diff --git a/.github/workflows/pr-test-npu.yml b/.github/workflows/pr-test-npu.yml
index 87b418cc..5be7fd2c 100644
--- a/.github/workflows/pr-test-npu.yml
+++ b/.github/workflows/pr-test-npu.yml
@@ -66,7 +66,7 @@ jobs:
         run: |
           python3 $GITHUB_WORKSPACE/tests/python/deepep/test_low_latency.py
 
-      - name: Run test deepep eplb
+      - name: Run test fused deep moe
         timeout-minutes: 10
         env:
           HCCL_BUFFSIZE: 2000
@@ -121,7 +121,7 @@ jobs:
         run: |
           python3 $GITHUB_WORKSPACE/tests/python/deepep/test_low_latency.py
 
-      - name: Run test deepep eplb
+      - name: Run test fused deep moe
         timeout-minutes: 10
         env:
           HCCL_BUFFSIZE: 2000
diff --git a/csrc/deepep/deep_ep.cpp b/csrc/deepep/deep_ep.cpp
index 02966361..213adbc7 100644
--- a/csrc/deepep/deep_ep.cpp
+++ b/csrc/deepep/deep_ep.cpp
@@ -608,46 +608,6 @@ std::vector<at::Tensor> Buffer::fused_deep_moe(const at::Tensor &x, const at::Te
     EP_HOST_ASSERT(expert_scales_optional.dim() == 2);
 
     this->is_padding = false;
-    at::Tensor new_x = x;
-    this->new_topk_idx = expert_ids;
-    at::Tensor new_scales = expert_scales_optional;
-
-    if (expert_ids.size(0) < PADDING_SIZE) {
-        this->is_padding = true;
-        this->padding_cnt = PADDING_SIZE - expert_ids.size(0);
-
-        std::vector<at::Tensor> x_blocks;
-        std::vector<at::Tensor> idx_blocks;
-
-        if (expert_ids.size(0) != 0) {
-            x_blocks.emplace_back(x);
-            idx_blocks.emplace_back(expert_ids);
-        } else {
-            this->ori_x = x.clone();  // store the original input when the batch is completely empty
-        }
-
-        int topk = static_cast<int>(expert_ids.size(1));
-        for (int i = 0; i < this->padding_cnt; i++) {
-            at::Tensor tmp_x = torch::ones({1, x.size(1)}, x.options());
-            at::Tensor tmp_idx =
-                torch::randperm(num_experts, expert_ids.options()).slice(0, 0, topk).reshape({1, topk});
-            x_blocks.emplace_back(tmp_x);
-            idx_blocks.emplace_back(tmp_idx);
-        }
-        new_x = torch::cat(x_blocks, 0);
-        this->new_topk_idx = torch::cat(idx_blocks, 0);
-
-        // padding expert_scales_optional
-        std::vector<at::Tensor> scales_blocks;
-        if (this->padding_cnt != PADDING_SIZE) {
-            scales_blocks.emplace_back(expert_scales_optional);
-        }
-        for (int i = 0; i < this->padding_cnt; i++) {
-            at::Tensor tmp_scales = torch::zeros({1, expert_scales_optional.size(1)}, expert_scales_optional.options());
-            scales_blocks.emplace_back(tmp_scales);
-        }
-        new_scales = torch::cat(scales_blocks, 0);
-    }
 
     char hcom_ep_name[128];
     if (!moe_all_to_all_group_name.empty()) {
@@ -656,11 +616,10 @@ std::vector<at::Tensor> Buffer::fused_deep_moe(const at::Tensor &x, const at::Te
         HCCL_CHECK(HcclGetCommName(ep_comm, hcom_ep_name));
     }
 
-    int64_t global_bs = std::max(new_topk_idx.size(0), num_max_dispatch_tokens_per_rank) * num_ranks;
-
+    int64_t global_bs = std::max(expert_ids.size(0), num_max_dispatch_tokens_per_rank) * num_ranks;
     auto x_shape = x.sizes();
     int h = x_shape[1];
-    int bs = this->new_topk_idx.size(0);
+    int bs = expert_ids.size(0);
 
     at::Tensor output = at::empty({bs, h}, x.options());
 
@@ -670,24 +629,14 @@ std::vector<at::Tensor> Buffer::fused_deep_moe(const at::Tensor &x, const at::Te
 
     EXEC_NPU_CMD(aclnnFusedDeepMoe,
                  // input
-                 new_x, this->new_topk_idx, gmm1_permuted_weight, gmm1_permuted_weight_scale, gmm2_weight,
-                 gmm2_weight_scale, static_cast<const std::nullptr_t &>(nullptr), new_scales,
+                 x, expert_ids, gmm1_permuted_weight, gmm1_permuted_weight_scale, gmm2_weight, gmm2_weight_scale,
+                 static_cast<const std::nullptr_t &>(nullptr), expert_scales_optional,
                  // attr
                  hcom_ep_name, num_ranks, rank, num_experts, shared_expert_num, shared_expert_rank_num, quant_mode,
                  global_bs,
                  // output
                  output, ep_recv_count);
 
-    // ---------- unpadding ----------
-    if (this->is_padding) {
-        if (expert_ids.size(0) == 0) {
-            output = this->ori_x;
-        } else {
-            output = output.slice(0, 0, PADDING_SIZE - this->padding_cnt);
-        }
-        this->is_padding = false;
-    }
-
     return {output, ep_recv_count};
 }
 }  // namespace deep_ep