diff --git a/doc/source/whatsnew/v1.5.3.rst b/doc/source/whatsnew/v1.5.3.rst
index 2141f672462ef..92577c48b865b 100644
--- a/doc/source/whatsnew/v1.5.3.rst
+++ b/doc/source/whatsnew/v1.5.3.rst
@@ -28,6 +28,7 @@ Fixed regressions
 Bug fixes
 ~~~~~~~~~
 - Bug in :meth:`.Styler.to_excel` leading to error when unrecognized ``border-style`` (e.g. ``"hair"``) provided to Excel writers (:issue:`48649`)
+- Bug in :meth:`Series.quantile` emitting warning from NumPy when :class:`Series` has only ``NA`` values (:issue:`50681`)
 - Bug when chaining several :meth:`.Styler.concat` calls, only the last styler was concatenated (:issue:`49207`)
 - Fixed bug when instantiating a :class:`DataFrame` subclass inheriting from ``typing.Generic`` that triggered a ``UserWarning`` on python 3.11 (:issue:`49649`)
 -
diff --git a/pandas/core/array_algos/quantile.py b/pandas/core/array_algos/quantile.py
index 217fbafce719c..d3d9cb1b29b9a 100644
--- a/pandas/core/array_algos/quantile.py
+++ b/pandas/core/array_algos/quantile.py
@@ -204,8 +204,10 @@ def _nanpercentile(
             result = np.array(result, copy=False).T
             if (
                 result.dtype != values.dtype
+                and not mask.all()
                 and (result == result.astype(values.dtype, copy=False)).all()
             ):
+                # mask.all() will never get cast back to int
                 # e.g. values id integer dtype and result is floating dtype,
                 #  only cast back to integer dtype if result values are all-integer.
                 result = result.astype(values.dtype, copy=False)
diff --git a/pandas/core/arrays/masked.py b/pandas/core/arrays/masked.py
index ec3fdc6db9dc9..aaa7c706d95bb 100644
--- a/pandas/core/arrays/masked.py
+++ b/pandas/core/arrays/masked.py
@@ -1039,6 +1039,11 @@ def _quantile(
                 raise NotImplementedError
             if self.isna().all():
                 out_mask = np.ones(res.shape, dtype=bool)
+
+                if is_integer_dtype(self.dtype):
+                    # We try to maintain int dtype if possible for not all-na case
+                    # as well
+                    res = np.zeros(res.shape, dtype=self.dtype.numpy_dtype)
             else:
                 out_mask = np.zeros(res.shape, dtype=bool)
         else:
diff --git a/pandas/tests/frame/methods/test_quantile.py b/pandas/tests/frame/methods/test_quantile.py
index 93e1bcc113765..c4b9873449424 100644
--- a/pandas/tests/frame/methods/test_quantile.py
+++ b/pandas/tests/frame/methods/test_quantile.py
@@ -897,15 +897,8 @@ def test_quantile_ea_all_na(self, request, obj, index):
         qs = [0.5, 0, 1]
         result = self.compute_quantile(obj, qs)
 
-        if np_version_under1p21 and index.dtype == "timedelta64[ns]":
-            msg = "failed on Numpy 1.20.3; TypeError: data type 'Int64' not understood"
-            mark = pytest.mark.xfail(reason=msg, raises=TypeError)
-            request.node.add_marker(mark)
-
         expected = index.take([-1, -1, -1], allow_fill=True, fill_value=index._na_value)
         expected = Series(expected, index=qs, name="A")
-        if expected.dtype == "Int64":
-            expected = expected.astype("Float64")
         expected = type(obj)(expected)
         tm.assert_equal(result, expected)
 
diff --git a/pandas/tests/series/methods/test_quantile.py b/pandas/tests/series/methods/test_quantile.py
index 6326d50b20ab8..0ef1e63f742db 100644
--- a/pandas/tests/series/methods/test_quantile.py
+++ b/pandas/tests/series/methods/test_quantile.py
@@ -225,3 +225,18 @@ def test_quantile_dtypes(self, dtype):
         if dtype == "Int64":
             expected = expected.astype("Float64")
         tm.assert_series_equal(result, expected)
+
+    def test_quantile_all_na(self, any_int_ea_dtype):
+        # GH#50681
+        ser = Series([pd.NA, pd.NA], dtype=any_int_ea_dtype)
+        with tm.assert_produces_warning(None):
+            result = ser.quantile([0.1, 0.5])
+        expected = Series([pd.NA, pd.NA], dtype=any_int_ea_dtype, index=[0.1, 0.5])
+        tm.assert_series_equal(result, expected)
+
+    def test_quantile_dtype_size(self, any_int_ea_dtype):
+        # GH#50681
+        ser = Series([pd.NA, pd.NA, 1], dtype=any_int_ea_dtype)
+        result = ser.quantile([0.1, 0.5])
+        expected = Series([1, 1], dtype=any_int_ea_dtype, index=[0.1, 0.5])
+        tm.assert_series_equal(result, expected)