# What is Sparse Model? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Neural Architectures  
> **Canonical Citation:** [https://spidits.com/ai-glossary/sparse-model](https://spidits.com/ai-glossary/sparse-model)

## Definition
A Sparse Model is a neural network architecture that activates only a specific subset of its total parameters for any given token or input, utilizing routing mechanisms to achieve massive parameter scale without proportional compute costs.

## Architectural Mechanics & Deep Dive
A sparse model is a neural network architecture (such as Mixture of Experts) that activates only a specific subset of its parameters or layers for a given input token, rather than processing every weight. This sparsity allows models to scale to trillions of parameters while maintaining manageable compute budgets and low inference latency.

## Industry Applications & Real-World Use Cases
Mixture of Experts (MoE) LLMs, conditional computation layers, and cost-effective inference hosting.

## Frequently Asked Questions

### What is an example of a sparse model architecture?
A Mixture of Experts (MoE) model, where a gating router sends each token to only 2 out of 8 available expert layers.

### Why are sparse models highly efficient?
They allow models to store vast amounts of knowledge (high parameter count) while running inference at the speed and cost of a much smaller model.

## Related Terminology
- [dense-model](https://spidits.com/ai-glossary/dense-model)
- [mixture-of-experts](https://spidits.com/ai-glossary/mixture-of-experts)
- [mixture-of-depths](https://spidits.com/ai-glossary/mixture-of-depths)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*