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This guide walks you through deploying your first AI model on Simplismart. We’ll use Gemma 4 31B Instruct as an example, which is a latest open-weight large language model from Google.

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Prerequisites

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Deployment Process

1

Select a Model

  1. Navigate to the Marketplace section in the left sidebar
  2. Search for Gemma 4 31B in the search bar
  3. Click on the Gemma 4 31B Instruct model card to view its details Model Marketplace search results
  4. On the model detail page, click Deploy Model to begin the deployment process Gemma 4 31B Instruct model card
2

Configure Deployment Settings

In the Create Deployment screen, fill in the following:

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Deployment Details

  • Deployment Name: A unique name (e.g. gemma-4-31b)
  • Permalink Identifier: A custom identifier used to create your model endpoint URL. This value will be added as a prefix to your endpoint URL.
  • Model: Confirm “Gemma 4 31B Instruct” is selected
  • Cloud: Select Simplismart Cloud for managed hosting. Choose Bring Your Own Cloud to deploy on your own cluster.
  • Accelerator Type: Select H100 (or another available GPU based on your quota)

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Scaling Parameters

Set Minimum Pods to 1 and Maximum Pods to 1. The GPU scaling capacity is shown below the inputs so you can verify resource availability before deploying.
For production workloads, see Scaling Parameters and Autoscaling Policy for stabilization windows and rate-limited scaling.
Create Deployment form
3

Add Tags

Tags help filter and organize deployments. Add at least one:
  • Key: env
  • Value: quickstart
4

Confirm and Deploy

Click Add Deployment in the top-right corner. A Deploy Model Confirmation modal appears summarising your configuration:Review the details, then click Confirm to start the deployment.Deploy Model Confirmation modal
5

Monitor Deployment Progress

After confirming, you are taken to the deployment detail page. The Health Check panel on the right shows Progressing while pods come up and the model loads. Once the model is ready, the status changes to Healthy.The Details tab shows the Deployment ID, processing type (SYNC), model endpoint, and scaling configuration. Use the Logs and Events tabs to follow startup activity.Deployment progressing
For the full deployment state machine and monitoring guidance, see Monitoring and Access.
6

Test Your Deployment

Once the deployment status shows Healthy, open the API tab in the deployment detail page to test it:
  1. The cURL snippet is pre-filled with your endpoint URL.
  2. Set the Authorization header to Bearer <your API key>.
  3. Copy and run the snippet. API tab with cURL snippet
Replace YOUR-ENDPOINT-HERE and YOUR_API_KEY with your actual values before running the command.
7

Clean Up Resources

To free up GPU resources and avoid unnecessary charges, delete the deployment when you are done.
  1. Open the Deployments tab.
  2. Select your deployment.
  3. Click the kebab menu (⋮) in the top-right of the deployment card and select Delete. See Managing Your Deployment for the full action set (Pause, Edit, Clone, Delete).

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Understanding Your Deployment

Your deployed model exposes an OpenAI-compatible endpoint, so you can use it with any client library that supports the OpenAI API format. The endpoint supports:
  • Text inputs and completions
  • Streaming and non-streaming responses
  • Standard OpenAI API parameters like temperature and max_tokens

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Monitoring and Management

After deployment, you can:

Monitor Performance

Track usage deployment health, request metrics, latency metrics, LLM metrics, and Pod resouce details in the Monitor tab

Adjust Resources

Scale your deployment up or down based on actual usage patterns

View Logs

Access detailed logs and events to troubleshoot any issues

Manage API Keys

Create and revoke API keys for secure access

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Next Steps

Now that you’ve successfully deployed your first model, consider these next steps:

Optimize Performance

Learn techniques to improve latency, throughput, and cost-efficiency using Simplismart Copilot.