diff --git a/models/toto-2.0/model.py b/models/toto-2.0/model.py
new file mode 100644
index 0000000..02fdd9b
--- /dev/null
+++ b/models/toto-2.0/model.py
@@ -0,0 +1,90 @@
+import logging
+import warnings
+
+import datasets
+import numpy as np
+
+import fev
+
+
+class Toto2Model(fev.ForecastingModel):
+    """Toto 2.0 model from https://github.com/DataDog/toto (https://pypi.org/project/toto-2)."""
+
+    model_name = "toto-2.0"
+    trained_on_datasets = []
+
+    def __init__(
+        self,
+        model_path: str = "Datadog/Toto-2.0-22m",
+        batch_size: int = 512,
+        context_length: int = 4096,
+        decode_block_size: int | None = None,
+        as_univariate: bool = False,
+        device: str = "auto",
+    ):
+        super().__init__()
+        self.model_path = model_path
+        self.batch_size = batch_size
+        self.context_length = context_length
+        self.decode_block_size = decode_block_size
+        self.as_univariate = as_univariate
+        self.device = device
+
+    def _fit_predict(self, task: fev.Task) -> list[datasets.DatasetDict]:
+        import torch
+        from toto2 import Toto2GluonTSModel, Toto2GluonTSModelConfig
+        from toto2 import Toto2Model as PretrainedToto2
+
+        if self.device == "auto":
+            self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        target_columns = ["target"] if self.as_univariate else task.target_columns
+
+        model = PretrainedToto2.from_pretrained(fev.utils.maybe_cache_from_s3(self.model_path))
+        config = Toto2GluonTSModelConfig(
+            prediction_length=task.horizon,
+            context_length=self.context_length,
+            target_dim=len(target_columns),
+            decode_block_size=self.decode_block_size,
+            quantiles=task.quantile_levels,
+        )
+        gts_model = Toto2GluonTSModel(model.to(self.device).eval(), config)
+        predictor = gts_model.create_predictor(batch_size=self.batch_size, device=self.device)
+
+        logging.getLogger("gluonts").setLevel(100)
+        # The 0.5 quantile is used as the point forecast (Toto 2.0 is quantile-based and has no mean prediction).
+        forecast_keys = {"predictions": 0.5, **{str(q): q for q in task.quantile_levels}}
+
+        predictions_per_window = []
+        for window in task.iter_windows():
+            _, prediction_dataset = fev.convert_input_data(window, adapter="gluonts", as_univariate=self.as_univariate)
+            with self._record_inference_time():
+                with warnings.catch_warnings():
+                    warnings.simplefilter("ignore", RuntimeWarning)
+                    forecasts = list(predictor.predict(prediction_dataset))
+            predictions_per_window.append(self._format_predictions(forecasts, task, forecast_keys))
+        return predictions_per_window
+
+    def _format_predictions(
+        self, forecasts: list, task: fev.Task, forecast_keys: dict[str, float]
+    ) -> datasets.DatasetDict:
+        """Format GluonTS forecasts into a `DatasetDict` keyed by target column, as expected by fev."""
+        if self.as_univariate:
+            # One univariate forecast per (item, variate), interleaved by variate; `f.quantile(q)` is (horizon,).
+            flat = datasets.Dataset.from_dict(
+                {key: np.stack([f.quantile(q) for f in forecasts]) for key, q in forecast_keys.items()}
+            )
+            return fev.utils.combine_univariate_predictions_to_multivariate(flat, target_columns=task.target_columns)
+        else:
+            # One forecast per item, reshaped to (num_items, horizon, n_variates). The model squeezes the
+            # variate axis for single-target tasks, so `f.quantile(q)` is (horizon,) there and (horizon, n_var) else.
+            quantiles = {
+                key: np.stack([f.quantile(q).reshape(task.horizon, -1) for f in forecasts])
+                for key, q in forecast_keys.items()
+            }
+            return datasets.DatasetDict(
+                {
+                    col: datasets.Dataset.from_dict({key: arr[..., i] for key, arr in quantiles.items()})
+                    for i, col in enumerate(task.target_columns)
+                }
+            )
diff --git a/models/toto-2.0/requirements.txt b/models/toto-2.0/requirements.txt
new file mode 100644
index 0000000..84cdc03
--- /dev/null
+++ b/models/toto-2.0/requirements.txt
@@ -0,0 +1 @@
+toto-2==2.0.0