diff --git a/vllm_gaudi/ops/hpu_fp8.py b/vllm_gaudi/ops/hpu_fp8.py
index c30cb7012..1c72f3b41 100644
--- a/vllm_gaudi/ops/hpu_fp8.py
+++ b/vllm_gaudi/ops/hpu_fp8.py
@@ -160,10 +160,14 @@ def apply(
             topk_weights = topk_weights.to(x.dtype)
         topk_ids = topk_ids.view(*x.shape[:-1], -1)
         topk_weights = topk_weights.view(*x.shape[:-1], -1)
+        if not layer.use_grouped_topk:
+            topk_ids = topk_ids.to(torch.int64)
+            topk_weights = topk_weights.to(x.dtype)
+
         output = layer.moe_op(
             x,
-            topk_ids.to(torch.int64),
-            topk_weights.to(x.dtype),
+            topk_ids,
+            topk_weights,
             permuted_weights=True,
             activation=activation,
         )
diff --git a/vllm_gaudi/ops/hpu_fused_moe.py b/vllm_gaudi/ops/hpu_fused_moe.py
index 2f0a107f5..158af825e 100644
--- a/vllm_gaudi/ops/hpu_fused_moe.py
+++ b/vllm_gaudi/ops/hpu_fused_moe.py
@@ -2,6 +2,7 @@
 
 import torch
 import vllm
+from vllm.model_executor.layers.batch_invariant import vllm_is_batch_invariant
 from vllm.model_executor.layers.fused_moe.layer import (FusedMoE, UnquantizedFusedMoEMethod)
 from vllm_gaudi.extension.ops import (VllmMixtureOfExpertsOp)
 
@@ -63,11 +64,14 @@ def forward_oot(
             topk_weights = topk_weights.to(x.dtype)
         topk_ids = topk_ids.view(*x.shape[:-1], -1)
         topk_weights = topk_weights.view(*x.shape[:-1], -1)
+        if not layer.use_grouped_topk:
+            topk_ids = topk_ids.to(torch.int64)
+            topk_weights = topk_weights.to(x.dtype)
 
         return layer.moe_op(
             x,
-            topk_ids.to(torch.int64),
-            topk_weights.to(x.dtype),
+            topk_ids,
+            topk_weights,
             permuted_weights=True,
             activation=activation,
         ).view(*input_shape)
@@ -153,7 +157,80 @@ def get_compressed_expert_map(expert_map: torch.Tensor) -> str:
     return ", ".join(mappings)
 
 
+def patched_grouped_topk(
+    hidden_states: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    num_expert_group: int = 0,
+    topk_group: int = 0,
+    scoring_func: str = "softmax",
+    routed_scaling_factor: float = 1.0,
+    e_score_correction_bias: torch.Tensor | None = None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+
+    gating_output = gating_output.float()
+    if e_score_correction_bias is not None:
+        e_score_correction_bias = e_score_correction_bias.float()
+
+    if scoring_func == "softmax":
+        scores = torch.softmax(gating_output, dim=-1)
+    elif scoring_func == "sigmoid":
+        scores = gating_output.sigmoid()
+    else:
+        raise ValueError(f"Unsupported scoring function: {scoring_func}")
+
+    # For batch invariance, use sorted=True to ensure deterministic expert selection
+    use_sorted = vllm_is_batch_invariant()
+
+    num_token = scores.size(0)
+    if e_score_correction_bias is not None:
+        # Store original scores before applying correction bias. We use biased
+        # scores for expert selection but original scores for routing weights
+        original_scores = scores
+        scores = scores + e_score_correction_bias.unsqueeze(0)
+        scores_tmp = scores.clone().reshape(num_token, num_expert_group, -1)
+        top1_val, top1_idx = torch.max(scores_tmp, dim=-1)
+        scores_tmp.scatter_(-1, top1_idx.unsqueeze(-1), torch.finfo(scores.dtype).min)
+        group_scores, top2_idx = torch.max(scores_tmp, dim=-1)
+        group_scores.add_(top1_val)
+    else:
+        group_scores = (scores.view(num_token, num_expert_group, -1).max(dim=-1).values)  # [n, n_group]
+
+    if num_token > 1024:
+        group_mask = torch.zeros_like(group_scores)
+        for i in range(topk_group):
+            _, group_idx = torch.max(group_scores, dim=-1)
+            group_mask.scatter_(1, group_idx.unsqueeze(-1), 1)
+            if i < topk_group - 1:
+                group_scores.scatter_(1, group_idx.unsqueeze(-1), torch.finfo(scores.dtype).min)
+    else:
+        group_idx = torch.topk(group_scores, k=topk_group, dim=-1, sorted=use_sorted)[1]  # [n, top_k_group]
+        group_mask = torch.zeros_like(group_scores)  # [n, n_group]
+        group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
+
+    tmp_scores = scores.reshape(num_token, num_expert_group, -1) + (
+        (1 - group_mask) * torch.finfo(scores.dtype).min).unsqueeze(-1)
+    tmp_scores = tmp_scores.reshape(num_token, -1)
+
+    if e_score_correction_bias is not None:
+        topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=use_sorted)[1]
+        # Use original unbiased scores for the routing weights
+        topk_weights = original_scores.gather(1, topk_ids)
+    else:
+        topk_weights, topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=use_sorted)
+
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+
+    if routed_scaling_factor != 1.0:
+        topk_weights = topk_weights * routed_scaling_factor
+    return topk_weights.to(hidden_states.dtype), topk_ids.to(torch.int64)
+
+
 # Apply patches
 FusedMoE.forward = patched_fused_moe_forward
 vllm.model_executor.layers.fused_moe.layer.get_compressed_expert_map = \
     get_compressed_expert_map
+vllm.model_executor.layers.fused_moe.layer.grouped_topk = \
+    patched_grouped_topk