diff --git a/fastdeploy/envs.py b/fastdeploy/envs.py
index c9869943d1d..902fa20f81e 100644
--- a/fastdeploy/envs.py
+++ b/fastdeploy/envs.py
@@ -212,17 +212,6 @@ def _validate_split_kv_size(value: int) -> int:
     "FD_XPU_MOE_FFN_QUANT_TYPE_MAP": lambda: os.getenv("FD_XPU_MOE_FFN_QUANT_TYPE_MAP", ""),
     # Whether to enable low latency in mixed scenario
     "FD_XPU_ENABLE_MIXED_EP_MODE": lambda: bool(int(os.getenv("FD_XPU_ENABLE_MIXED_EP_MODE", "0"))),
-    # Whether to use phi FP8 quantization,if 1,use paddle default.
-    "FD_USE_PHI_FP8_QUANT": lambda: bool(int(os.getenv("FD_USE_PHI_FP8_QUANT", "1"))),
-    # Enables the Paddle/phi combined TopK operator only when topk_method == noaux_tc,
-    # intended for training alignment. Defaults to 0 (disabled).
-    "FD_USE_PHI_MOE_TOPK": lambda: bool(int(os.getenv("FD_USE_PHI_MOE_TOPK", "0"))),
-    # Whether to use phi MOE permute,if 1,use paddle op.
-    "FD_USE_PHI_MOE_PERMUTE": lambda: bool(int(os.getenv("FD_USE_PHI_MOE_PERMUTE", "0"))),
-    # Whether to use phi rms_norm,if 1,use paddle op.
-    "FD_USE_PHI_RMSNORM": lambda: bool(int(os.getenv("FD_USE_PHI_RMSNORM", "0"))),
-    # Control class SiluAndMul to use swiglu or fusid_bias_act operator in the forward_cuda function
-    "FD_SiluAndMul_USE_PHI_SWIGLU": lambda: bool(int(os.getenv("FD_SiluAndMul_USE_PHI_SWIGLU", "0"))),
     # Reserve output blocks for decoding requests when schedule new prefill requests
     "FD_RESERVE_OUTPUT_BLOCK_NUM_FOR_DECODE_WHEN_SCHEDULE_NEW_PREFILL": lambda: int(
         os.getenv("FD_RESERVE_OUTPUT_BLOCK_NUM_FOR_DECODE_WHEN_SCHEDULE_NEW_PREFILL", "16")
@@ -270,8 +259,22 @@ def _validate_split_kv_size(value: int) -> int:
     "FD_SAVE_OUTPUT_CACHE_FOR_PREEMPTED_REQUEST": lambda: bool(
         int(os.getenv("FD_SAVE_OUTPUT_CACHE_FOR_PREEMPTED_REQUEST", "1"))
     ),
+    # train-infer consistency, used in RL
     # Whether to align RoPE and moe gate precision with training
     "FD_ENABLE_RL": lambda: int(os.getenv("FD_ENABLE_RL", "0")),
+    # Whether to use phi FP8 quantization,if 1,use paddle default.
+    "FD_USE_PHI_FP8_QUANT": lambda: bool(int(os.getenv("FD_USE_PHI_FP8_QUANT", "1"))),
+    # Enables the Paddle/phi combined TopK operator only when topk_method == noaux_tc,
+    # intended for training alignment. Defaults to 0 (disabled).
+    "FD_USE_PHI_MOE_TOPK": lambda: bool(int(os.getenv("FD_USE_PHI_MOE_TOPK", "0"))),
+    # Whether to use phi MOE permute,if 1,use paddle op.
+    "FD_USE_PHI_MOE_PERMUTE": lambda: bool(int(os.getenv("FD_USE_PHI_MOE_PERMUTE", "0"))),
+    # Whether to use phi rms_norm,if 1,use paddle op.
+    "FD_USE_PHI_RMSNORM": lambda: bool(int(os.getenv("FD_USE_PHI_RMSNORM", "0"))),
+    # Control class SiluAndMul to use swiglu or fusid_bias_act operator in the forward_cuda function
+    "FD_SiluAndMul_USE_PHI_SWIGLU": lambda: bool(int(os.getenv("FD_SiluAndMul_USE_PHI_SWIGLU", "0"))),
+    # Whether to enable FP8 quantization with pow2scale.
+    "FD_FP8_QUANT_WITH_POW2SCALE": lambda: bool(int(os.getenv("FD_FP8_QUANT_WITH_POW2SCALE", "0"))),
 }
 
 
diff --git a/fastdeploy/model_executor/layers/moe/fused_moe_deepgemm_backend.py b/fastdeploy/model_executor/layers/moe/fused_moe_deepgemm_backend.py
index 53247e29126..a16e5ccbe9c 100644
--- a/fastdeploy/model_executor/layers/moe/fused_moe_deepgemm_backend.py
+++ b/fastdeploy/model_executor/layers/moe/fused_moe_deepgemm_backend.py
@@ -188,7 +188,7 @@ def m_grouped_fp8_gemm_nt_contiguous_custom_python_op(
         else:
             ffn_in_x, ffn_in_x_scale_tensor = paddle.incubate.nn.functional.fp8_quant_blockwise(
                 ffn_out,
-                using_pow2_scale=not disable_ue8m0_cast,
+                using_pow2_scale=not disable_ue8m0_cast or fastdeploy.envs.FD_FP8_QUANT_WITH_POW2SCALE,
                 using_ue8m0_scale=not disable_ue8m0_cast,
             )
             ffn_in_x_scale_tensor = ffn_in_x_scale_tensor.T[: ffn_in_x.shape[0]]
@@ -355,7 +355,7 @@ def apply_ep_prefill(
         else:
             x_fp8, x_scale_tensor = paddle.incubate.nn.functional.fp8_quant_blockwise(
                 x,
-                using_pow2_scale=self.quant_config.deepgemm_scale_ue8m0,
+                using_pow2_scale=self.quant_config.deepgemm_scale_ue8m0 or fastdeploy.envs.FD_FP8_QUANT_WITH_POW2SCALE,
                 output_scale_transpose=self.quant_config.deepgemm_scale_ue8m0,
                 using_ue8m0_scale=self.quant_config.deepgemm_scale_ue8m0,
             )
@@ -581,7 +581,8 @@ def apply_ep_prefill(
                 else:
                     ffn_in_x, ffn_in_x_scale_tensor = paddle.incubate.nn.functional.fp8_quant_blockwise(
                         ffn_out,
-                        using_pow2_scale=self.quant_config.deepgemm_scale_ue8m0,
+                        using_pow2_scale=self.quant_config.deepgemm_scale_ue8m0
+                        or fastdeploy.envs.FD_FP8_QUANT_WITH_POW2SCALE,
                         using_ue8m0_scale=self.quant_config.deepgemm_scale_ue8m0,
                     )
                     ffn_in_x_scale_tensor = ffn_in_x_scale_tensor.T[: ffn_in_x.shape[0]]
@@ -773,7 +774,7 @@ def apply_tp(
         else:
             recv_x, recv_x_scale = paddle.incubate.nn.functional.fp8_quant_blockwise(
                 x,
-                using_pow2_scale=self.quant_config.deepgemm_scale_ue8m0,
+                using_pow2_scale=self.quant_config.deepgemm_scale_ue8m0 or fastdeploy.envs.FD_FP8_QUANT_WITH_POW2SCALE,
                 output_scale_transpose=self.quant_config.deepgemm_scale_ue8m0,
                 using_ue8m0_scale=self.quant_config.deepgemm_scale_ue8m0,
             )
diff --git a/fastdeploy/model_executor/layers/moe/fused_moe_triton_backend.py b/fastdeploy/model_executor/layers/moe/fused_moe_triton_backend.py
index 010b61e0496..56f6e6dd42c 100644
--- a/fastdeploy/model_executor/layers/moe/fused_moe_triton_backend.py
+++ b/fastdeploy/model_executor/layers/moe/fused_moe_triton_backend.py
@@ -1248,7 +1248,7 @@ def python_op_fused_moe_kernel_paddle(
         x_q, x_scale = fastdeploy.model_executor.ops.gpu.per_token_quant(x, quant_config.weight_block_size[0], False)
     else:
         x_q, x_scale = paddle.incubate.nn.functional.fp8_quant_blockwise(
-            x, using_pow2_scale=False, output_scale_transpose=False
+            x, using_pow2_scale=fastdeploy.envs.FD_FP8_QUANT_WITH_POW2SCALE, output_scale_transpose=False
         )
         x_scale = x_scale[: x.shape[0]]
 
@@ -1306,7 +1306,9 @@ def python_op_fused_moe_kernel_paddle(
         )
     else:
         x_q, x_scale = paddle.incubate.nn.functional.fp8_quant_blockwise(
-            intermediate_cache2, using_pow2_scale=False, output_scale_transpose=False
+            intermediate_cache2,
+            using_pow2_scale=fastdeploy.envs.FD_FP8_QUANT_WITH_POW2SCALE,
+            output_scale_transpose=False,
         )
         x_scale = x_scale[: x_q.shape[0]]
 
diff --git a/fastdeploy/model_executor/layers/quantization/block_wise_fp8.py b/fastdeploy/model_executor/layers/quantization/block_wise_fp8.py
index b277efbd73d..709a5e69613 100644
--- a/fastdeploy/model_executor/layers/quantization/block_wise_fp8.py
+++ b/fastdeploy/model_executor/layers/quantization/block_wise_fp8.py
@@ -353,7 +353,7 @@ def apply(self, layer, x):
         else:
             x, x_scale_tensor = paddle.incubate.nn.functional.fp8_quant_blockwise(
                 x,
-                using_pow2_scale=self.quant_config.deepgemm_scale_ue8m0,
+                using_pow2_scale=self.quant_config.deepgemm_scale_ue8m0 or fastdeploy.envs.FD_FP8_QUANT_WITH_POW2SCALE,
                 output_scale_transpose=True,
                 using_ue8m0_scale=self.quant_config.deepgemm_scale_ue8m0,
             )