diff --git a/python/sglang/srt/layers/quantization/fp8.py b/python/sglang/srt/layers/quantization/fp8.py
index 029cc9eedde9..e11f208455b1 100644
--- a/python/sglang/srt/layers/quantization/fp8.py
+++ b/python/sglang/srt/layers/quantization/fp8.py
@@ -932,52 +932,45 @@ def process_weights_after_loading_block_quant(self, layer: Module) -> None:
             will_use_deepgemm = self.is_deepgemm_moe_runner_backend_enabled()
 
             if self.is_fp4_expert:
-                if get_moe_runner_backend().is_marlin():
-                    layer.w13_weight.data = layer.w13_weight.data.view(torch.int8)
-                    layer.w2_weight.data = layer.w2_weight.data.view(torch.int8)
-                elif not get_moe_runner_backend().is_flashinfer_mxfp4():
-                    raise NotImplementedError(
-                        "DeepSeekV4 FP4 experts now require a native FP4 MoE backend. "
-                        "Use `--moe-runner-backend marlin` on Hopper or "
-                        "`--moe-runner-backend flashinfer_mxfp4` when available."
+                # FP4 experts support three MoE backends:
+                # - marlin (Hopper w4a16): only needs int8 view
+                # - flashinfer_mxfp4: only needs int8 view
+                # - deepgemm/auto (Blackwell): int8 view + mega_moe or scale conversion
+                layer.w13_weight.data = layer.w13_weight.data.view(torch.int8)
+                layer.w2_weight.data = layer.w2_weight.data.view(torch.int8)
+
+                if envs.SGLANG_OPT_USE_DEEPGEMM_MEGA_MOE.get():
+                    from sglang.srt.models.deepseek_v4 import (
+                        build_mega_moe_experts_weights,
                     )
 
-                else:
-                    layer.w13_weight.data = layer.w13_weight.data.view(torch.int8)
-                    layer.w2_weight.data = layer.w2_weight.data.view(torch.int8)
+                    build_mega_moe_experts_weights(layer)
+                    return
 
-                    if envs.SGLANG_OPT_USE_DEEPGEMM_MEGA_MOE.get():
-                        from sglang.srt.models.deepseek_v4 import (
-                            build_mega_moe_experts_weights,
+                if (
+                    envs.SGLANG_OPT_DEEPGEMM_SCALE_CONVERT_AT_INIT.get()
+                    and envs.SGLANG_DSV4_MODE.get() == "2604"
+                    and deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0
+                    and will_use_deepgemm
+                ):
+                    from deep_gemm import transform_sf_into_required_layout
+
+                    for scale_param, weight_param in [
+                        (layer.w13_weight_scale_inv, layer.w13_weight),
+                        (layer.w2_weight_scale_inv, layer.w2_weight),
+                    ]:
+                        num_experts, n, _ = scale_param.data.shape
+                        k = weight_param.shape[2] * 2
+                        scale_param.data = transform_sf_into_required_layout(
+                            scale_param.data,
+                            mn=n,
+                            k=k,
+                            recipe=(1, 32),
+                            num_groups=num_experts,
+                            disable_ue8m0_cast=False,
                         )
-
-                        build_mega_moe_experts_weights(layer)
-                        return
-
-                    if (
-                        envs.SGLANG_OPT_DEEPGEMM_SCALE_CONVERT_AT_INIT.get()
-                        and envs.SGLANG_DSV4_MODE.get() == "2604"
-                        and deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0
-                        and will_use_deepgemm
-                    ):
-                        from deep_gemm import transform_sf_into_required_layout
-
-                        for scale_param, weight_param in [
-                            (layer.w13_weight_scale_inv, layer.w13_weight),
-                            (layer.w2_weight_scale_inv, layer.w2_weight),
-                        ]:
-                            num_experts, n, _ = scale_param.data.shape
-                            k = weight_param.shape[2] * 2
-                            scale_param.data = transform_sf_into_required_layout(
-                                scale_param.data,
-                                mn=n,
-                                k=k,
-                                recipe=(1, 32),
-                                num_groups=num_experts,
-                                disable_ue8m0_cast=False,
-                            )
-                        layer.w13_weight_scale_inv.format_ue8m0 = True
-                        layer.w2_weight_scale_inv.format_ue8m0 = True
+                    layer.w13_weight_scale_inv.format_ue8m0 = True
+                    layer.w2_weight_scale_inv.format_ue8m0 = True
 
             if (
                 not self.is_fp4_expert