diff --git a/python/sglang/srt/models/deepseek_v2.py b/python/sglang/srt/models/deepseek_v2.py
index 0512fba87..352480f2d 100644
--- a/python/sglang/srt/models/deepseek_v2.py
+++ b/python/sglang/srt/models/deepseek_v2.py
@@ -1709,53 +1709,35 @@ class DeepseekV2ForCausalLM(nn.Module):
     def determine_num_fused_shared_experts(
         self, architecture: str = "DeepseekV3ForCausalLM"
     ):
-        self.num_fused_shared_experts = (
-            0
-            if global_server_args_dict["disable_shared_experts_fusion"]
-            else self.config.n_shared_experts
-        )
-        if self.num_fused_shared_experts > 0:
-            # Only Deepseek V3/R1 can use shared experts fusion optimization now.
-            if (
-                not _is_cuda
-                or self.config.architectures[0] != architecture
-                or self.config.n_routed_experts != 256
-            ):
-                self.num_fused_shared_experts = 0
-                global_server_args_dict["disable_shared_experts_fusion"] = True
-                log_info_on_rank0(
-                    logger,
-                    "Only Deepseek V3/R1 on NV-platform can use shared experts fusion optimization. Shared experts fusion optimization is disabled.",
-                )
-            elif (
-                global_server_args_dict["enable_deepep_moe"]
-                or global_server_args_dict["enable_ep_moe"]
-            ):
-                self.num_fused_shared_experts = 0
-                global_server_args_dict["disable_shared_experts_fusion"] = True
-                log_info_on_rank0(
-                    logger,
-                    "Deepseek V3/R1 can not use shared experts fusion optimization when in deepep_moe or ep_moe mode. Shared experts fusion optimization is disabled.",
-                )
-        elif self.num_fused_shared_experts == 0:
-            if (
-                _is_cuda
-                and torch.cuda.get_device_capability("cuda") >= (9, 0)
-                and self.config.architectures[0] == architecture
-                and self.config.n_routed_experts == 256
-                and (
-                    not (
-                        global_server_args_dict["enable_deepep_moe"]
-                        or global_server_args_dict["enable_ep_moe"]
-                    )
-                )
-            ):
-                self.num_fused_shared_experts = self.config.n_shared_experts
-                global_server_args_dict["disable_shared_experts_fusion"] = False
-                log_info_on_rank0(
-                    logger,
-                    "Deepseek V3/R1 with fp8/fp4 can use shared experts fusion optimization when SM version >=90. Shared experts fusion optimization is enabled.",
-                )
+        self.num_fused_shared_experts = 0
+        if global_server_args_dict["disable_shared_experts_fusion"]:
+            return
+
+        # Only Deepseek V3/R1 can use shared experts fusion optimization now.
+        disable_reason = None
+        if (
+            not _is_cuda
+            or torch.cuda.get_device_capability("cuda") < (9, 0)
+            or self.config.architectures[0] != architecture
+            or self.config.n_routed_experts != 256
+            or self.config.n_shared_experts != 1
+        ):
+            disable_reason = "Only Deepseek V3/R1 on NV-platform with capability >= 90 can use shared experts fusion optimization."
+        elif (
+            global_server_args_dict["enable_deepep_moe"]
+            or global_server_args_dict["enable_ep_moe"]
+        ):
+            disable_reason = "Deepseek V3/R1 can not use shared experts fusion optimization when in deepep_moe or ep_moe mode."
+
+        if disable_reason is not None:
+            global_server_args_dict["disable_shared_experts_fusion"] = True
+            log_info_on_rank0(
+                logger,
+                f"{disable_reason} Shared experts fusion optimization is disabled.",
+            )
+            return
+
+        self.num_fused_shared_experts = self.config.n_shared_experts
 
     def get_input_embeddings(self) -> nn.Embedding:
         return self.model.embed_tokens