> ## Documentation Index > Fetch the complete documentation index at: https://docs.gcore.com/llms.txt > Use this file to discover all available pages before exploring further. # Deploy a custom model If the [Application Catalog](/edge-ai/everywhere-inference/application-catalog) does not include the model you need, you can deploy any Docker container image from a public or private registry. The container image must meet Everywhere Inference requirements — [prepare it for deployment](/edge-ai/everywhere-inference/ai-models/prepare-a-custom-ai-model-for-deployment) before proceeding. [Request a quota increase](/edge-ai/everywhere-inference/quotas/request-quota-increase) if the account quota is insufficient for the selected flavor. ## Deploy the model In the [Gcore Customer Portal](https://portal.gcore.com/), navigate to **Everywhere Inference** > **Deployments** and click **Deploy custom inference** in the top-right corner. The **Deploy custom model** form opens. Deployments page showing Deploy application from catalog and Deploy custom inference buttons ### Step 1. Configure the model image Under **Model image**, configure the container image source. **Public registry**: Select **Public**, then enter the **Model image URL (docker tag)** and the **Container port** where the model listens for requests. Model image section with Public registry type selected **Private registry**: Select **Private**, select the registry from the **Registry** dropdown, then enter the **Model image URL (docker tag)** and the **Container port**. Model image section with Private registry type selected, showing Registry dropdown If no private registry is configured yet, click **+ Add registry** to [add one](/edge-ai/everywhere-inference/container-image-registries/add-a-registry). (Optional) Enable the **Set startup command** toggle to specify a command that runs when the container starts. ### Step 2. Select pod configuration Under **Pod configuration**, select the compute resources for the deployment: * **Flavor type** — select **CPU-optimized** or **GPU-optimized**. * **Flavor** — select the hardware configuration from the dropdown. The following flavor parameters are recommended based on model size: | **Billion parameters** | **Recommended flavor** | | ---------------------- | ---------------------- | | \< 21 | 1 x L40S 48 GB | | 21–41 | 2 x L40S 48 GB | | > 41 | 4 x L40S 48 GB | ### Step 3. Set up routing placement Under **Routing placement**, select up to six regions where the model will run. Routing placement and Autoscaling limits sections ### Step 4. Configure autoscaling Under **Autoscaling limits**, configure pod scaling: * **All selected regions** — applies the same autoscaling settings to all selected regions. * **Custom** — applies different settings per region. * **Minimum pods** — the minimum number of pods to maintain during low-traffic periods. * **Maximum pods** — the maximum number of pods that can be provisioned during peak traffic. * **Cooldown period** — the time (in seconds) the autoscaler waits after a scaling event before making another adjustment. * **Pod lifetime** — the time (in seconds) before an idle pod is deleted after its last request. A pod with a lifetime of zero seconds will take approximately one minute to scale down. Under **Autoscaling triggers**, define the conditions that trigger pod provisioning. By default, CPU utilization and GPU utilization triggers are included with an 80% threshold. Click **Add trigger** to add more triggers. Autoscaling triggers and Health checks sections ### Step 5 (optional). Configure health checks Under **Health checks**, enable probes to monitor container availability: * **Liveness probe** — restarts the container if it becomes unresponsive. * **Readiness probe** — removes the container from load balancing until it is ready to serve traffic. * **Startup probe** — delays other probes until the container finishes starting up. ### Step 6. Set deployment details Under **Deployment details**, enter a deployment name. Use only letters and numbers — hyphens are not allowed in deployment names. An optional description can also be added. Deployment details and Additional options sections ### Step 7 (optional). Set additional options Under **Additional options**: * Enable **Set environment variables** to pass key-value pairs to the container at runtime. * Enable **Enable API Key authentication** to restrict access using [API keys](/edge-ai/everywhere-inference/api-keys/create-inference-deployment-with-auth). Multiple API keys can be associated with a single deployment, and the same API key can be attached to multiple deployments. ### Step 8. Finalize the deployment Review the estimated cost in the right panel, then click **Deploy model**. Gcore creates the deployment and opens the **Deployments** page, where the deployment status is visible. ## Deployment status The new deployment appears on the **Deployments** page with a **Deploying** status. Once all pods are running, the status changes to **Active**. The endpoint URL becomes available on the deployment detail page. Use it to send inference requests as described in [Query a deployed model](/edge-ai/everywhere-inference/ai-models/query-deployed-model). Logs, compute settings, and other deployment options are available on the [deployment detail page](/edge-ai/everywhere-inference/ai-models/manage-ai-model-deployments).