diff --git a/projects/nerf/nerf/implicit_function.py b/projects/nerf/nerf/implicit_function.py
index db5b37d5..f07cec2c 100644
--- a/projects/nerf/nerf/implicit_function.py
+++ b/projects/nerf/nerf/implicit_function.py
@@ -1,5 +1,5 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
-from typing import List
+from typing import Tuple
 
 import torch
 from pytorch3d.renderer import RayBundle, ray_bundle_to_ray_points
@@ -23,7 +23,8 @@ class NeuralRadianceField(torch.nn.Module):
         n_hidden_neurons_xyz: int = 256,
         n_hidden_neurons_dir: int = 128,
         n_layers_xyz: int = 8,
-        append_xyz: List[int] = (5,),
+        append_xyz: Tuple[int] = (5,),
+        use_multiple_streams: bool = True,
         **kwargs,
     ):
         """
@@ -43,6 +44,8 @@ class NeuralRadianceField(torch.nn.Module):
             n_layers_xyz: The number of layers of the MLP that outputs the
                 occupancy field.
             append_xyz: The list of indices of the skip layers of the occupancy MLP.
+            use_multiple_streams: Whether density and color should be calculated on
+                separate CUDA streams.
         """
         super().__init__()
 
@@ -83,13 +86,14 @@ class NeuralRadianceField(torch.nn.Module):
             torch.nn.Linear(n_hidden_neurons_dir, 3),
             torch.nn.Sigmoid(),
         )
+        self.use_multiple_streams = use_multiple_streams
 
     def _get_densities(
         self,
         features: torch.Tensor,
         depth_values: torch.Tensor,
         density_noise_std: float,
-    ):
+    ) -> torch.Tensor:
         """
         This function takes `features` predicted by `self.mlp_xyz`
         and converts them to `raw_densities` with `self.density_layer`.
@@ -111,7 +115,9 @@ class NeuralRadianceField(torch.nn.Module):
         densities = 1 - (-deltas * torch.relu(raw_densities)).exp()
         return densities
 
-    def _get_colors(self, features: torch.Tensor, rays_directions: torch.Tensor):
+    def _get_colors(
+        self, features: torch.Tensor, rays_directions: torch.Tensor
+    ) -> torch.Tensor:
         """
         This function takes per-point `features` predicted by `self.mlp_xyz`
         and evaluates the color model in order to attach to each
@@ -125,12 +131,54 @@ class NeuralRadianceField(torch.nn.Module):
 
         return self.color_layer((self.intermediate_linear(features), rays_embedding))
 
+    def _get_densities_and_colors(
+        self, features: torch.Tensor, ray_bundle: RayBundle, density_noise_std: float
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        The second part of the forward calculation.
+
+        Args:
+            features: the output of the common mlp (the prior part of the
+                calculation), shape
+                (minibatch x ... x self.n_hidden_neurons_xyz).
+            ray_bundle: As for forward().
+            density_noise_std:  As for forward().
+
+        Returns:
+            rays_densities: A tensor of shape `(minibatch, ..., num_points_per_ray, 1)`
+                denoting the opacity of each ray point.
+            rays_colors: A tensor of shape `(minibatch, ..., num_points_per_ray, 3)`
+                denoting the color of each ray point.
+        """
+        if self.use_multiple_streams and features.is_cuda:
+            current_stream = torch.cuda.current_stream(features.device)
+            other_stream = torch.cuda.Stream(features.device)
+            other_stream.wait_stream(current_stream)
+
+            with torch.cuda.stream(other_stream):
+                rays_densities = self._get_densities(
+                    features, ray_bundle.lengths, density_noise_std
+                )
+                # rays_densities.shape = [minibatch x ... x 1] in [0-1]
+
+            rays_colors = self._get_colors(features, ray_bundle.directions)
+            # rays_colors.shape = [minibatch x ... x 3] in [0-1]
+
+            current_stream.wait_stream(other_stream)
+        else:
+            # Same calculation as above, just serial.
+            rays_densities = self._get_densities(
+                features, ray_bundle.lengths, density_noise_std
+            )
+            rays_colors = self._get_colors(features, ray_bundle.directions)
+        return rays_densities, rays_colors
+
     def forward(
         self,
         ray_bundle: RayBundle,
         density_noise_std: float = 0.0,
         **kwargs,
-    ):
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         """
         The forward function accepts the parametrizations of
         3D points sampled along projection rays. The forward
@@ -169,14 +217,9 @@ class NeuralRadianceField(torch.nn.Module):
         features = self.mlp_xyz(embeds_xyz, embeds_xyz)
         # features.shape = [minibatch x ... x self.n_hidden_neurons_xyz]
 
-        rays_densities = self._get_densities(
-            features, ray_bundle.lengths, density_noise_std
+        rays_densities, rays_colors = self._get_densities_and_colors(
+            features, ray_bundle, density_noise_std
         )
-        # rays_densities.shape = [minibatch x ... x 1] in [0-1]
-
-        rays_colors = self._get_colors(features, ray_bundle.directions)
-        # rays_colors.shape = [minibatch x ... x 3] in [0-1]
-
         return rays_densities, rays_colors
 
 
@@ -207,7 +250,7 @@ class MLPWithInputSkips(torch.nn.Module):
         output_dim: int,
         skip_dim: int,
         hidden_dim: int,
-        input_skips: List[int] = (),
+        input_skips: Tuple[int] = (),
     ):
         """
         Args:
@@ -238,7 +281,7 @@ class MLPWithInputSkips(torch.nn.Module):
         self.mlp = torch.nn.ModuleList(layers)
         self._input_skips = set(input_skips)
 
-    def forward(self, x, z):
+    def forward(self, x: torch.Tensor, z: torch.Tensor) -> torch.Tensor:
         """
         Args:
             x: The input tensor of shape `(..., input_dim)`.
diff --git a/projects/nerf/nerf/nerf_renderer.py b/projects/nerf/nerf/nerf_renderer.py
index c84afc26..084cecc5 100644
--- a/projects/nerf/nerf/nerf_renderer.py
+++ b/projects/nerf/nerf/nerf_renderer.py
@@ -62,7 +62,7 @@ class RadianceFieldRenderer(torch.nn.Module):
         n_hidden_neurons_xyz: int = 256,
         n_hidden_neurons_dir: int = 128,
         n_layers_xyz: int = 8,
-        append_xyz: List[int] = (5,),
+        append_xyz: Tuple[int] = (5,),
         density_noise_std: float = 0.0,
     ):
         """