diff --git a/RELEASE-NOTES.md b/RELEASE-NOTES.md
index 4cbb2a6c613..8f415b0932d 100644
--- a/RELEASE-NOTES.md
+++ b/RELEASE-NOTES.md
@@ -16,6 +16,7 @@ It also brings some dreadfully awaited fixes, so be sure to go through the chang
 - Removed `theanof.set_theano_config` because it illegally changed Theano's internal state (see [#4329](https://github.com/pymc-devs/pymc3/pull/4329)).
 
 ### New Features
+- Option to `disable_bounds_check=True` when instantiating `pymc3.Model()` for faster sampling for models that cannot violate boundary constraints (which are most of them; see [#4377](https://github.com/pymc-devs/pymc3/pull/4377)).
 - `OrderedProbit` distribution added (see [#4232](https://github.com/pymc-devs/pymc3/pull/4232)).
 - `plot_posterior_predictive_glm` now works with `arviz.InferenceData` as well (see [#4234](https://github.com/pymc-devs/pymc3/pull/4234))
 
diff --git a/pymc3/distributions/dist_math.py b/pymc3/distributions/dist_math.py
index e3020007356..dc67f89652a 100644
--- a/pymc3/distributions/dist_math.py
+++ b/pymc3/distributions/dist_math.py
@@ -33,6 +33,7 @@
 
 from pymc3.distributions.shape_utils import to_tuple
 from pymc3.distributions.special import gammaln
+from pymc3.model import modelcontext
 from pymc3.theanof import floatX
 
 f = floatX
@@ -67,6 +68,15 @@ def bound(logp, *conditions, **kwargs):
     -------
     logp with elements set to -inf where any condition is False
     """
+
+    # If called inside a model context, see if bounds check is disabled
+    try:
+        model = modelcontext(kwargs.get("model"))
+        if model.disable_bounds_check:
+            return logp
+    except TypeError:  # No model found
+        pass
+
     broadcast_conditions = kwargs.get("broadcast_conditions", True)
 
     if broadcast_conditions:
diff --git a/pymc3/model.py b/pymc3/model.py
index 14d72442780..6a6e159b6ca 100644
--- a/pymc3/model.py
+++ b/pymc3/model.py
@@ -809,6 +809,11 @@ class Model(Factor, WithMemoization, metaclass=ContextMeta):
         temporarily in the model context. See the documentation
         of theano for a complete list. Set config key
         ``compute_test_value`` to `raise` if it is None.
+    disable_bounds_check: bool
+        Disable checks that ensure that input parameters to distributions
+        are in a valid range. If your model is built in a way where you
+        know your parameters can only take on valid values you can disable
+        this for increased speed.
 
     Examples
     --------
@@ -895,11 +900,14 @@ def __new__(cls, *args, **kwargs):
         instance._theano_config = theano_config
         return instance
 
-    def __init__(self, name="", model=None, theano_config=None, coords=None):
+    def __init__(
+        self, name="", model=None, theano_config=None, coords=None, disable_bounds_check=False
+    ):
         self.name = name
         self.coords = {}
         self.RV_dims = {}
         self.add_coords(coords)
+        self.disable_bounds_check = disable_bounds_check
 
         if self.parent is not None:
             self.named_vars = treedict(parent=self.parent.named_vars)
diff --git a/pymc3/tests/test_dist_math.py b/pymc3/tests/test_dist_math.py
index 9aa9dbf16c8..359cc8d656c 100644
--- a/pymc3/tests/test_dist_math.py
+++ b/pymc3/tests/test_dist_math.py
@@ -60,6 +60,13 @@ def test_bound():
     assert np.prod(bound(logp, cond).eval()) == -np.inf
 
 
+def test_bound_disabled():
+    with pm.Model(disable_bounds_check=True):
+        logp = tt.ones(3)
+        cond = np.array([1, 0, 1])
+        assert np.all(bound(logp, cond).eval() == logp.eval())
+
+
 def test_alltrue_scalar():
     assert alltrue_scalar([]).eval()
     assert alltrue_scalar([True]).eval()