diff --git a/src/state/_cli/_emb/_eval.py b/src/state/_cli/_emb/_eval.py
index 74ddd8fb..d2313f4c 100644
--- a/src/state/_cli/_emb/_eval.py
+++ b/src/state/_cli/_emb/_eval.py
@@ -118,8 +118,9 @@ def load_config_override(config_path: str | None = None):
     with torch.no_grad():
         with torch.autocast(device_type=device_type, dtype=precision):
             for batch in tqdm(dataloader, desc="Processing batches"):
-                torch.cuda.synchronize()
-                torch.cuda.empty_cache()
+                if device_type == "cuda":
+                    torch.cuda.synchronize()
+                    torch.cuda.empty_cache()
 
                 # Compute embeddings
                 _, _, _, emb, ds_emb = model._compute_embedding_for_batch(batch)
diff --git a/src/state/emb/nn/eval_utils.py b/src/state/emb/nn/eval_utils.py
index 283d2977..1df78a94 100644
--- a/src/state/emb/nn/eval_utils.py
+++ b/src/state/emb/nn/eval_utils.py
@@ -76,7 +76,8 @@ def evaluate_de(model, cfg, device=None, logger=print):
     pred_exp = model._predict_exp_for_adata(
         tmp_adata, cfg["validations"]["diff_exp"]["dataset_name"], cfg["validations"]["diff_exp"]["obs_pert_col"]
     )
-    torch.cuda.synchronize()
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
     de_metrics = compute_gene_overlap_cross_pert(
         pred_exp, true_top_genes, k=cfg["validations"]["diff_exp"]["top_k_rank"]
     )