diff --git a/tensorrt_llm/_torch/models/modeling_deepseekv3.py b/tensorrt_llm/_torch/models/modeling_deepseekv3.py
index e64f0990d4..39699e0059 100644
--- a/tensorrt_llm/_torch/models/modeling_deepseekv3.py
+++ b/tensorrt_llm/_torch/models/modeling_deepseekv3.py
@@ -457,14 +457,14 @@ class Deepseekv3MoE(nn.Module):
     def compute_routed_output(self, hidden_states, hidden_states_fp4,
                               all_rank_num_tokens, cutlass_min_latency_mode):
         # max-throughput
-        if self.use_dp and self.mapping.tp_size > 1 and not self.enable_alltoall:
+        if self.use_dp and self.mapping.tp_size > 1:
             max_num_token = max(all_rank_num_tokens)
             hidden_states = torch.nn.functional.pad(
                 hidden_states,
                 (0, 0, 0, max_num_token - hidden_states.shape[0]))
             # FP4 all_gather moves this bf16 allgather in to after topk and fp4 quantization
             # to reduce allreduce BW
-            if disable_fp4_allgather():
+            if disable_fp4_allgather() and not self.enable_alltoall:
                 hidden_states = allgather(hidden_states,
                                           self.mapping,
                                           gather_dim=0)
diff --git a/tensorrt_llm/_torch/modules/fused_moe.py b/tensorrt_llm/_torch/modules/fused_moe.py
index 0a49df75a7..7007f471ac 100755
--- a/tensorrt_llm/_torch/modules/fused_moe.py
+++ b/tensorrt_llm/_torch/modules/fused_moe.py
@@ -903,14 +903,13 @@ class FusedMoE(nn.Module):
             if self.use_dp and self.enable_alltoall:
                 all_rank_chunk_size_list = []
                 for single_rank_num_tokens in all_rank_num_tokens:
-                    single_rank_num_chunks = (single_rank_num_tokens +
-                                              max_chunk_size -
-                                              1) // max_chunk_size
-                    assert single_rank_num_chunks == num_chunks,\
-                        "num_chunks should be the same for attention dp and ep"
-                    all_rank_chunk_size_list.append(
-                        split_chunk(single_rank_num_tokens,
-                                    single_rank_num_chunks))
+                    single_rank_num_chunks = num_chunks
+                    single_rank_chunk_size_list = split_chunk(
+                        single_rank_num_tokens, single_rank_num_chunks)
+                    single_rank_chunk_size_list = [
+                        1 if x == 0 else x for x in single_rank_chunk_size_list
+                    ]
+                    all_rank_chunk_size_list.append(single_rank_chunk_size_list)
 
                 for chunk_id in range(num_chunks):
                     chunk_all_rank_num_tokens = [