diff --git a/rfdiffusion/inference/utils.py b/rfdiffusion/inference/utils.py
index 3fb14112..4e318118 100644
--- a/rfdiffusion/inference/utils.py
+++ b/rfdiffusion/inference/utils.py
@@ -6,7 +6,7 @@
 from rfdiffusion.diffusion import get_beta_schedule
 from scipy.spatial.transform import Rotation as scipy_R
 from rfdiffusion.util import rigid_from_3_points
-from rfdiffusion.util_module import ComputeAllAtomCoords
+
 from rfdiffusion import util
 import random
 import logging
@@ -152,7 +152,6 @@ def get_next_ca(
         noise_scale: scale factor for the noise being added
 
     """
-    get_allatom = ComputeAllAtomCoords().to(device=xt.device)
     L = len(xt)
 
     # bring to origin after global alignment (when don't have a motif) or replace input motif and bring to origin, and then scale
@@ -435,7 +434,6 @@ def get_next_pose(
             include_motif_sidechains (bool): Provide sidechains of the fixed motif to the model
         """
 
-        get_allatom = ComputeAllAtomCoords().to(device=xt.device)
         L, n_atom = xt.shape[:2]
         assert (xt.shape[1] == 14) or (xt.shape[1] == 27)
         assert (px0.shape[1] == 14) or (px0.shape[1] == 27)
diff --git a/scripts/run_inference.py b/scripts/run_inference.py
index 3fb6466e..623516e1 100755
--- a/scripts/run_inference.py
+++ b/scripts/run_inference.py
@@ -94,10 +94,13 @@ def main(conf: HydraConfig) -> None:
             px0, x_t, seq_t, plddt = sampler.sample_step(
                 t=t, x_t=x_t, seq_init=seq_t, final_step=sampler.inf_conf.final_step
             )
-            px0_xyz_stack.append(px0)
-            denoised_xyz_stack.append(x_t)
-            seq_stack.append(seq_t)
-            plddt_stack.append(plddt[0])  # remove singleton leading dimension
+            # Move trajectory data to CPU immediately to free GPU memory
+            # for the next denoising step. x_t/seq_t are cloned inside
+            # sample_step, so moving previous outputs to CPU is safe.
+            px0_xyz_stack.append(px0.cpu())
+            denoised_xyz_stack.append(x_t.cpu())
+            seq_stack.append(seq_t.cpu())
+            plddt_stack.append(plddt[0].cpu())  # remove singleton leading dimension
 
         # Flip order for better visualization in pymol
         denoised_xyz_stack = torch.stack(denoised_xyz_stack)