Models
Sparse MoE
Sparse MoE activates only a subset of experts per token.
Quick definition
Sparse MoE activates only a subset of experts per token.
- Category: Models
- Focus: model capability and fit
- Used in: Choosing a model that fits latency and cost constraints.
What it means
It reduces computation compared to dense models. In models workflows, sparse moe 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
Use 2 experts out of 16 for each token.
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.
