> ## Documentation Index
> Fetch the complete documentation index at: https://docs.eigenai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Deployments

> Deploy a model to a dedicated inference endpoint and call it via an OpenAI-compatible API.

The Deployments page lets you provision a dedicated GPU endpoint for any supported model. Once a deployment is **Ready**, you can call it using the OpenAI-compatible API.

## Prerequisites

* An EigenAI account with available credits.
* An API key (see [Authentication](/platform/authentication)).

## The deployments list

The Deployments page shows a table of all your deployments.

| Column          | Description                                                                                                       |
| --------------- | ----------------------------------------------------------------------------------------------------------------- |
| Deployment name | Human-readable name and short ID (e.g., `dep-6a6ee693c4`).                                                        |
| Type            | Deployment type. Currently `llm` for all text and vision-language models.                                         |
| Status          | Current state (see [Deployment statuses](#deployment-statuses) below).                                            |
| Actions         | **Sync status** refreshes the status from the backend. **Terminate** shuts down the deployment and stops billing. |

Use the **Search** box to filter by name, model, or ID. Use the **All types** and **All statuses** dropdowns to narrow the list.

Click **Refresh** to reload all deployment statuses at once.

### Deployment statuses

| Status       | Description                                              |
| ------------ | -------------------------------------------------------- |
| Provisioning | The deployment is starting up. GPUs are being allocated. |
| Ready        | The deployment is running and accepting API requests.    |
| Terminated   | The deployment has been shut down.                       |
| Failed       | The deployment encountered an error and could not start. |

## Create a deployment

Click **Create Deployment** to open the 3-step wizard.

### Step 1 — Select model

Choose the model you want to deploy. 35 models are available, organized by provider.

| Provider       | Models                                                                                                                                                                                                                                                                                                                                                 |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| DeepSeek       | DeepSeek R1, DeepSeek R1 (0528), DeepSeek R1 (0528, FP4), DeepSeek V3.1 Terminus, DeepSeek V3.2                                                                                                                                                                                                                                                        |
| Qwen3 (Text)   | Qwen3 4B Thinking 2507 (FP8), Qwen3 4B Instruct 2507 (FP8), Qwen3 30B Thinking 2507 (FP8), Qwen3 30B Instruct 2507 (FP8), Qwen3 Next 80B Thinking (FP8), Qwen3 235B Thinking 2507 (FP8), Qwen3 235B Instruct 2507 (FP8), Qwen3 Coder 480B Instruct (FP8)                                                                                               |
| Qwen3 (Vision) | Qwen3-VL 2B Instruct (FP8), Qwen3-VL 2B Thinking (FP8), Qwen3-VL 4B Instruct (FP8), Qwen3-VL 4B Thinking (FP8), Qwen3-VL 8B Instruct (FP8), Qwen3-VL 8B Thinking (FP8), Qwen3-VL 30B Instruct (FP8), Qwen3-VL 30B Thinking (FP8), Qwen3-VL 32B Instruct (FP8), Qwen3-VL 32B Thinking (FP8), Qwen3-VL 235B Instruct (FP8), Qwen3-VL 235B Thinking (FP8) |
| GLM            | GLM 4.5V, GLM 5 (FP8)                                                                                                                                                                                                                                                                                                                                  |
| Llama          | Llama 3.1 70B Instruct, Llama 4 Scout 17B 16E Instruct                                                                                                                                                                                                                                                                                                 |
| Kimi           | Kimi K2 Instruct, Kimi K2 Thinking                                                                                                                                                                                                                                                                                                                     |
| Others         | GPT-OSS 120B, Intern S1, MiMo V2 Flash, Nemotron 3 Nano 30B (BF16)                                                                                                                                                                                                                                                                                     |

Each model card shows:

* **GPU minimum** — the minimum number of GPUs required.
* **Capability tags** — e.g., `Think` (chain-of-thought reasoning), `Tools` (function calling), `Turbo` (speculative decoding), `Vision` (image input), `FP8` (FP8 quantized weights), `BF16` (BF16 quantized weights).

Use the provider filter tabs (**All**, **DeepSeek**, **Qwen3 (Text)**, **Qwen3 (Vision)**, **GLM**, **Llama**, **Kimi**, **Others**) to browse by family.

You can also deploy a fine-tuned model checkpoint directly from the [Fine-tuning](/platform/fine-tuning) job detail page using the **Deploy** button on any epoch checkpoint.

### Step 2 — Configuration

Configure the hardware and runtime options for your deployment.

| Field             | Description                                                                                                |
| ----------------- | ---------------------------------------------------------------------------------------------------------- |
| Display name      | A human-readable name for this deployment. Auto-generated by default (e.g., `deepseek-r1-chief-moth-207`). |
| Hardware platform | GPU type. Currently **NVIDIA H200** (141 GB, \$5.99/GPU/hr).                                               |
| GPU count         | Number of GPUs per replica: 1, 2, 4, or 8. Some models require a minimum GPU count.                        |
| Replicas          | Number of parallel instances for scaling: 1, 2, 3, or 4.                                                   |
| Reasoning parser  | Enable structured chain-of-thought output. Recommended for reasoning models (e.g., DeepSeek R1).           |
| Tool call parser  | Enable function calling and tool use.                                                                      |

### Step 3 — Review & deploy

Review the deployment summary before launching.

| Field            | Description                                                    |
| ---------------- | -------------------------------------------------------------- |
| Name             | Display name of the deployment.                                |
| Model            | The model being deployed.                                      |
| Hardware         | GPU type selected.                                             |
| GPUs per replica | GPU count chosen in step 2.                                    |
| Replicas         | Number of replicas chosen in step 2.                           |
| Reasoning parser | Whether reasoning output parsing is enabled.                   |
| Tool calling     | Whether tool call parsing is enabled.                          |
| Estimated cost   | Calculated as: GPUs per replica × replicas × price per GPU/hr. |

Click **Deploy** to provision the endpoint. The deployment will enter **Provisioning** status.

## View deployment details

Click any row in the deployments list to expand its details inline.

| Field       | Description                                      |
| ----------- | ------------------------------------------------ |
| Model name  | The unique model identifier to use in API calls. |
| Base model  | The base model or checkpoint used.               |
| Accelerator | GPU type and count (e.g., `H200:1`).             |
| Replicas    | Number of running replicas.                      |
| Checkpoint  | The specific model checkpoint deployed.          |

## Call a deployment

When a deployment is **Ready**, an **OpenAI-Compatible Endpoint** section appears with everything you need to make your first request.

**Base URL**

```
https://api-web.eigenai.com/api/deployment/v1
```

**Model name**

Each deployment gets a unique model name (e.g., `qwen3-vl-8b-instruct-mushy-booby-167-dep-6a6ee693c4`). Use this as the `model` field in your requests. Click the copy icon to copy it.

**Authentication**

Include your API key in the `Authorization` header. Click **Get API Key** to go to the API Keys page.

<CodeGroup>
  ```bash cURL theme={null}
  curl https://api-web.eigenai.com/api/deployment/v1/chat/completions \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "YOUR_DEPLOYMENT_MODEL_NAME",
      "messages": [{"role": "user", "content": "Hello"}]
    }'
  ```

  ```python Python theme={null}
  from openai import OpenAI

  client = OpenAI(
      api_key="YOUR_API_KEY",
      base_url="https://api-web.eigenai.com/api/deployment/v1"
  )

  response = client.chat.completions.create(
      model="YOUR_DEPLOYMENT_MODEL_NAME",
      messages=[{"role": "user", "content": "Hello"}]
  )
  print(response.choices[0].message.content)
  ```
</CodeGroup>

<Info>
  Replace `YOUR_DEPLOYMENT_MODEL_NAME` with the model name shown in the deployment's detail panel.
</Info>

## Terminate a deployment

Click **Terminate** in the Actions column to shut down a deployment. Billing stops once the status changes to **Terminated**. This action cannot be undone.