diff --git a/unsloth/kernels/fast_lora.py b/unsloth/kernels/fast_lora.py
index 6ec4bbf141..3599f2f97b 100644
--- a/unsloth/kernels/fast_lora.py
+++ b/unsloth/kernels/fast_lora.py
@@ -128,31 +128,31 @@ class LoRA_MLP(torch.autograd.Function):
         h, df, de = DW, e, g
 
         # Down projection LoRA weights
-        d_downA = (h.t() @ dY) @ downB.t()
-        d_downB = downA.t() @ (h.t() @ dY)
+        d_downA = h.t() @ (dY @ downB.t())
+        d_downB = (downA.t() @ h.t()) @ dY
         d_downA *= downS
         d_downB *= downS
 
         # Up projection LoRA weights
-        d_upA   = (X.t() @ df) @ upB.t()
-        d_upB   = upA.t() @ (X.t() @ df)
+        d_upA   = X.t() @ (df @ upB.t())
+        d_upB   = (upA.t() @ X.t()) @ df
         d_upA  *= upS
         d_upB  *= upS
 
         # Gate projection LoRA weights
-        d_gateA = (X.t() @ de) @ gateB.t()
-        d_gateB = gateA.t() @ (X.t() @ de)
+        d_gateA = X.t() @ (de @ gateB.t())
+        d_gateB = (gateA.t() @ X.t()) @ de
         d_gateA *= gateS
         d_gateB *= gateS
 
         # dX  = matmul_lora(df, upW.t(), upW_quant, upB, upA, upS)
         # dX += matmul_lora(de, gateW.t(), gateW_quant, gateB, gateA, gateS)
         upW = fast_dequantize(upW.t(), upW_quant)
-        dX = torch.matmul(df, upW.t())#, out = X)
-        del upW
+        dX = torch.matmul(df, upW.t(), out = X)
+        #del upW
         dX += df @ upB.to(dtype).t() @ (upS * upA.to(dtype).t())
 
-        gateW = fast_dequantize(gateW.t(), gateW_quant)
+        gateW = fast_dequantize(gateW.t(), gateW_quant, out = upW.t())
         dX += de @ gateW.t()
         del gateW
         dX += de @ gateB.to(dtype).t() @ (gateS * gateA.to(dtype).t())