# How to Set Up Dedicated Compute and Deploy Your Models - YouTube

[How to Set Up Dedicated Compute and Deploy Your Models](https://www.youtube.com/watch?v=WZkcEQ1D7x0)

> _**Note:** Although the video above showcases an older version of our UI, it still accurately illustrates how to deploy models on our platform._

Deployments bring your models to life by running them on dedicated compute, ready to serve predictions at scale with configurable replicas, autoscaling, and cost controls.

A deployment defines how a model runs on a selected nodepool, acting as the bridge between your model and the underlying infrastructure.

For advanced use cases, a single model can be deployed across [multiple nodepools](https://docs.clarifai.com/compute/deployments/deploy-model#step-4-set-dynamic-routing-optional) to optimize for different workloads, availability, or performance requirements.

With deployments you can:

- Serve models with low-latency, production-grade inference
- Auto-scale replicas based on traffic demand
- Monitor health, usage, and cost in real time
- Run on dedicated infrastructure with full resource isolation

[**📄️Create Clusters and Nodepools** \ 
Set up capabilities that match your computational needs](https://docs.clarifai.com/compute/deployments/clusters-nodepools)  
[**📄️Deploy a Model** \ 
Deploy a model into your created cluster and nodepool](https://docs.clarifai.com/compute/deployments/deploy-model)  
[**📄️Manage Your Compute** \ 
Edit and delete deployments, clusters, and nodepools](https://docs.clarifai.com/compute/deployments/manage-compute)

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