diff --git a/vllm/model_executor/layers/fused_moe/layer.py b/vllm/model_executor/layers/fused_moe/layer.py
index 1fd8f21758..c6c908f73a 100644
--- a/vllm/model_executor/layers/fused_moe/layer.py
+++ b/vllm/model_executor/layers/fused_moe/layer.py
@@ -45,7 +45,8 @@ if current_platform.is_cuda_alike():
         from .pplx_prepare_finalize import PplxPrepareAndFinalize
     if has_deepep:
         from .deepep_ht_prepare_finalize import DeepEPHTPrepareAndFinalize
-        from .deepep_ll_prepare_finalize import DeepEPLLPrepareAndFinalize
+        from .deepep_ll_prepare_finalize import (DEEPEP_QUANT_BLOCK_SIZE,
+                                                 DeepEPLLPrepareAndFinalize)
 else:
     fused_experts = None  # type: ignore
     FusedMoEPermuteExpertsUnpermute = None  # type: ignore
@@ -377,6 +378,12 @@ class FusedMoEMethodBase(QuantizeMethodBase):
                 all2all_manager.world_size)
             handle = all2all_manager.get_handle(all_to_all_args)
 
+            # Note : We may want to use FP8 dispatch even otherwise just to
+            # reduce datamovement
+            use_fp8_dispatch = (quant_dtype == current_platform.fp8_dtype()
+                                and act_quant_block_size
+                                == DEEPEP_QUANT_BLOCK_SIZE)
+
             # Note (varun): Whether to use FP8 dispatch or not needs some
             # profiling. Turning it off for now.
             prepare_finalize = DeepEPLLPrepareAndFinalize(
@@ -386,7 +393,7 @@ class FusedMoEMethodBase(QuantizeMethodBase):
                 max_tokens_per_rank=moe.max_num_tokens,
                 quant_dtype=quant_dtype,
                 block_shape=act_quant_block_size,
-                use_fp8_dispatch=False,
+                use_fp8_dispatch=use_fp8_dispatch,
             )
 
         self.topk_indices_dtype = None