diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index a179e59f..e218b506 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -19,7 +19,6 @@ jobs:
       matrix:
         os: [ubuntu-18.04]
         floatx: [float32, float64]
-        test-subset: pymc-experimental/tests
       fail-fast: false
     runs-on: ${{ matrix.os }}
     env:
@@ -81,7 +80,6 @@ jobs:
       matrix:
         os: [windows-latest]
         floatx: [float32, float64]
-        test-subset: pymc-experimental/tests
       fail-fast: false
     runs-on: ${{ matrix.os }}
     env:
diff --git a/conda-envs/environment-test-py38.yml b/conda-envs/environment-test-py38.yml
index 4567e0b8..0ecbe95d 100644
--- a/conda-envs/environment-test-py38.yml
+++ b/conda-envs/environment-test-py38.yml
@@ -11,4 +11,3 @@ dependencies:
 - xhistogram
 - pip:
   - "git+https://github.com/pymc-devs/pymc.git@main"
-  - blackjax
diff --git a/conda-envs/environment-test-py39.yml b/conda-envs/environment-test-py39.yml
index 19a8adc4..570af385 100644
--- a/conda-envs/environment-test-py39.yml
+++ b/conda-envs/environment-test-py39.yml
@@ -11,4 +11,3 @@ dependencies:
 - xhistogram
 - pip:
   - "git+https://github.com/pymc-devs/pymc.git@main"
-  - blackjax
diff --git a/pymc_experimental/__init__.py b/pymc_experimental/__init__.py
index 882ccda5..8946a3a1 100644
--- a/pymc_experimental/__init__.py
+++ b/pymc_experimental/__init__.py
@@ -11,6 +11,5 @@
         _log.addHandler(handler)
 
 
-from pymc_experimental import distributions, gp, inference, utils
+from pymc_experimental import distributions, gp, utils
 from pymc_experimental.bart import *
-from pymc_experimental.inference.fit import fit
diff --git a/pymc_experimental/bart/__init__.py b/pymc_experimental/bart/__init__.py
index 33127b7d..4b9379fe 100644
--- a/pymc_experimental/bart/__init__.py
+++ b/pymc_experimental/bart/__init__.py
@@ -1,4 +1,4 @@
-#   Copyright 2022 The PyMC Developers
+#   Copyright 2020 The PyMC Developers
 #
 #   Licensed under the Apache License, Version 2.0 (the "License");
 #   you may not use this file except in compliance with the License.
diff --git a/pymc_experimental/inference/__init__.py b/pymc_experimental/inference/__init__.py
deleted file mode 100644
index 2339ccd4..00000000
--- a/pymc_experimental/inference/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from pymc_experimental.inference.fit import fit
diff --git a/pymc_experimental/inference/fit.py b/pymc_experimental/inference/fit.py
deleted file mode 100644
index 6b9835c0..00000000
--- a/pymc_experimental/inference/fit.py
+++ /dev/null
@@ -1,37 +0,0 @@
-#   Copyright 2022 The PyMC Developers
-#
-#   Licensed under the Apache License, Version 2.0 (the "License");
-#   you may not use this file except in compliance with the License.
-#   You may obtain a copy of the License at
-#
-#       http://www.apache.org/licenses/LICENSE-2.0
-#
-#   Unless required by applicable law or agreed to in writing, software
-#   distributed under the License is distributed on an "AS IS" BASIS,
-#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#   See the License for the specific language governing permissions and
-#   limitations under the License.
-
-
-def fit(method, **kwargs):
-    """
-    Fit a model with an inference algorithm
-
-    Parameters
-    ----------
-    method : str
-        Which inference method to run.
-        Supported: pathfinder
-
-    kwargs are passed on.
-
-    Returns
-    -------
-    arviz.InferenceData
-    """
-    if method == "pathfinder":
-        try:
-            from pymc_experimental.inference.pathfinder import fit_pathfinder
-        except ImportError as exc:
-            raise RuntimeError("Need BlackJAX to use `pathfinder`") from exc
-        return fit_pathfinder(**kwargs)
diff --git a/pymc_experimental/inference/pathfinder.py b/pymc_experimental/inference/pathfinder.py
deleted file mode 100644
index df2c616a..00000000
--- a/pymc_experimental/inference/pathfinder.py
+++ /dev/null
@@ -1,149 +0,0 @@
-#   Copyright 2022 The PyMC Developers
-#
-#   Licensed under the Apache License, Version 2.0 (the "License");
-#   you may not use this file except in compliance with the License.
-#   You may obtain a copy of the License at
-#
-#       http://www.apache.org/licenses/LICENSE-2.0
-#
-#   Unless required by applicable law or agreed to in writing, software
-#   distributed under the License is distributed on an "AS IS" BASIS,
-#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#   See the License for the specific language governing permissions and
-#   limitations under the License.
-
-
-import collections
-import sys
-from typing import Optional
-
-import arviz as az
-import blackjax
-import jax
-import jax.numpy as jnp
-import jax.random as random
-import numpy as np
-import pymc as pm
-from pymc import modelcontext
-from pymc.sampling import RandomSeed, _get_seeds_per_chain
-from pymc.sampling_jax import get_jaxified_graph
-from pymc.util import get_default_varnames
-
-
-def convert_flat_trace_to_idata(
-    samples,
-    dims=None,
-    coords=None,
-    include_transformed=False,
-    postprocessing_backend="cpu",
-    model=None,
-):
-
-    model = modelcontext(model)
-    init_position_dict = model.initial_point()
-    trace = collections.defaultdict(list)
-    astart = pm.blocking.DictToArrayBijection.map(init_position_dict)
-    for sample in samples:
-        raveld_vars = pm.blocking.RaveledVars(sample, astart.point_map_info)
-        point = pm.blocking.DictToArrayBijection.rmap(raveld_vars, init_position_dict)
-        for p, v in point.items():
-            trace[p].append(v.tolist())
-
-    trace = {k: np.asarray(v)[None, ...] for k, v in trace.items()}
-
-    var_names = model.unobserved_value_vars
-    vars_to_sample = list(get_default_varnames(var_names, include_transformed=include_transformed))
-    print("Transforming variables...", file=sys.stdout)
-    jax_fn = get_jaxified_graph(inputs=model.value_vars, outputs=vars_to_sample)
-    result = jax.vmap(jax.vmap(jax_fn))(
-        *jax.device_put(list(trace.values()), jax.devices(postprocessing_backend)[0])
-    )
-
-    trace = {v.name: r for v, r in zip(vars_to_sample, result)}
-    idata = az.from_dict(trace, dims=dims, coords=coords)
-
-    return idata
-
-
-def fit_pathfinder(
-    iterations=5_000,
-    random_seed: Optional[RandomSeed] = None,
-    postprocessing_backend="cpu",
-    ftol=1e-4,
-    model=None,
-):
-    """
-    Fit the pathfinder algorithm as implemented in blackjax
-
-    Requires the JAX backend
-
-    Parameters
-    ----------
-    iterations : int
-        Number of iterations to run.
-    random_seed : int
-        Random seed to set.
-    postprocessing_backend : str
-        Where to compute transformations of the trace.
-        "cpu" or "gpu".
-    ftol : float
-        Floating point tolerance
-
-    Returns
-    -------
-    arviz.InferenceData
-
-    Reference
-    ---------
-    https://arxiv.org/abs/2108.03782
-    """
-
-    (random_seed,) = _get_seeds_per_chain(random_seed, 1)
-
-    model = modelcontext(model)
-
-    rvs = [rv.name for rv in model.value_vars]
-    init_position_dict = model.initial_point()
-    init_position = [init_position_dict[rv] for rv in rvs]
-
-    new_logprob, new_input = pm.aesaraf.join_nonshared_inputs(
-        init_position_dict, (model.logp(),), model.value_vars, ()
-    )
-
-    logprob_fn_list = get_jaxified_graph([new_input], new_logprob)
-
-    def logprob_fn(x):
-        return logprob_fn_list(x)[0]
-
-    dim = sum(v.size for v in init_position_dict.values())
-
-    rng_key = random.PRNGKey(random_seed)
-    w0 = random.multivariate_normal(rng_key, 2.0 + jnp.zeros(dim), jnp.eye(dim))
-    path = blackjax.vi.pathfinder.init(rng_key, logprob_fn, w0, return_path=True, ftol=ftol)
-
-    pathfinder = blackjax.kernels.pathfinder(rng_key, logprob_fn, ftol=ftol)
-    state = pathfinder.init(w0)
-
-    def inference_loop(rng_key, kernel, initial_state, num_samples):
-        @jax.jit
-        def one_step(state, rng_key):
-            state, info = kernel(rng_key, state)
-            return state, (state, info)
-
-        keys = jax.random.split(rng_key, num_samples)
-        return jax.lax.scan(one_step, initial_state, keys)
-
-    _, rng_key = random.split(rng_key)
-    print("Running pathfinder...", file=sys.stdout)
-    _, (_, samples) = inference_loop(rng_key, pathfinder.step, state, iterations)
-
-    dims = {
-        var_name: [dim for dim in dims if dim is not None]
-        for var_name, dims in model.RV_dims.items()
-    }
-
-    idata = convert_flat_trace_to_idata(
-        samples, postprocessing_backend=postprocessing_backend, coords=model.coords, dims=dims
-    )
-
-    return idata
diff --git a/pymc_experimental/tests/test_pathfinder.py b/pymc_experimental/tests/test_pathfinder.py
deleted file mode 100644
index 6d92e0dd..00000000
--- a/pymc_experimental/tests/test_pathfinder.py
+++ /dev/null
@@ -1,48 +0,0 @@
-#   Copyright 2022 The PyMC Developers
-#
-#   Licensed under the Apache License, Version 2.0 (the "License");
-#   you may not use this file except in compliance with the License.
-#   You may obtain a copy of the License at
-#
-#       http://www.apache.org/licenses/LICENSE-2.0
-#
-#   Unless required by applicable law or agreed to in writing, software
-#   distributed under the License is distributed on an "AS IS" BASIS,
-#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#   See the License for the specific language governing permissions and
-#   limitations under the License.
-
-import sys
-
-import numpy as np
-import pymc as pm
-import pytest
-
-import pymc_experimental as pmx
-
-
-@pytest.mark.skipif(sys.platform == "win32", reason="JAX not supported on windows.")
-def test_pathfinder():
-    # Data of the Eight Schools Model
-    J = 8
-    y = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])
-    sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])
-
-    with pm.Model() as model:
-
-        mu = pm.Normal("mu", mu=0.0, sigma=10.0)
-        tau = pm.HalfCauchy("tau", 5.0)
-
-        theta = pm.Normal("theta", mu=0, sigma=1, shape=J)
-        theta_1 = mu + tau * theta
-        obs = pm.Normal("obs", mu=theta, sigma=sigma, shape=J, observed=y)
-
-        idata = pmx.fit(method="pathfinder", iterations=100)
-
-        assert idata is not None
-        assert "theta" in idata.posterior._variables.keys()
-        assert "tau" in idata.posterior._variables.keys()
-        assert "mu" in idata.posterior._variables.keys()
-        assert idata.posterior["mu"].shape == (1, 100)
-        assert idata.posterior["tau"].shape == (1, 100)
-        assert idata.posterior["theta"].shape == (1, 100, 8)
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 4c58c77a..6c049906 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,2 +1 @@
 dask[all]
-blackjax