diff --git a/heat/cluster/tests/test_spectral.py b/heat/cluster/tests/test_spectral.py
index eb59d92ce5..9e24dddfc5 100644
--- a/heat/cluster/tests/test_spectral.py
+++ b/heat/cluster/tests/test_spectral.py
@@ -35,51 +35,49 @@ def test_get_and_set_params(self):
         self.assertEqual(10, spectral.n_clusters)
 
     def test_fit_iris(self):
-        if ht.MPI_WORLD.size <= 4:
-            # todo: fix tests with >7 processes, NaNs appearing in spectral._spectral_embedding
-            # get some test data
-            iris = ht.load("heat/datasets/iris.csv", sep=";", split=0)
-            m = 10
-            # fit the clusters
-            spectral = ht.cluster.Spectral(
-                n_clusters=3, gamma=1.0, metric="rbf", laplacian="fully_connected", n_lanczos=m
-            )
-            spectral.fit(iris)
-            self.assertIsInstance(spectral.labels_, ht.DNDarray)
+        # get some test data
+        iris = ht.load("heat/datasets/iris.csv", sep=";", split=0)
+        m = 10
+        # fit the clusters
+        spectral = ht.cluster.Spectral(
+            n_clusters=3, gamma=1.0, metric="rbf", laplacian="fully_connected", n_lanczos=m
+        )
+        spectral.fit(iris)
+        self.assertIsInstance(spectral.labels_, ht.DNDarray)
 
-            spectral = ht.cluster.Spectral(
-                metric="euclidean",
-                laplacian="eNeighbour",
-                threshold=0.5,
-                boundary="upper",
-                n_lanczos=m,
-            )
-            labels = spectral.fit_predict(iris)
-            self.assertIsInstance(labels, ht.DNDarray)
+        spectral = ht.cluster.Spectral(
+            metric="euclidean",
+            laplacian="eNeighbour",
+            threshold=0.5,
+            boundary="upper",
+            n_lanczos=m,
+        )
+        labels = spectral.fit_predict(iris)
+        self.assertIsInstance(labels, ht.DNDarray)
 
-            spectral = ht.cluster.Spectral(
-                gamma=0.1,
-                metric="rbf",
-                laplacian="eNeighbour",
-                threshold=0.5,
-                boundary="upper",
-                n_lanczos=m,
-            )
-            labels = spectral.fit_predict(iris)
-            self.assertIsInstance(labels, ht.DNDarray)
+        spectral = ht.cluster.Spectral(
+            gamma=0.1,
+            metric="rbf",
+            laplacian="eNeighbour",
+            threshold=0.5,
+            boundary="upper",
+            n_lanczos=m,
+        )
+        labels = spectral.fit_predict(iris)
+        self.assertIsInstance(labels, ht.DNDarray)
 
-            kmeans = {"kmeans++": "kmeans++", "max_iter": 30, "tol": -1}
-            spectral = ht.cluster.Spectral(
-                n_clusters=3, gamma=1.0, normalize=True, n_lanczos=m, params=kmeans
-            )
-            labels = spectral.fit_predict(iris)
-            self.assertIsInstance(labels, ht.DNDarray)
+        kmeans = {"kmeans++": "kmeans++", "max_iter": 30, "tol": -1}
+        spectral = ht.cluster.Spectral(
+            n_clusters=3, gamma=1.0, normalize=True, n_lanczos=m, params=kmeans
+        )
+        labels = spectral.fit_predict(iris)
+        self.assertIsInstance(labels, ht.DNDarray)
 
-            # Errors
-            with self.assertRaises(NotImplementedError):
-                spectral = ht.cluster.Spectral(metric="ahalanobis", n_lanczos=m)
+        # Errors
+        with self.assertRaises(NotImplementedError):
+            spectral = ht.cluster.Spectral(metric="ahalanobis", n_lanczos=m)
 
-            iris_split = ht.load("heat/datasets/iris.csv", sep=";", split=1)
-            spectral = ht.cluster.Spectral(n_lanczos=20)
-            with self.assertRaises(NotImplementedError):
-                spectral.fit(iris_split)
+        iris_split = ht.load("heat/datasets/iris.csv", sep=";", split=1)
+        spectral = ht.cluster.Spectral(n_lanczos=20)
+        with self.assertRaises(NotImplementedError):
+            spectral.fit(iris_split)