diff --git a/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart b/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
index 95b3f2b08..26623fecc 100644
--- a/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
+++ b/mobile/lib/services/machine_learning/face_ml/feedback/cluster_feedback.dart
@@ -481,46 +481,46 @@ class ClusterFeedbackService {
     late Map<int, Vector> clusterAvgBigClusters;
     final List<(int, double)> suggestionsMean = [];
     for (final minimumSize in checkSizes.toSet()) {
-      // if (smallestPersonClusterSize >= minimumSize) {
-      clusterAvgBigClusters = await _getUpdateClusterAvg(
-        allClusterIdsToCountMap,
-        ignoredClusters,
-        minClusterSize: minimumSize,
-      );
-      w?.log(
-        'Calculate avg for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
-      );
-      final List<(int, double)> suggestionsMeanBigClusters =
-          _calcSuggestionsMean(
-        clusterAvgBigClusters,
-        personClusters,
-        ignoredClusters,
-        goodMeanDistance,
-      );
-      w?.log(
-        'Calculate suggestions using mean for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
-      );
-      for (final suggestion in suggestionsMeanBigClusters) {
-        // Skip suggestions that have a high overlap with the person's files
-        final suggestionSet = allClusterIdToFaceIDs[suggestion.$1]!
-            .map((faceID) => getFileIdFromFaceId(faceID))
-            .toSet();
-        final overlap = personFileIDs.intersection(suggestionSet);
-        if (overlap.isNotEmpty &&
-            ((overlap.length / suggestionSet.length) > 0.5)) {
-          await FaceMLDataDB.instance.captureNotPersonFeedback(
-            personID: p.remoteID,
-            clusterID: suggestion.$1,
-          );
-          continue;
+      if (smallestPersonClusterSize >= minimumSize) {
+        clusterAvgBigClusters = await _getUpdateClusterAvg(
+          allClusterIdsToCountMap,
+          ignoredClusters,
+          minClusterSize: minimumSize,
+        );
+        w?.log(
+          'Calculate avg for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
+        );
+        final List<(int, double)> suggestionsMeanBigClusters =
+            _calcSuggestionsMean(
+          clusterAvgBigClusters,
+          personClusters,
+          ignoredClusters,
+          goodMeanDistance,
+        );
+        w?.log(
+          'Calculate suggestions using mean for ${clusterAvgBigClusters.length} clusters of min size $minimumSize',
+        );
+        for (final suggestion in suggestionsMeanBigClusters) {
+          // Skip suggestions that have a high overlap with the person's files
+          final suggestionSet = allClusterIdToFaceIDs[suggestion.$1]!
+              .map((faceID) => getFileIdFromFaceId(faceID))
+              .toSet();
+          final overlap = personFileIDs.intersection(suggestionSet);
+          if (overlap.isNotEmpty &&
+              ((overlap.length / suggestionSet.length) > 0.5)) {
+            await FaceMLDataDB.instance.captureNotPersonFeedback(
+              personID: p.remoteID,
+              clusterID: suggestion.$1,
+            );
+            continue;
+          }
+          suggestionsMean.add(suggestion);
+        }
+        if (suggestionsMean.isNotEmpty) {
+          return suggestionsMean
+              .map((e) => (e.$1, e.$2, true))
+              .toList(growable: false);
         }
-        suggestionsMean.add(suggestion);
-      }
-      if (suggestionsMean.isNotEmpty) {
-        return suggestionsMean
-            .map((e) => (e.$1, e.$2, true))
-            .toList(growable: false);
-        // }
       }
     }
     w?.reset();
@@ -784,24 +784,31 @@ class ClusterFeedbackService {
     Map<int, int>? allClusterIdsToCountMap,
   }) {
     final Map<int, List<(int, double)>> suggestions = {};
+    const suggestionMax = 2000;
     int suggestionCount = 0;
+    int comparisons = 0;
     final w = (kDebugMode ? EnteWatch('getSuggestions') : null)?..start();
-    for (final otherClusterID in clusterAvg.keys) {
-      // ignore the cluster that belong to the person or is ignored
-      if (personClusters.contains(otherClusterID) ||
-          ignoredClusters.contains(otherClusterID)) {
+
+    // ignore the clusters that belong to the person or is ignored
+    Set<int> otherClusters = clusterAvg.keys.toSet().difference(personClusters);
+    otherClusters = otherClusters.difference(ignoredClusters);
+
+    for (final otherClusterID in otherClusters) {
+      final Vector? otherAvg = clusterAvg[otherClusterID];
+      if (otherAvg == null) {
+        _logger.warning('no avg for othercluster $otherClusterID');
         continue;
       }
-      final Vector otherAvg = clusterAvg[otherClusterID]!;
       int? nearestPersonCluster;
       double? minDistance;
       for (final personCluster in personClusters) {
         if (clusterAvg[personCluster] == null) {
-          _logger.info('no avg for cluster $personCluster');
+          _logger.warning('no avg for personcluster $personCluster');
           continue;
         }
         final Vector avg = clusterAvg[personCluster]!;
         final distance = cosineDistanceSIMD(avg, otherAvg);
+        comparisons++;
         if (distance < maxClusterDistance) {
           if (minDistance == null || distance < minDistance) {
             minDistance = distance;
@@ -815,11 +822,13 @@ class ClusterFeedbackService {
             .add((otherClusterID, minDistance));
         suggestionCount++;
       }
-      if (suggestionCount >= 2000) {
+      if (suggestionCount >= suggestionMax) {
         break;
       }
     }
-    w?.log('calculation inside calcSuggestionsMean');
+    w?.log(
+      'calculation inside calcSuggestionsMean for ${personClusters.length} person clusters and ${otherClusters.length} other clusters (so ${personClusters.length * otherClusters.length} combinations, $comparisons comparisons made resulted in $suggestionCount suggestions)',
+    );
 
     if (suggestions.isNotEmpty) {
       final List<(int, double)> suggestClusterIds = [];
diff --git a/mobile/lib/services/machine_learning/face_ml/person/person_service.dart b/mobile/lib/services/machine_learning/face_ml/person/person_service.dart
index 913e52268..8617e9cda 100644
--- a/mobile/lib/services/machine_learning/face_ml/person/person_service.dart
+++ b/mobile/lib/services/machine_learning/face_ml/person/person_service.dart
@@ -114,7 +114,9 @@ class PersonService {
   }
 
   bool _shouldUpdateRemotePerson(
-      PersonData personData, Map<int, Set<String>> dbPersonCluster) {
+    PersonData personData,
+    Map<int, Set<String>> dbPersonCluster,
+  ) {
     bool result = false;
     if ((personData.assigned?.length ?? 0) != dbPersonCluster.length) {
       log(