# Using Dashboard UI

To get started follow the steps below:

1. #### Go to [Create Instance](https://www.qblocks.cloud/client/v2/create-instance) Page:

   <img src="https://2367841498-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-M9O-9uez85Lp9ysQP3O%2Fuploads%2FkcakgYDrQWPtDa1ItBHM%2FScreenshot%202023-03-31%20at%209.15.46%20PM.png?alt=media&#x26;token=771822c4-a0ae-4c7a-ae4a-50b444fbd2b9" alt="" width="100%">
2. #### Select a GPU Type:
   1. Different GPUs carry different number of processing cores and memory.
   2. Based on your ML model’s size and number of parameters, you can choose a specific GPU type
3. **Select required data storage:**
   1. Q Blocks instances offer fast IO in GPU instances with NVMe/SSD storage access for best performance
4. **Choose an AI Framework Image:**
   1. Images help you pre-configure your instance with AI frameworks such as TensorFlow, PyTorch or Keras
   2. They also come with out-of-the-box support for CUDA and GPU drivers.
   3. If you don't a specific framework driven environment then choose base Ubuntu Image.
   4. All these environments offer superuser (sudo su) access with support for installation and management of python pip packages, conda environments etc. You may consider it as a linux based GPU instance which can be modified as per your software needs.

Once the configuration is selected, click on “Create Instance”.

> ✅ The GPU powered computing instance should be accessible in 1 - 5 minutes.

### Checkout this video for quick walkthrough:

{% embed url="<https://youtu.be/9k3A4oLK3uM>" %}

We hope to see you make something wonderful with Q Blocks GPU instances.

#### Technical support:

For any query or issues in getting started, reach us out at <support@qblocks.cloud> for quick resolution.


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