diff --git a/vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py b/vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py
index 43126195205..9ae82d4e73c 100644
--- a/vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py
+++ b/vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py
@@ -317,9 +317,9 @@ class TrtLlmFp8ExpertsMonolithic(TrtLlmFp8ExpertsBase, mk.FusedMoEExpertsMonolit
         from flashinfer.fused_moe import Fp8QuantizationType, WeightLayout
 
         assert not apply_router_weight_on_input
-        assert activation == MoEActivation.SILU
+        assert activation in [MoEActivation.SILU, MoEActivation.RELU2_NO_MUL]
+        activation_type = activation_to_flashinfer_int(activation)
         assert self.topk <= global_num_experts
-        assert self.topk <= 10
         assert global_num_experts % 4 == 0
         assert self.quant_config.block_shape in [[128, 128], [1, 32]]
         # Kernel expects #experts <= #threads 512
@@ -333,13 +333,19 @@ class TrtLlmFp8ExpertsMonolithic(TrtLlmFp8ExpertsBase, mk.FusedMoEExpertsMonolit
             use_shuffled_weight = True
             weight_layout = WeightLayout.MajorK
             hidden_states_scale = a1q_scale
+            # FlashInfer expects None for non-grouped MXFP8 routing configs.
+            n_group = num_expert_group or None
+            selected_topk_group = topk_group or None
         else:
+            assert self.topk <= 10
             fp8_quant_type = Fp8QuantizationType.DeepSeekFp8
             use_shuffled_weight = True
             weight_layout = WeightLayout.BlockMajorK
             hidden_states_scale = a1q_scale.t().contiguous()
+            n_group = num_expert_group or 0
+            selected_topk_group = topk_group or 0
 
-        return flashinfer.fused_moe.trtllm_fp8_block_scale_moe(
+        kwargs = dict(
             routing_logits=router_logits,
             routing_bias=e_score_correction_bias,
             hidden_states=hidden_states,
@@ -350,8 +356,8 @@ class TrtLlmFp8ExpertsMonolithic(TrtLlmFp8ExpertsBase, mk.FusedMoEExpertsMonolit
             gemm2_weights_scale=self.quant_config.w2_scale,
             num_experts=global_num_experts,
             top_k=self.topk,
-            n_group=(num_expert_group or 0),
-            topk_group=(topk_group or 0),
+            n_group=n_group,
+            topk_group=selected_topk_group,
             intermediate_size=self.intermediate_size_per_partition,
             local_expert_offset=self.ep_rank * self.local_num_experts,
             local_num_experts=self.local_num_experts,
@@ -361,6 +367,9 @@ class TrtLlmFp8ExpertsMonolithic(TrtLlmFp8ExpertsBase, mk.FusedMoEExpertsMonolit
             weight_layout=weight_layout,
             fp8_quantization_type=fp8_quant_type,
         )
+        if is_mxfp8 or activation == MoEActivation.RELU2_NO_MUL:
+            kwargs["activation_type"] = activation_type
+        return flashinfer.fused_moe.trtllm_fp8_block_scale_moe(**kwargs)
 
     def _apply_per_tensor(
         self,