diff --git a/unsloth/kernels/cross_entropy_loss.py b/unsloth/kernels/cross_entropy_loss.py
index 009e351daa..f17a560200 100644
--- a/unsloth/kernels/cross_entropy_loss.py
+++ b/unsloth/kernels/cross_entropy_loss.py
@@ -246,23 +246,23 @@ def fast_cross_entropy_loss(logits, labels):
     # We now support any vocab size due to Gemma!
 
     # Prelim support Qwen, Deepseek other large vocab sizes > 2^16
-    if d > MAX_FUSED_SIZE:
-        logger.warning_once(
-            f"Unsloth: Vocab size of {d} exceeds the max CUDA blocksize of {MAX_FUSED_SIZE}.\n"\
-            "For now, Unsloth will use Pytorch's CrossEntropyLoss, which will entail a\n"\
-            "25% increase in memory usage and be slower. Make an issue on \n"\
-            "Unsloth's Github page if you want a faster and more memory efficient kernel!"
-        )
-        loss = slow_cross_entropy_loss(
-            logits.float().view(batch*seq_len, d), # Must cast to float32 for numerical stability
-            labels.view(-1),
-        )
-        return loss
-    else:
-        loss = Fast_CrossEntropyLoss.apply(
-            logits.view(batch*seq_len, d),
-            labels.view(-1),
-        )
-        n_items = torch.count_nonzero(labels != -100)
-        return loss.sum() / n_items
+    # if d > MAX_FUSED_SIZE:
+    #     logger.warning_once(
+    #         f"Unsloth: Vocab size of {d} exceeds the max CUDA blocksize of {MAX_FUSED_SIZE}.\n"\
+    #         "For now, Unsloth will use Pytorch's CrossEntropyLoss, which will entail a\n"\
+    #         "25% increase in memory usage and be slower. Make an issue on \n"\
+    #         "Unsloth's Github page if you want a faster and more memory efficient kernel!"
+    #     )
+    #     loss = slow_cross_entropy_loss(
+    #         logits.float().view(batch*seq_len, d), # Must cast to float32 for numerical stability
+    #         labels.view(-1),
+    #     )
+    #     return loss
+    # else:
+    loss = Fast_CrossEntropyLoss.apply(
+        logits.view(batch*seq_len, d),
+        labels.view(-1),
+    )
+    n_items = torch.count_nonzero(labels != -100)
+    return loss.sum() / n_items
 pass