diff --git a/fvdb_reality_capture/functional/_autograd.py b/fvdb_reality_capture/functional/_autograd.py
index 6b24486c..49b2e55e 100644
--- a/fvdb_reality_capture/functional/_autograd.py
+++ b/fvdb_reality_capture/functional/_autograd.py
@@ -412,8 +412,9 @@ def backward(ctx: Any, *grad_outputs: torch.Tensor | None) -> tuple[torch.Tensor
 class _RasterizeScreenSpaceGaussiansSparseFn(torch.autograd.Function):
     """Alpha-blending at an arbitrary set of pixels with gradients to the 2D quantities.
 
-    The forward pass takes and returns flat per-pixel tensors; the jagged structure of the pixel
-    selection is saved so the backward pass can rebuild the JaggedTensors the kernel expects.
+    The forward pass takes and returns flat per-pixel tensors. The pixel selection is kept on ``ctx``
+    so the backward pass can wrap the flat gradients in the JaggedTensors the kernel expects.
+    Rebuilding it from saved structure tensors would cost a device-to-host sync per backward step.
     """
 
     @staticmethod
@@ -468,9 +469,6 @@ def forward(
             opacities,
             tile_offsets,
             tile_gaussian_ids,
-            pixels_to_render.jdata,
-            pixels_to_render.joffsets,
-            pixels_to_render.jlidx,
             alphas_jt.jdata,
             last_ids_jt.jdata,
             active_tiles,
@@ -481,6 +479,9 @@ def forward(
         _save_optional(ctx, to_save, backgrounds, masks)
         ctx.save_for_backward(*to_save)
 
+        # Integer pixel coordinates need no gradient and are not modified in place, so holding the
+        # JaggedTensor directly is safe
+        ctx.pixels_to_render = pixels_to_render
         ctx.image_width = image_width
         ctx.image_height = image_height
         ctx.image_origin_w = image_origin_w
@@ -500,11 +501,11 @@ def backward(ctx: Any, *grad_outputs: torch.Tensor | None) -> tuple[torch.Tensor
 
         saved = ctx.saved_tensors
         means2d, conics, features, opacities, tile_offsets, tile_gaussian_ids = saved[:6]
-        pixels_jdata, joffsets, jlidx, alphas_jdata, last_ids_jdata = saved[6:11]
-        active_tiles, tile_pixel_mask, tile_pixel_cumsum, pixel_map = saved[11:15]
-        backgrounds, masks = _load_optional(ctx, saved, 15)
+        alphas_jdata, last_ids_jdata = saved[6:8]
+        active_tiles, tile_pixel_mask, tile_pixel_cumsum, pixel_map = saved[8:12]
+        backgrounds, masks = _load_optional(ctx, saved, 12)
 
-        pixels_jt = JaggedTensor.from_data_offsets_and_list_ids(pixels_jdata, joffsets, jlidx)
+        pixels_jt: JaggedTensor = ctx.pixels_to_render
         assert d_rendered is not None
         assert d_alphas is not None
         _, d_means2d, d_conics, d_features, d_opacities = F.rasterize_screen_space_gaussians_sparse_bwd(