# Inference setup

*Estimated read time: 2 minutes*

## Inference setup—pick your scenario

This page focuses on two `ModelSpec` parameters:

* **`zoo_url`**: where the model is stored (cloud vs. local)
* **`inference_host_address`**: where the model runs (`"@cloud"` vs. `"@local"`)

Pick the setup that matches where you want inference to run and where your models live.

{% hint style="info" %}
AI Server setups—like custom endpoints and multi-model hosting—are covered in the Advanced topics section.
{% endhint %}

### Cloud inference

**Cloud zoo** → **cloud runtime**

**Use when**: You want zero local setup—everything runs in the cloud.

{% code overflow="wrap" %}

```python
from degirum_tools import ModelSpec

spec = ModelSpec(
    model_name="yolov8n_coco--640x640_quant_axelera_metis_1",
    zoo_url="degirum/axelera",  # cloud model zoo
    inference_host_address="@local",  # inference executes on your machine
    model_properties={"device_type": ["AXELERA/METIS"]},
)
model = spec.load_model()
```

{% endcode %}

### Local inference with cloud zoo

**Cloud zoo** → **local runtime**

**Use when**: You have local hardware but prefer to fetch models from the cloud.

{% code overflow="wrap" %}

```python
from degirum_tools import ModelSpec

spec = ModelSpec(
    model_name="yolov8n_coco--640x640_quant_axelera_metis_1",
    zoo_url="degirum/axelera",  # fetch artifacts from cloud
    inference_host_address="@local",  # inference executes on your machine
    model_properties={"device_type": ["AXELERA/METIS"]},
)
model = spec.load_model()
```

{% endcode %}

### Local inference with local zoo

Local zoo → local runtime

**Use when**: You want offline operation and predictable model behavior. Use `ModelSpec.ensure_local()` to download and store the model locally, then switch the `ModelSpec` to local before loading.

{% code overflow="wrap" %}

```python
from degirum_tools import ModelSpec

spec = ModelSpec(
    model_name="yolov8n_coco--640x640_quant_axelera_metis_1",
    zoo_url="degirum/axelera",  # start from cloud reference
    inference_host_address="@local",  # inference executes locally
    model_properties={"device_type": ["AXELERA/METIS"]},
)

# One-time (while online): download/verify artifacts and update spec to local zoo.
spec.ensure_local()

# After this, loading uses the local zoo (offline-friendly).
model = spec.load_model()
```

{% endcode %}

{% hint style="info" %}

* Once loaded, model objects are callable: `model(x)` ≡ `model.predict(x)`
* Public model zoos generally don't require a token; private ones do.
* For advanced setups (e.g., using AI Server or hosting your own model zoo), see Advanced topics.
  {% endhint %}


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