diff --git a/backends/gaudi/server/text_generation_server/layers/moe/unquantized.py b/backends/gaudi/server/text_generation_server/layers/moe/unquantized.py
index ec158398..58709ec3 100644
--- a/backends/gaudi/server/text_generation_server/layers/moe/unquantized.py
+++ b/backends/gaudi/server/text_generation_server/layers/moe/unquantized.py
@@ -4,7 +4,9 @@ import torch
 import torch.nn as nn
 
 from text_generation_server.utils.weights import UnquantizedWeight, Weights
-from vllm_hpu_extension.ops import DynamicFusedMOE
+from vllm_hpu_extension.ops import VllmMixtureOfExpertsOp
+import habana_frameworks.torch as htorch
+import torch.nn.functional as F
 
 
 class UnquantizedSparseMoELayer(nn.Module):
@@ -53,13 +55,29 @@ class UnquantizedSparseMoELayer(nn.Module):
             weights=weights,
         )
 
-        self.hpu_fused_moe = DynamicFusedMOE(n_experts)
+        self.MoeOp = VllmMixtureOfExpertsOp(n_experts, 0, n_experts - 1)
         for i in range(n_experts):
-            self.hpu_fused_moe.MoeOp.w13_list[i].set_weight(self.gate_up_proj[i])
-            self.hpu_fused_moe.MoeOp.w2_list[i].set_weight(self.down_proj[i])
+            self.MoeOp.w13_list[i].set_weight(self.gate_up_proj[i])
+            self.MoeOp.w2_list[i].set_weight(self.down_proj[i])
 
     def forward(self, x: torch.Tensor, *, gating_output: torch.Tensor) -> torch.Tensor:
-        return self.hpu_fused_moe(x, gating_output, self.topk)
+        htorch.core.mark_step()
+        routing_weights = F.softmax(gating_output, dim=1, dtype=torch.float32)
+        routing_weights, selected_experts = torch.topk(
+            routing_weights, self.topk, dim=-1
+        )
+        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+        routing_weights = routing_weights.to(x.dtype)
+
+        final_hidden_states = self.MoeOp(
+            hidden_states=x,
+            expert_routing_table=selected_experts,
+            router_weights=routing_weights,
+            permuted_weights=True,
+            activation="silu",
+        )
+
+        return final_hidden_states.view(-1, x.shape[1])
 
 
 def _load_expert_multi_weights_col(