diff --git a/src/spikeinterface/sortingcomponents/matching/circus.py b/src/spikeinterface/sortingcomponents/matching/circus.py
index 64a6b2333d..faf73465ff 100644
--- a/src/spikeinterface/sortingcomponents/matching/circus.py
+++ b/src/spikeinterface/sortingcomponents/matching/circus.py
@@ -50,11 +50,16 @@ def compress_templates(
     if remove_mean:
         templates_array -= templates_array.mean(axis=(1, 2))[:, None, None]
 
-    temporal, singular, spatial = np.linalg.svd(templates_array, full_matrices=False)
-    # Keep only the strongest components
-    temporal = temporal[:, :, :approx_rank].astype(np.float32)
-    singular = singular[:, :approx_rank].astype(np.float32)
-    spatial = spatial[:, :approx_rank, :].astype(np.float32)
+    num_templates, num_samples, num_channels = templates_array.shape
+    temporal = np.zeros((num_templates, num_samples, approx_rank), dtype=np.float32)
+    spatial = np.zeros((num_templates, approx_rank, num_channels), dtype=np.float32)
+    singular = np.zeros((num_templates, approx_rank), dtype=np.float32)
+
+    for i in range(num_templates):
+        i_temporal, i_singular, i_spatial = np.linalg.svd(templates_array[i], full_matrices=False)
+        temporal[i, :, : min(approx_rank, num_channels)] = i_temporal[:, :approx_rank]
+        spatial[i, : min(approx_rank, num_channels), :] = i_spatial[:approx_rank, :]
+        singular[i, : min(approx_rank, num_channels)] = i_singular[:approx_rank]
 
     if return_new_templates:
         templates_array = np.matmul(temporal * singular[:, np.newaxis, :], spatial)