diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 8aa8f8bb60654..79805bec85af0 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -3369,8 +3369,6 @@ class  max_speed
 
         nv.validate_take(tuple(), kwargs)
 
-        self._consolidate_inplace()
-
         new_data = self._mgr.take(
             indices, axis=self._get_block_manager_axis(axis), verify=True
         )
@@ -3508,8 +3506,6 @@ class   animal   locomotion
         if axis == 1:
             return self[key]
 
-        self._consolidate_inplace()
-
         index = self.index
         if isinstance(index, MultiIndex):
             loc, new_index = self.index.get_loc_level(key, drop_level=drop_level)
@@ -5432,7 +5428,6 @@ def values(self) -> np.ndarray:
                ['lion', 80.5, 1],
                ['monkey', nan, None]], dtype=object)
         """
-        self._consolidate_inplace()
         return self._mgr.as_array(transpose=self._AXIS_REVERSED)
 
     @property
@@ -6101,8 +6096,6 @@ def fillna(
         inplace = validate_bool_kwarg(inplace, "inplace")
         value, method = validate_fillna_kwargs(value, method)
 
-        self._consolidate_inplace()
-
         # set the default here, so functions examining the signaure
         # can detect if something was set (e.g. in groupby) (GH9221)
         if axis is None:
@@ -6537,8 +6530,6 @@ def replace(
         if not is_bool(regex) and to_replace is not None:
             raise AssertionError("'to_replace' must be 'None' if 'regex' is not a bool")
 
-        self._consolidate_inplace()
-
         if value is None:
             # passing a single value that is scalar like
             # when value is None (GH5319), for compat
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index 6a7df1c8d88a1..e43c7bf887bcc 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -378,8 +378,6 @@ def apply(self: T, f, align_keys=None, **kwargs) -> T:
         result_blocks: List[Block] = []
         # fillna: Series/DataFrame is responsible for making sure value is aligned
 
-        self._consolidate_inplace()
-
         aligned_args = {k: kwargs[k] for k in align_keys}
 
         for b in self.blocks:
@@ -412,7 +410,6 @@ def apply(self: T, f, align_keys=None, **kwargs) -> T:
     def quantile(
         self,
         axis: int = 0,
-        consolidate: bool = True,
         transposed: bool = False,
         interpolation="linear",
         qs=None,
@@ -426,8 +423,6 @@ def quantile(
         Parameters
         ----------
         axis: reduction axis, default 0
-        consolidate: bool, default True. Join together blocks having same
-            dtype
         transposed: bool, default False
             we are holding transposed data
         interpolation : type of interpolation, default 'linear'
@@ -442,9 +437,6 @@ def quantile(
         #  simplify some of the code here and in the blocks
         assert self.ndim >= 2
 
-        if consolidate:
-            self._consolidate_inplace()
-
         def get_axe(block, qs, axes):
             # Because Series dispatches to DataFrame, we will always have
             #  block.ndim == 2
@@ -675,8 +667,6 @@ def is_mixed_type(self) -> bool:
 
     @property
     def is_numeric_mixed_type(self) -> bool:
-        # Warning, consolidation needs to get checked upstairs
-        self._consolidate_inplace()
         return all(block.is_numeric for block in self.blocks)
 
     @property