diff --git a/docs/api/models/foundation/models.md b/docs/api/models/foundation/models.md
index 65dcfa6..b8b34f8 100644
--- a/docs/api/models/foundation/models.md
+++ b/docs/api/models/foundation/models.md
@@ -26,6 +26,11 @@
         members:
             - Sundial
 
+::: timecopilot.models.foundation.t0
+    options:
+        members:
+            - T0
+
 ::: timecopilot.models.foundation.tabpfn
     options:
         members:
diff --git a/docs/model-hub.md b/docs/model-hub.md
index 44047bb..6115386 100644
--- a/docs/model-hub.md
+++ b/docs/model-hub.md
@@ -44,6 +44,7 @@ TimeCopilot provides a unified interface to state-of-the-art foundation models f
 - [Moirai](api/models/foundation/models.md#timecopilot.models.foundation.moirai) ([arXiv:2402.02592](https://arxiv.org/abs/2402.02592))
 - [PatchTST-FM](api/models/foundation/models.md#timecopilot.models.foundation.patchtst_fm) ([arXiv:2602.06909](https://arxiv.org/abs/2602.06909))
 - [Sundial](api/models/foundation/models.md#timecopilot.models.foundation.sundial) ([arXiv:2502.00816](https://arxiv.org/pdf/2502.00816))
+- [T0](api/models/foundation/models.md#timecopilot.models.foundation.t0) ([model card](https://huggingface.co/theforecastingcompany/t0-alpha))
 - [TabPFN](api/models/foundation/models.md#timecopilot.models.foundation.tabpfn) ([arXiv:2501.02945](https://arxiv.org/abs/2501.02945))
 - [TiRex](api/models/foundation/models.md#timecopilot.models.foundation.tirex) ([arXiv:2505.23719](https://arxiv.org/abs/2505.23719))
 - [TimeGPT](api/models/foundation/models.md#timecopilot.models.foundation.timegpt) ([arXiv:2310.03589](https://arxiv.org/abs/2310.03589))
diff --git a/pyproject.toml b/pyproject.toml
index b9682d2..06f6263 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -92,6 +92,7 @@ dependencies = [
   "statsforecast>=2.0.2",
   "tabpfn-time-series==1.0.3 ; python_full_version < '3.13'",
   "tensorboard>=2.20.0",
+  "tfc-t0>=0.1.2 ; python_full_version >= '3.11' and python_full_version < '3.14'",
   "timecopilot-chronos-forecasting>=0.2.1",
   "timecopilot-granite-tsfm>=0.1.2",
   "timecopilot-timesfm>=0.2.1",
diff --git a/tests/models/conftest.py b/tests/models/conftest.py
index a3838ac..1c32679 100644
--- a/tests/models/conftest.py
+++ b/tests/models/conftest.py
@@ -145,6 +145,11 @@ def disable_mps_session(monkeypatch):
 
     models.append(TiRex())
 
+if (3, 11) <= sys.version_info < (3, 14):
+    from timecopilot.models.foundation.t0 import T0
+
+    models.append(T0(context_length=256, batch_size=2))
+
 if sys.version_info < (3, 13):
     from tabpfn_time_series import TabPFNMode
 
diff --git a/tests/models/test_models.py b/tests/models/test_models.py
index 8fa6827..06b1718 100644
--- a/tests/models/test_models.py
+++ b/tests/models/test_models.py
@@ -29,6 +29,16 @@ def test_tirex_import_fails():
     assert "requires Python >= 3.11" in str(excinfo.value)
 
 
+@pytest.mark.skipif(
+    (3, 11) <= sys.version_info < (3, 14),
+    reason="T0 requires Python >= 3.11 and < 3.14",
+)
+def test_t0_import_fails():
+    with pytest.raises(ImportError) as excinfo:
+        from timecopilot.models.foundation.t0 import T0  # noqa: F401
+    assert "requires Python >= 3.11 and < 3.14" in str(excinfo.value)
+
+
 @pytest.mark.skipif(
     sys.version_info < (3, 13),
     reason="Sundial requires Python < 3.13",
diff --git a/timecopilot/models/foundation/t0.py b/timecopilot/models/foundation/t0.py
new file mode 100644
index 0000000..aec8749
--- /dev/null
+++ b/timecopilot/models/foundation/t0.py
@@ -0,0 +1,195 @@
+import sys
+from contextlib import contextmanager
+
+if sys.version_info < (3, 11) or sys.version_info >= (3, 14):
+    raise ImportError("T0 requires Python >= 3.11 and < 3.14")
+
+import numpy as np
+import pandas as pd
+import torch
+from t0 import T0Forecaster
+from tqdm import tqdm
+
+from ..utils.forecaster import Forecaster, QuantileConverter
+from .utils import TimeSeriesDataset
+
+
+class T0(Forecaster):
+    """
+    T0 is an open-weights time series foundation model from
+    [The Forecasting Company](https://theforecastingcompany.com/). It is a
+    decoder-style patch transformer that alternates time and covariate
+    attention layers, producing probabilistic multi-horizon quantile
+    forecasts. It decodes up to 1,024 timesteps in a single forward pass and
+    falls back on autoregressive rollout for longer horizons. See the
+    [model card](https://huggingface.co/theforecastingcompany/t0-alpha)
+    for more details.
+    """
+
+    def __init__(
+        self,
+        repo_id: str = "theforecastingcompany/t0-alpha",
+        context_length: int = 4096,
+        batch_size: int = 16,
+        alias: str = "T0",
+    ):
+        # ruff: noqa: E501
+        """
+        Args:
+            repo_id (str, optional): The Hugging Face Hub model ID or local path to
+                load the T0 model from. Defaults to "theforecastingcompany/t0-alpha".
+                See the full list of models at
+                [Hugging Face](https://huggingface.co/theforecastingcompany).
+            context_length (int, optional): Maximum context length (input window
+                size) for the model. Series longer than this are truncated to the
+                most recent `context_length` observations. Defaults to 4096.
+            batch_size (int, optional): Batch size to use for inference. Defaults
+                to 16. Adjust based on available memory.
+            alias (str, optional): Name to use for the model in output DataFrames
+                and logs. Defaults to "T0".
+
+        Notes:
+            **Requirements:**
+
+            - T0 requires Python 3.11 to 3.13 (via the
+              [`tfc-t0`](https://pypi.org/project/tfc-t0/) package).
+
+            **Available models:**
+
+            | Model ID                                                                                          | Parameters |
+            | ------------------------------------------------------------------------------------------------- | ---------- |
+            | [`theforecastingcompany/t0-alpha`](https://huggingface.co/theforecastingcompany/t0-alpha)         | ~102M      |
+
+            **Resources:**
+
+            - HuggingFace: [theforecastingcompany/t0-alpha](https://huggingface.co/theforecastingcompany/t0-alpha)
+            - Platform: [Retrocast](https://app.retrocast.com/)
+
+            **Technical Details:**
+
+            - The model is loaded onto the best available device (GPU if
+              available, otherwise CPU).
+            - T0 predicts 5 quantile knots (0.1, 0.25, 0.5, 0.75, 0.9); the
+              median (0.5) is used as the point forecast and other requested
+              quantiles are obtained by linear interpolation across the knots.
+            - NaN values in the context are treated as missing observations.
+            - T0 natively supports past and known-future covariates through its
+              `predict` API; this integration currently exposes the univariate
+              path only.
+        """
+        self.repo_id = repo_id
+        self.context_length = context_length
+        self.batch_size = batch_size
+        self.alias = alias
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    @contextmanager
+    def _get_model(self) -> T0Forecaster:
+        model = T0Forecaster.from_pretrained(self.repo_id).to(self.device).eval()
+        try:
+            yield model
+        finally:
+            del model
+            torch.cuda.empty_cache()
+
+    def _to_context(self, batch: list[torch.Tensor]) -> torch.Tensor:
+        """Left-pad a ragged batch with NaN (treated as missing by T0)."""
+        max_len = min(
+            max(len(ts) for ts in batch),
+            self.context_length,
+        )
+        context = torch.full(
+            (len(batch), max_len),
+            float("nan"),
+            dtype=torch.float32,
+        )
+        for idx, ts in enumerate(batch):
+            ts = ts[-max_len:]
+            context[idx, -len(ts) :] = ts.to(dtype=torch.float32)
+        return context
+
+    def forecast(
+        self,
+        df: pd.DataFrame,
+        h: int,
+        freq: str | None = None,
+        level: list[int | float] | None = None,
+        quantiles: list[float] | None = None,
+    ) -> pd.DataFrame:
+        """Generate forecasts for time series data using the model.
+
+        This method produces point forecasts and, optionally, prediction
+        intervals or quantile forecasts. The input DataFrame can contain one
+        or multiple time series in stacked (long) format.
+
+        Args:
+            df (pd.DataFrame):
+                DataFrame containing the time series to forecast. It must
+                include as columns:
+
+                    - "unique_id": an ID column to distinguish multiple series.
+                    - "ds": a time column indicating timestamps or periods.
+                    - "y": a target column with the observed values.
+
+            h (int):
+                Forecast horizon specifying how many future steps to predict.
+            freq (str, optional):
+                Frequency of the time series (e.g. "D" for daily, "M" for
+                monthly). See [Pandas frequency aliases](https://pandas.pydata.org/
+                pandas-docs/stable/user_guide/timeseries.html#offset-aliases) for
+                valid values. If not provided, the frequency will be inferred
+                from the data.
+            level (list[int | float], optional):
+                Confidence levels for prediction intervals, expressed as
+                percentages (e.g. [80, 95]). If provided, the returned
+                DataFrame will include lower and upper interval columns for
+                each specified level.
+            quantiles (list[float], optional):
+                List of quantiles to forecast, expressed as floats between 0
+                and 1. Should not be used simultaneously with `level`. When
+                provided, the output DataFrame will contain additional columns
+                named in the format "model-q-{percentile}", where {percentile}
+                = 100 × quantile value. Quantiles the model wasn't trained on
+                are linearly interpolated across its fixed knots.
+
+        Returns:
+            pd.DataFrame:
+                DataFrame containing forecast results. Includes:
+
+                    - point forecasts for each timestamp and series.
+                    - prediction intervals if `level` is specified.
+                    - quantile forecasts if `quantiles` is specified.
+
+                For multi-series data, the output retains the same unique
+                identifiers as the input DataFrame.
+        """
+        freq = self._maybe_infer_freq(df, freq)
+        qc = QuantileConverter(level=level, quantiles=quantiles)
+        dataset = TimeSeriesDataset.from_df(df, batch_size=self.batch_size)
+        fcst_df = dataset.make_future_dataframe(h=h, freq=freq)
+        # T0 interpolates arbitrary quantile levels from its trained knots,
+        # so the median and any user-requested quantiles come from one pass.
+        pred_quantiles = sorted(set(qc.quantiles or []) | {0.5})
+        median_idx = pred_quantiles.index(0.5)
+        fcsts: list[np.ndarray] = []
+        with self._get_model() as model:
+            for batch in tqdm(dataset):
+                out = model.predict(
+                    self._to_context(batch),
+                    horizon=h,
+                    quantiles=pred_quantiles,
+                )
+                # shape: (batch, h, n_quantiles)
+                fcsts.append(out.quantiles.cpu().numpy())
+        fcsts_np = np.concatenate(fcsts, axis=0)
+        fcst_df[self.alias] = fcsts_np[..., median_idx].reshape(-1, 1)
+        if qc.quantiles is not None:
+            for q in qc.quantiles:
+                fcst_df[f"{self.alias}-q-{int(q * 100)}"] = fcsts_np[
+                    ..., pred_quantiles.index(q)
+                ].reshape(-1, 1)
+            fcst_df = qc.maybe_convert_quantiles_to_level(
+                fcst_df,
+                models=[self.alias],
+            )
+        return fcst_df