> ## Documentation Index
> Fetch the complete documentation index at: https://docs.acasia.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Deploy your first inference endpoint
> Pick a model, pick a cluster, and have a callable REST endpoint in about five minutes.
**In this guide:**
* Confirm your organization context and review available clusters.
* Pick a model from the Inference Models catalog and a target cluster with enough capacity.
* Deploy the endpoint, wait for Active status, then connect your application through the Acasia API.
## Before you begin
| Requirement | Description |
| ------------------------------- | ------------------------------------------------------------------------- |
| Acasia account access | You can log in to the Acasia Developer Portal. |
| Correct organization context | You are operating inside the intended organization. |
| Active cluster | Your organization has at least one active cluster available. |
| Available resources | The cluster has enough GPU, CPU, RAM, and storage for the selected model. |
| Model access | The model is available in the Inference Models catalog. |
| Hugging Face token, if required | Some models may require authenticated Hugging Face access. |
Inference endpoints, clusters, and API keys are scoped per organization. Use the organization switcher to confirm you're in the right one before deploying.
## Step 1 — Review your dashboard
| Dashboard area | What to check |
| -------------- | ----------------------------------------------------------- |
| Clusters | Confirm that at least one cluster is available. |
| Cluster status | Confirm the cluster is marked Active. |
| GPU model | Confirm the cluster has the GPU type needed for your model. |
| Live models | Check whether models are already running on the cluster. |
## Step 2 — Inspect cluster resources
| Resource | Why it matters |
| --------- | ---------------------------------------------------------------------------- |
| GPU count | Determines whether the model can run with the required accelerator capacity. |
| GPU model | Confirms hardware compatibility. |
| CPU cores | Supports runtime services, preprocessing, and orchestration. |
| RAM | Supports model loading and serving processes. |
| Storage | Stores model files, cache, logs, and temporary outputs. |
| Status | The cluster must be Active before deployment. |
## Step 3 — Open Inference Models
From the left navigation in the Developer Portal, select **Inference Models**.
| Page element | Purpose |
| -------------------- | ----------------------------------------------------- |
| Create New Inference | Starts the deployment workflow. |
| Hugging Face Token | Adds or manages Hugging Face access for gated models. |
| Active tab | Shows running inference deployments. |
| Terminated tab | Shows stopped or removed deployments. |
## Step 4 — Choose a model
Select **Create New Inference** and pick a model from the catalog.
| Consideration | Guidance |
| ------------------- | ----------------------------------------------------------------------------------------------- |
| Task | Select a model that matches your use case — text, image, video, audio, or multimodal inference. |
| Model size | Larger models usually require more GPU memory and compute. |
| Access requirements | Some models may require a Hugging Face token. |
If the model is gated, add a Hugging Face token from the Inference Models page before deployment. Without it, provisioning will fail when the runtime tries to pull model weights.
## Step 5 — Choose a cluster and deploy
Pick an Active cluster that has enough GPU, CPU, RAM, and storage for the selected model.
Confirm the model, cluster, and any optional parameters.
Acasia Cloud records the desired deployment state. The control plane then provisions the model on the target cluster.
## Step 6 — Verify the endpoint
Return to the **Active** tab on Inference Models and watch the endpoint move through its lifecycle states.
| State | Meaning |
| ------------ | --------------------------------------------- |
| Active | The endpoint is running and available. |
| Provisioning | The endpoint is being created or prepared. |
| Failed | The deployment did not complete successfully. |
| Terminated | The endpoint has been stopped or removed. |
## Step 7 — Call the endpoint
```bash theme={null}
curl --request POST \
--url https://endpoint.acasia.ai/v1/completions \
--header "Content-Type: application/json" \
--header "Authorization: Bearer $ACASIA_API_KEY" \
--data '{
"model": "your-model-id",
"prompt": "Summarize the safety risks in a warehouse aisle.",
"max_tokens": 300
}'
```
Endpoint paths, model identifiers, and payload formats vary by deployment. Always confirm against the endpoint detail view in the Developer Portal.
## Pre-deployment checklist
| Check | Confirmation |
| -------------------- | ------------------------------------------------------- |
| Organization context | You are in the correct organization. |
| Cluster status | The target cluster is active. |
| GPU capacity | The cluster has enough GPUs for the model. |
| Memory and storage | The cluster has enough RAM and storage. |
| Model selection | The model matches your use case. |
| Hugging Face access | Token is configured if the model requires gated access. |
## Summary
1. Confirm the correct organization context
2. Review available clusters
3. Open Inference Models → Create New Inference
4. Choose a model and an active cluster
5. Click Deploy Endpoint
6. Verify the endpoint appears under Active
7. Connect your application through the Acasia API
## Next steps
Generate and store a credential for production use.
Open a secure shell to your cluster.
Develop against a remote GPU cluster.