diff --git a/asv_bench/benchmarks/rolling.py b/asv_bench/benchmarks/rolling.py
index f85dc83ab8605..e3c9c7ccdc51c 100644
--- a/asv_bench/benchmarks/rolling.py
+++ b/asv_bench/benchmarks/rolling.py
@@ -150,19 +150,18 @@ def time_quantile(self, constructor, window, dtype, percentile, interpolation):
         self.roll.quantile(percentile, interpolation=interpolation)
 
 
-class PeakMemFixed:
-    def setup(self):
-        N = 10
-        arr = 100 * np.random.random(N)
-        self.roll = pd.Series(arr).rolling(10)
-
-    def peakmem_fixed(self):
-        # GH 25926
-        # This is to detect memory leaks in rolling operations.
-        # To save time this is only ran on one method.
-        # 6000 iterations is enough for most types of leaks to be detected
-        for x in range(6000):
-            self.roll.max()
+class PeakMemFixedWindowMinMax:
+
+    params = ["min", "max"]
+
+    def setup(self, operation):
+        N = int(1e6)
+        arr = np.random.random(N)
+        self.roll = pd.Series(arr).rolling(2)
+
+    def peakmem_fixed(self, operation):
+        for x in range(5):
+            getattr(self.roll, operation)()
 
 
 class ForwardWindowMethods:
diff --git a/doc/source/whatsnew/v1.1.0.rst b/doc/source/whatsnew/v1.1.0.rst
index 07849702c646d..43bf0e82490e2 100644
--- a/doc/source/whatsnew/v1.1.0.rst
+++ b/doc/source/whatsnew/v1.1.0.rst
@@ -608,6 +608,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrame.resample` where an ``AmbiguousTimeError`` would be raised when the resulting timezone aware :class:`DatetimeIndex` had a DST transition at midnight (:issue:`25758`)
 - Bug in :meth:`DataFrame.groupby` where a ``ValueError`` would be raised when grouping by a categorical column with read-only categories and ``sort=False`` (:issue:`33410`)
 - Bug in :meth:`GroupBy.first` and :meth:`GroupBy.last` where None is not preserved in object dtype (:issue:`32800`)
+- Bug in :meth:`Rolling.min` and :meth:`Rolling.max`: Growing memory usage after multiple calls when using a fixed window (:issue:`30726`)
 
 Reshaping
 ^^^^^^^^^
diff --git a/pandas/_libs/window/aggregations.pyx b/pandas/_libs/window/aggregations.pyx
index 673820fd8464a..afa0539014041 100644
--- a/pandas/_libs/window/aggregations.pyx
+++ b/pandas/_libs/window/aggregations.pyx
@@ -971,8 +971,8 @@ cdef inline numeric calc_mm(int64_t minp, Py_ssize_t nobs,
     return result
 
 
-def roll_max_fixed(ndarray[float64_t] values, ndarray[int64_t] start,
-                   ndarray[int64_t] end, int64_t minp, int64_t win):
+def roll_max_fixed(float64_t[:] values, int64_t[:] start,
+                   int64_t[:] end, int64_t minp, int64_t win):
     """
     Moving max of 1d array of any numeric type along axis=0 ignoring NaNs.
 
@@ -988,7 +988,7 @@ def roll_max_fixed(ndarray[float64_t] values, ndarray[int64_t] start,
             make the interval closed on the right, left,
             both or neither endpoints
     """
-    return _roll_min_max_fixed(values, start, end, minp, win, is_max=1)
+    return _roll_min_max_fixed(values, minp, win, is_max=1)
 
 
 def roll_max_variable(ndarray[float64_t] values, ndarray[int64_t] start,
@@ -1011,8 +1011,8 @@ def roll_max_variable(ndarray[float64_t] values, ndarray[int64_t] start,
     return _roll_min_max_variable(values, start, end, minp, is_max=1)
 
 
-def roll_min_fixed(ndarray[float64_t] values, ndarray[int64_t] start,
-                   ndarray[int64_t] end, int64_t minp, int64_t win):
+def roll_min_fixed(float64_t[:] values, int64_t[:] start,
+                   int64_t[:] end, int64_t minp, int64_t win):
     """
     Moving min of 1d array of any numeric type along axis=0 ignoring NaNs.
 
@@ -1025,7 +1025,7 @@ def roll_min_fixed(ndarray[float64_t] values, ndarray[int64_t] start,
     index : ndarray, optional
        index for window computation
     """
-    return _roll_min_max_fixed(values, start, end, minp, win, is_max=0)
+    return _roll_min_max_fixed(values, minp, win, is_max=0)
 
 
 def roll_min_variable(ndarray[float64_t] values, ndarray[int64_t] start,
@@ -1112,9 +1112,7 @@ cdef _roll_min_max_variable(ndarray[numeric] values,
     return output
 
 
-cdef _roll_min_max_fixed(ndarray[numeric] values,
-                         ndarray[int64_t] starti,
-                         ndarray[int64_t] endi,
+cdef _roll_min_max_fixed(numeric[:] values,
                          int64_t minp,
                          int64_t win,
                          bint is_max):