Models
Parameter count
Parameter count is the number of learned weights in a model.
Quick definition
Parameter count is the number of learned weights in a model.
- Category: Models
- Focus: model capability and fit
- Used in: Choosing a model that fits latency and cost constraints.
What it means
More parameters often increase capacity but require more compute. In models workflows, parameter count often shapes model capability and fit.
How it works
Model architecture and scale determine capability. Context length, parameter count, and modality support vary across models.
Why it matters
Model architecture affects capability, context length, and speed.
Common use cases
- Choosing a model that fits latency and cost constraints.
- Selecting longer context for document-heavy workflows.
- Using specialized models for code, vision, or speech.
Example
A 7B model has seven billion parameters.
Pitfalls and tips
Bigger is not always better. Match the model to the task and evaluate in production.
In BoltAI
In BoltAI, this shows up in model selection and configuration.
