diff --git a/empyricalRMT/observables/rigidity.py b/empyricalRMT/observables/rigidity.py
index 28d7e82..55d88f5 100644
--- a/empyricalRMT/observables/rigidity.py
+++ b/empyricalRMT/observables/rigidity.py
@@ -301,7 +301,7 @@ def delta_L(
     return np.mean(delta_running), converged, k  # type: ignore
 
 
-@jit(nopython=True, cache=True, fastmath=True)
+@jit(nopython=True, cache=False, fastmath=True)
 def _delta_grid(
     unfolded: ndarray, starts: ndarray, L: float, gridsize: int, use_simpson: bool
 ) -> f64:
@@ -320,7 +320,7 @@ def _delta_grid(
     return np.mean(delta3s)  # type: ignore
 
 
-@jit(nopython=True, cache=True, fastmath=True)
+@jit(nopython=True, cache=False, fastmath=True)
 def _slope(x: ndarray, y: ndarray) -> f64:
     """Perform linear regression to compute the slope."""
     x_mean = np.mean(x)
@@ -334,12 +334,12 @@ def _slope(x: ndarray, y: ndarray) -> f64:
     return cov / var  # type: ignore
 
 
-@jit(nopython=True, cache=True, fastmath=True)
+@jit(nopython=True, cache=False, fastmath=True)
 def _intercept(x: ndarray, y: ndarray, slope: f64) -> f64:
     return np.mean(y) - slope * np.mean(x)  # type: ignore
 
 
-@jit(nopython=True, cache=True, fastmath=True)
+@jit(nopython=True, cache=False, fastmath=True)
 def _integrate_fast(grid: ndarray, values: ndarray) -> f64:
     """scipy.integrate.trapz is excruciatingly slow and unusable for our purposes.
     This tiny rewrite seems to result in a near 20x speedup. However, being trapezoidal
@@ -354,7 +354,7 @@ def _integrate_fast(grid: ndarray, values: ndarray) -> f64:
 
 # NOTE: !!!! Very important *NOT* to use parallel=True here, since we parallelize
 # the outer loops. Adding it inside *dramatically* slows performance.
-@jit(nopython=True, cache=True, fastmath=True)
+@jit(nopython=True, cache=False, fastmath=True)
 def _sq_lin_deviation(eigs: ndarray, steps: ndarray, K: f64, w: f64, grid: fArr) -> fArr:
     """Compute the sqaured deviation of the staircase function of the best fitting
     line, over the region in `grid`.
@@ -390,7 +390,7 @@ def _sq_lin_deviation(eigs: ndarray, steps: ndarray, K: f64, w: f64, grid: fArr)
 
 
 # fmt: off
-@jit(nopython=True, cache=True, fastmath=True)
+@jit(nopython=True, cache=False, fastmath=True)
 def _int_simps_nonunif(grid: fArr, vals: fArr) -> f64:
     """
     Simpson rule for irregularly spaced data. Copied shamelessly from
diff --git a/empyricalRMT/observables/step.py b/empyricalRMT/observables/step.py
index 52e676e..2ae35b3 100644
--- a/empyricalRMT/observables/step.py
+++ b/empyricalRMT/observables/step.py
@@ -32,7 +32,7 @@ def step_values(eigs: fArr, x: Union[float, fArr]) -> Union[float, iArr]:
     return cast(iArr, _step_function_fast(eigs, x))
 
 
-@njit(fastmath=True, cache=True)  # type: ignore[misc]
+@njit(fastmath=True, cache=False)  # type: ignore[misc]
 def _step_function_fast(eigs: fArr, x: fArr) -> iArr:
     """optimized version that does not repeatedly call np.sum(eigs <= x), since
     this function needed to be called extensively in rigidity calculation."""
@@ -70,7 +70,7 @@ def _step_function_fast(eigs: fArr, x: fArr) -> iArr:
     return ret
 
 
-@njit(fastmath=True, cache=True, parallel=True)
+@njit(fastmath=True, cache=False, parallel=True)
 def _step_function_correct(eigs: fArr, x: fArr) -> fArr:
     """Intended for testing _step_function_fast correctness, as this function
     is for sure correct, just slow.
diff --git a/empyricalRMT/utils.py b/empyricalRMT/utils.py
index 81161a2..95fd905 100644
--- a/empyricalRMT/utils.py
+++ b/empyricalRMT/utils.py
@@ -85,7 +85,7 @@ def make_parent_directories(path: Path) -> None:
         make_directory(path)
 
 
-@jit(nopython=True, cache=True)
+@jit(nopython=True)
 def kahan_add(current_sum: f64, update: f64, carry_over: f64) -> Tuple[f64, f64]:
     """
     Returns
@@ -103,7 +103,7 @@ def kahan_add(current_sum: f64, update: f64, carry_over: f64) -> Tuple[f64, f64]
     return updated_sum, c
 
 
-@jit(nopython=True, cache=True, fastmath=True)
+@jit(nopython=True, fastmath=True)
 def nd_find(arr: ndarray, value: Any) -> Optional[int]:
     for i, val in np.ndenumerate(arr):
         if val == value:
@@ -133,7 +133,7 @@ def flatten_4D(img4D: ndarray) -> np.ndarray:
         return img4D.reshape((np.prod(img4D.shape[0:-1]),) + (img4D.shape[-1],))
 
 
-@jit(nopython=True, fastmath=True, cache=True)
+@jit(nopython=True, fastmath=True)
 def slope(x: ndarray, y: ndarray) -> np.float64:
     x_mean = np.mean(x)
     y_mean = np.mean(y)
@@ -158,12 +158,12 @@ def variance(arr: ndarray) -> np.float64:
     return np.float64(scale * summed)
 
 
-@jit(nopython=True, fastmath=True, cache=True)
+@jit(nopython=True, fastmath=True)
 def intercept(x: ndarray, y: ndarray, slope: np.float64) -> np.float64:
     return np.float64(np.mean(y) - slope * np.mean(x))
 
 
-@jit(nopython=True, fastmath=True, cache=True)
+@jit(nopython=True, fastmath=True)
 def fast_r(x: ndarray, y: ndarray) -> np.float64:
     n = len(x)
     num = x * y - n * np.mean(x) * np.mean(y)