diff --git a/nsrdb/config/templates/config_nsrdb_post2017.json b/nsrdb/config/templates/config_nsrdb_post2017.json
index 8fd8a7eb..a10f1ada 100644
--- a/nsrdb/config/templates/config_nsrdb_post2017.json
+++ b/nsrdb/config/templates/config_nsrdb_post2017.json
@@ -70,6 +70,8 @@
     "walltime": 4
   },
   "ml-cloud-fill": {
-    "fill_all": false
+    "col_chunk": 10000,
+    "fill_all": false,
+    "max_workers": null
   }
 }
diff --git a/nsrdb/config/templates/config_nsrdb_pre2018.json b/nsrdb/config/templates/config_nsrdb_pre2018.json
index f729afa2..7bd8613e 100644
--- a/nsrdb/config/templates/config_nsrdb_pre2018.json
+++ b/nsrdb/config/templates/config_nsrdb_pre2018.json
@@ -70,6 +70,8 @@
     "walltime": 4
   },
   "ml-cloud-fill": {
-    "fill_all": false
+    "col_chunk": 10000,
+    "fill_all": false,
+    "max_workers": null
   }
 }
diff --git a/nsrdb/gap_fill/mlclouds_fill.py b/nsrdb/gap_fill/mlclouds_fill.py
index 671fe13f..4483f0f4 100644
--- a/nsrdb/gap_fill/mlclouds_fill.py
+++ b/nsrdb/gap_fill/mlclouds_fill.py
@@ -1008,6 +1008,8 @@ def run(cls, h5_source, fill_all=False, model_path=None, var_meta=None,
                     .format(h5_source))
         logger.info('Running MLCloudsFill with model: {}'
                     .format(model_path))
+        logger.info('Running MLCloudsFill with col_chunk: {}'
+                    .format(col_chunk))
         obj = cls(h5_source, fill_all=fill_all, model_path=model_path,
                   var_meta=var_meta)
         obj.archive_cld_properties()
@@ -1015,14 +1017,19 @@ def run(cls, h5_source, fill_all=False, model_path=None, var_meta=None,
 
         if col_chunk is None:
             slices = [slice(None)]
+            logger.info('MLClouds gap fill is being run without col_chunk for '
+                        'full data shape {} all on one process. If you see '
+                        'memory errors, try setting the col_chunk input to '
+                        'distribute the problem across multiple small workers.'
+                        .format(obj._res_shape))
         else:
             columns = np.arange(obj._res_shape[1])
             N = np.ceil(len(columns) / col_chunk)
             arrays = np.array_split(columns, N)
             slices = [slice(a[0], 1 + a[-1]) for a in arrays]
-            logger.info('Gap fill will be run across the full data column '
-                        'shape {} in {} column chunks with approximately {} '
-                        'sites per chunk'
+            logger.info('MLClouds gap fill will be run across the full data '
+                        'column shape {} in {} column chunks with '
+                        'approximately {} sites per chunk'
                         .format(len(columns), len(slices), col_chunk))
 
         if max_workers == 1: