diff --git a/nimare/annotate/gclda.py b/nimare/annotate/gclda.py
index 4668f8b6b..51b1cec6e 100755
--- a/nimare/annotate/gclda.py
+++ b/nimare/annotate/gclda.py
@@ -353,7 +353,7 @@ def __init__(
             self.topics["total_n_word_tokens_by_topic"][0, topic] += 1
             self.topics["n_word_tokens_doc_by_topic"][doc, topic] += 1
 
-    def fit(self, n_iters=10000, loglikely_freq=10):
+    def fit(self, n_iters=5000, loglikely_freq=10):
         """Run multiple iterations.
 
         .. versionchanged:: 0.0.8
diff --git a/nimare/meta/cbma/ale.py b/nimare/meta/cbma/ale.py
index b57c260bc..288cf9886 100755
--- a/nimare/meta/cbma/ale.py
+++ b/nimare/meta/cbma/ale.py
@@ -156,7 +156,7 @@ def __init__(
             **kwargs,
         )
         self.null_method = null_method
-        self.n_iters = None if null_method == "approximate" else n_iters or 10000
+        self.n_iters = None if null_method == "approximate" else n_iters or 5000
         self.n_cores = _check_ncores(n_cores)
         self.dataset = None
 
@@ -403,7 +403,7 @@ class ALESubtraction(PairwiseCBMAEstimator):
     def __init__(
         self,
         kernel_transformer=ALEKernel,
-        n_iters=10000,
+        n_iters=5000,
         memory=Memory(location=None, verbose=0),
         memory_level=0,
         n_cores=1,
@@ -698,7 +698,7 @@ class SCALE(CBMAEstimator):
     def __init__(
         self,
         xyz,
-        n_iters=10000,
+        n_iters=5000,
         n_cores=1,
         kernel_transformer=ALEKernel,
         memory=Memory(location=None, verbose=0),
diff --git a/nimare/meta/cbma/mkda.py b/nimare/meta/cbma/mkda.py
index aada0982f..7cf9bdea9 100644
--- a/nimare/meta/cbma/mkda.py
+++ b/nimare/meta/cbma/mkda.py
@@ -1196,7 +1196,7 @@ def __init__(
             **kwargs,
         )
         self.null_method = null_method
-        self.n_iters = None if null_method == "approximate" else n_iters or 10000
+        self.n_iters = None if null_method == "approximate" else n_iters or 5000
         self.n_cores = _check_ncores(n_cores)
         self.dataset = None