuning: Fine-tuning the model to improve performance on specific tasks.

4. Hosting: Infrastructure and resources required to deploy and maintain the model.

5. Deployment: Integrating the model into production environments.

---