diff --git a/unsloth/kernels/fast_lora.py b/unsloth/kernels/fast_lora.py
index 05829cbc94..e3d43b6596 100644
--- a/unsloth/kernels/fast_lora.py
+++ b/unsloth/kernels/fast_lora.py
@@ -194,8 +194,9 @@ class LoRA_MLP_New(torch.autograd.Function):
 
         e = matmul_lora(X, gateW, gateW_quant, gateA, gateB, gateS)
         g = matmul_lora(X,   upW,   upW_quant,   upA,   upB,   upS)
-        f = torch.nn.functional.silu(e)
-        h = f * g
+        # f = torch.nn.functional.silu(e)
+        # h = f * g
+        h = swiglu_fg_kernel(e, g)
         i = matmul_lora(h, downW, downW_quant, downA, downB, downS)
 
         ctx.custom_saved_tensors = (
@@ -208,11 +209,6 @@ class LoRA_MLP_New(torch.autograd.Function):
         return i
     pass
 
-    def _silu_backward(dy, X):
-        # https://github.com/pytorch/pytorch/blob/563b065f5a4b4055fa6b025c2514b566d5fd9439/aten/src/ATen/native/Activation.cpp#L483
-        sigm = 1 / (1 + torch.exp(-X.float()))
-        return (dy.float() * sigm * (1 + X.float() * (1 - sigm))).to(X.dtype)
-    pass
 
     @classmethod
     @torch.cuda.amp.custom_bwd
@@ -232,13 +228,15 @@ class LoRA_MLP_New(torch.autograd.Function):
         g  = g .view(-1, g .shape[-1])
         dtype = X.dtype
 
-        f = torch.nn.functional.silu(e)
-        h = f * g
         DW = matmul_lora(dY, downW.t(), downW_quant, downB, downA, downS)
-        df = DW * f  # 88us
-        dg = DW * g  # 88us
-        sigm = 1.0 / (1.0 + torch.exp(-e.float()))
-        de = (dg.float() * sigm * (1.0 + e.float() * (1.0 - sigm))).to(dtype)
+
+        e = e.float()
+        se = 1.0 / (1.0 + torch.exp(-e))
+        f = se * e
+        h = f * g
+        df = DW * f
+        dg = DW * g
+        de = (dg.float() * se * (1.0 + e * (1.0 - se))).to(dtype)
 
         # Down projection LoRA weights
         d_downA = h.t() @ (dY @ downB.t())