diff --git a/unsloth_zoo/temporary_patches/moe_utils.py b/unsloth_zoo/temporary_patches/moe_utils.py
index e686fcc82..e9b7a8d0e 100644
--- a/unsloth_zoo/temporary_patches/moe_utils.py
+++ b/unsloth_zoo/temporary_patches/moe_utils.py
@@ -1107,6 +1107,19 @@ def forward_triton_grouped_gemm(
 
     use_separated_lora = _should_use_separated_lora()
 
+    # Prepare gate_up LoRA data (mirrors the down block below).
+    # Attribute is populated by the patched ParamWrapper forward.
+    gate_up_lora = None
+    if getattr(self, "_unsloth_lora_gate_up_proj", None) is not None:
+        gate_up_lora = self._unsloth_lora_gate_up_proj[:3]
+    elif (
+        use_separated_lora
+        and hasattr(self, "gate_up_proj")
+        and _has_lora_adapters(self.gate_up_proj)
+    ):
+        gate_up_lora = _extract_lora_weights(
+            self.gate_up_proj, num_experts=self.num_experts
+        )
 
     # Handle 3D inputs (batch_size, seq_len, hidden_dim)
     is_3d = hidden_states.dim() == 3
@@ -1185,6 +1198,26 @@ def forward_triton_grouped_gemm(
         is_first_gemm=True,
     )
 
+    # Add separated LoRA contribution for gate_up.
+    # grouped_gemm above ran with permute_x=True (internal gather); first_gemm_output
+    # is in expert-sorted order. _apply_lora_grouped_mm expects pre-permuted input,
+    # so gather hidden_states using gather_indices // top_k (maps expert-sorted row
+    # back to its originating token row).
+    if gate_up_lora is not None:
+        first_weight, second_weight, scaling = gate_up_lora
+        first_weight = first_weight.to(hidden_states.dtype)
+        second_weight = second_weight.to(hidden_states.dtype)
+        permuted_hidden = hidden_states[gather_indices // top_k]
+        gate_up_lora_delta = _apply_lora_grouped_mm(
+            permuted_hidden,
+            first_weight,
+            second_weight,
+            offsets,
+            scaling,
+            grouped_mm_func=native_moe_grouped_mm,
+        )
+        first_gemm_output = first_gemm_output + gate_up_lora_delta
+
     # Apply activation and multiply gate with up
     if hasattr(self, 'act_fn') and callable(self.act_fn):
         gate, up = first_gemm_output.chunk(2, dim=-1)