diff --git a/unsloth/models/glm4_moe.py b/unsloth/models/glm4_moe.py
index 3abcb0947e..5d04b2f1d0 100644
--- a/unsloth/models/glm4_moe.py
+++ b/unsloth/models/glm4_moe.py
@@ -123,15 +123,6 @@ except ImportError:
 torch_nn_functional_silu = torch.nn.functional.silu
 
 
-def _glm4_supports_grouped_gemm_fp8_safe(experts_module) -> bool:
-    """
-    GLM grouped GEMM expects high-precision expert weights.
-    FP8 routed expert weights must use the existing naive fallback path.
-    """
-    gate_up_proj = getattr(experts_module, "gate_up_proj", None)
-    return gate_up_proj is not None and gate_up_proj.dtype != torch.float8_e4m3fn
-
-
 def Glm4MoeLiteMoE_fast_forward(self, hidden_states):
     """
     Optimized MoE forward pass using grouped GEMM.
@@ -164,7 +155,7 @@ def Glm4MoeLiteMoE_fast_forward(self, hidden_states):
         )
 
     # Use grouped GEMM for expert computation
-    if HAS_GROUPED_GEMM and _glm4_supports_grouped_gemm_fp8_safe(self.experts):
+    if HAS_GROUPED_GEMM:
         # Cast hidden_states to match expert weights dtype
         # Under autocast, hidden_states may be fp32 while weights are bf16
         hidden_states = hidden_states.to(self.experts.gate_up_proj.dtype)
@@ -238,7 +229,7 @@ def Glm4MoeLiteNaiveMoe_fast_forward(
     # Cast routing weights to match hidden_states dtype (Qwen3 pattern)
     top_k_weights = top_k_weights.to(hidden_states.dtype)
 
-    if not HAS_GROUPED_GEMM or not _glm4_supports_grouped_gemm_fp8_safe(self):
+    if not HAS_GROUPED_GEMM:
         # Fallback to original naive implementation
         final_hidden_states = torch.zeros_like(hidden_states)
         with torch.no_grad():
diff --git a/unsloth/models/loader.py b/unsloth/models/loader.py
index c87bd60ac7..29bf95308d 100644
--- a/unsloth/models/loader.py
+++ b/unsloth/models/loader.py
@@ -66,8 +66,8 @@ from ..device_type import (
 from unsloth_zoo.utils import Version, _get_dtype
 from unsloth_zoo.hf_utils import dtype_from_config
 from unsloth_zoo.tiled_mlp import patch_tiled_mlp
-from unsloth_zoo.temporary_patches.glm4_moe import (
-    maybe_patch_glm4_moe_expert_fp8_scales,
+from unsloth_zoo.temporary_patches.moe_utils_fp8 import (
+    maybe_patch_stacked_moe_expert_fp8_scales,
 )
 
 transformers_version = Version(transformers_version)
@@ -810,7 +810,7 @@ class FastLanguageModel(FastLlamaModel):
 
         if load_in_fp8 != False:
             _tag_model_with_fp8_torchao_config(model, fp8_mode)
-        maybe_patch_glm4_moe_expert_fp8_scales(
+        maybe_patch_stacked_moe_expert_fp8_scales(
             model,
             model_name = model_name,
             token = token,
@@ -1553,7 +1553,7 @@ class FastModel(FastBaseModel):
 
         if load_in_fp8 != False:
             _tag_model_with_fp8_torchao_config(model, fp8_mode)
-        maybe_patch_glm4_moe_expert_fp8_scales(
+        maybe_patch_stacked_moe_expert_fp8_scales(
             model,
             model_name = model_name,
             token = token,