diff --git a/nerfstudio/field_components/encodings.py b/nerfstudio/field_components/encodings.py
index 652d387f5..6e0158654 100644
--- a/nerfstudio/field_components/encodings.py
+++ b/nerfstudio/field_components/encodings.py
@@ -274,18 +274,18 @@ class HashEncoding(Encoding):
         self.hash_table_size = 2**log2_hashmap_size
 
         levels = torch.arange(num_levels)
-        growth_factor = np.exp((np.log(max_res) - np.log(min_res)) / (num_levels - 1))
+        growth_factor = np.exp((np.log(max_res) - np.log(min_res)) / (num_levels - 1)) if num_levels > 1 else 1
         self.scalings = torch.floor(min_res * growth_factor**levels)
 
         self.hash_offset = levels * self.hash_table_size
-        self.hash_table = torch.rand(size=(self.hash_table_size * num_levels, features_per_level)) * 2 - 1
-        self.hash_table *= hash_init_scale
-        self.hash_table = nn.Parameter(self.hash_table)
 
         self.tcnn_encoding = None
+        self.hash_table = torch.empty(0)
         if implementation == "tcnn" and not TCNN_EXISTS:
             print_tcnn_speed_warning("HashEncoding")
-        elif implementation == "tcnn":
+            implementation = "torch"
+
+        if implementation == "tcnn":
             encoding_config = {
                 "otype": "HashGrid",
                 "n_levels": self.num_levels,
@@ -301,6 +301,10 @@ class HashEncoding(Encoding):
                 n_input_dims=3,
                 encoding_config=encoding_config,
             )
+        elif implementation == "torch":
+            self.hash_table = torch.rand(size=(self.hash_table_size * num_levels, features_per_level)) * 2 - 1
+            self.hash_table *= hash_init_scale
+            self.hash_table = nn.Parameter(self.hash_table)
 
         if self.tcnn_encoding is None:
             assert (