diff --git a/unsloth/kernels/rms_layernorm.py b/unsloth/kernels/rms_layernorm.py
index 3b54604a0b..4b22f8c3e5 100644
--- a/unsloth/kernels/rms_layernorm.py
+++ b/unsloth/kernels/rms_layernorm.py
@@ -57,7 +57,6 @@ pass
 @triton.jit
 def _rms_layernorm_backward(
     dY, dY_row_stride,
-    dX, dX_row_stride,
     X,   X_row_stride,
     W,   W_row_stride,
     r,   r_row_stride,
@@ -79,9 +78,6 @@ def _rms_layernorm_backward(
     X  += row_idx *  X_row_stride
     r  += row_idx *  r_row_stride
 
-    if GEMMA: dX += row_idx * dY_row_stride
-    else:     dX = dY
-
     dY_row = tl.load(dY + col_offsets, mask = mask, other = 0).to(tl.float32)
     X_row  = tl.load(X  + col_offsets, mask = mask, other = 0).to(tl.float32)
     W_row  = tl.load(W  + col_offsets, mask = mask, other = 0).to(tl.float32)
@@ -95,7 +91,7 @@ def _rms_layernorm_backward(
 
     rowsum_dY_normed = tl.sum(dY_W * normed, axis = 0)
     output = inv_var/n_cols * (n_cols*dY_W - normed*rowsum_dY_normed)
-    tl.store(dX + col_offsets, output, mask = mask)
+    tl.store(dY + col_offsets, output, mask = mask)
 pass
 
 
@@ -176,11 +172,9 @@ class Fast_RMS_Layernorm(torch.autograd.Function):
         n_cols : int
         n_rows, n_cols = dY.shape
         # dW = X
-        dX = torch.empty_like(dY, device = "cuda:0") if ctx.GEMMA else dY
 
         _rms_layernorm_backward[(n_rows,)](
             dY, dY.stride(0),
-            dX, dX.stride(0),
             X,  X .stride(0),
             W,  W .stride(0),
             r,  r .stride(0),