diff --git a/kt-kernel/python/utils/amx.py b/kt-kernel/python/utils/amx.py
index 1dfd3b6..cb7fd82 100644
--- a/kt-kernel/python/utils/amx.py
+++ b/kt-kernel/python/utils/amx.py
@@ -448,6 +448,10 @@ class NativeMoEWrapper(BaseMoEWrapper):
                     self.down_scales = [t.to(torch.float32).contiguous() for t in weights["down_scale"]]
                 assert self.gate_scales[0].dtype == torch.float32, "Expected float32 scales for FP8"
             elif self.method == "FP8_PERCHANNEL":
+                if self.gate_scales[0].dtype != torch.float32:
+                    self.gate_scales = [t.to(torch.float32).contiguous() for t in weights["gate_scale"]]
+                    self.up_scales = [t.to(torch.float32).contiguous() for t in weights["up_scale"]]
+                    self.down_scales = [t.to(torch.float32).contiguous() for t in weights["down_scale"]]
                 assert self.gate_scales[0].dtype == torch.float32, "Expected float32 scales for FP8_PERCHANNEL"
 
         t2 = time.time()
diff --git a/kt-kernel/python/utils/loader.py b/kt-kernel/python/utils/loader.py
index 250a99e..ed3ce89 100644
--- a/kt-kernel/python/utils/loader.py
+++ b/kt-kernel/python/utils/loader.py
@@ -243,6 +243,7 @@ class FP8SafeTensorLoader(SafeTensorLoader):
     Supported formats:
     - DeepSeek style: {base}.mlp.experts.{id}.{gate,up,down}_proj.weight
     - Mixtral/MiniMax style: {base}.block_sparse_moe.experts.{id}.{w1,w3,w2}.weight
+    - Mistral style: {base}.experts.{id}.{w1,w3,w2}.weight
 
     Supported scale formats (auto-detected):
     - Block-wise: weight_scale_inv (DeepSeek FP8)
@@ -255,6 +256,7 @@ class FP8SafeTensorLoader(SafeTensorLoader):
     MOE_FORMATS = {
         "deepseek": ("{base}.mlp.experts", "gate_proj", "up_proj", "down_proj"),
         "mixtral": ("{base}.block_sparse_moe.experts", "w1", "w3", "w2"),
+        "mistral": ("{base}.experts", "w1", "w3", "w2"),
     }
 
     def __init__(self, file_path: str, scale_suffix: str = None):
@@ -297,6 +299,10 @@ class FP8SafeTensorLoader(SafeTensorLoader):
                         self._detected_format = fmt_name
                         print(f"[FP8SafeTensorLoader] Detected format: {fmt_name}")
                         break
+                    elif fmt_name == "mistral" and ".mlp.experts" not in key and ".block_sparse_moe.experts" not in key:
+                        self._detected_format = fmt_name
+                        print(f"[FP8SafeTensorLoader] Detected format: {fmt_name}")
+                        break
             if self._detected_format:
                 break
 
@@ -321,8 +327,21 @@ class FP8SafeTensorLoader(SafeTensorLoader):
                     return
                 elif f".{gate}.weight_scale" in key and "weight_scale_inv" not in key:
                     self._scale_suffix = "weight_scale"
-                    self._is_per_channel = True
-                    print("[FP8SafeTensorLoader] Detected scale format: per-channel (weight_scale)")
+                    # Some models (e.g., Mistral) use block-wise FP8 scales but keep
+                    # the key suffix as `weight_scale` (without `_inv`). Infer format
+                    # from scale tensor shape instead of suffix alone:
+                    # - per-channel: [N] or [N, 1]
+                    # - block-wise: [N_block, K_block] (both dims > 1)
+                    scale_tensor = self.load_tensor(key, device="cpu")
+                    if scale_tensor.dim() == 1:
+                        self._is_per_channel = True
+                    elif scale_tensor.dim() == 2 and scale_tensor.shape[1] == 1:
+                        self._is_per_channel = True
+                    else:
+                        self._is_per_channel = False
+
+                    scale_kind = "per-channel" if self._is_per_channel else "block-wise"
+                    print(f"[FP8SafeTensorLoader] Detected scale format: {scale_kind} (weight_scale)")
                     return
             # Default to weight_scale_inv
             self._scale_suffix = "weight_scale_inv"
@@ -333,12 +352,20 @@ class FP8SafeTensorLoader(SafeTensorLoader):
             scale_type = "per-channel" if self._is_per_channel else "block-wise"
             print(f"[FP8SafeTensorLoader] Using explicit scale format: {scale_type} ({self._scale_suffix})")
 
-    def _get_experts_prefix(self, base_key: str) -> str:
-        """Get the experts prefix based on detected format."""
+    def _get_experts_prefix_candidates(self, base_key: str) -> list[str]:
+        """Get candidate experts prefixes based on detected format and base key variants."""
         path_tpl, _, _, _ = self.MOE_FORMATS[self._detected_format]
+        candidates = []
         if self._is_vl_model:
             base_key = base_key.replace("model.layers", "model.language_model.layers")
-        return path_tpl.format(base=base_key)
+        candidates.append(path_tpl.format(base=base_key))
+
+        # Some model weights (e.g., Mistral native format) do not have "model." prefix.
+        if base_key.startswith("model."):
+            candidates.append(path_tpl.format(base=base_key[len("model.") :]))
+
+        # Deduplicate while preserving order.
+        return list(dict.fromkeys(candidates))
 
     def _get_proj_names(self):
         """Get projection names (gate, up, down) based on detected format."""
@@ -363,15 +390,21 @@ class FP8SafeTensorLoader(SafeTensorLoader):
         Supports both block-wise (weight_scale_inv) and per-channel (weight_scale) formats.
         Per-channel scales are squeezed from [N, 1] to [N] if needed.
         """
-        experts_prefix = self._get_experts_prefix(base_key)
+        experts_prefix_candidates = self._get_experts_prefix_candidates(base_key)
         gate_name, up_name, down_name = self._get_proj_names()
 
         expert_count = 0
-        while self.has_tensor(f"{experts_prefix}.{expert_count}.{gate_name}.weight"):
-            expert_count += 1
+        experts_prefix = None
+        for prefix in experts_prefix_candidates:
+            expert_count = 0
+            while self.has_tensor(f"{prefix}.{expert_count}.{gate_name}.weight"):
+                expert_count += 1
+            if expert_count > 0:
+                experts_prefix = prefix
+                break
 
-        if expert_count == 0:
-            raise ValueError(f"No experts found for key {experts_prefix}")
+        if expert_count == 0 or experts_prefix is None:
+            raise ValueError(f"No experts found for keys: {experts_prefix_candidates}")
 
         gate_weights = [None] * expert_count
         up_weights = [None] * expert_count
@@ -423,13 +456,13 @@ class FP8SafeTensorLoader(SafeTensorLoader):
         return self._is_per_channel
 
 
-
 class BF16SafeTensorLoader(SafeTensorLoader):
     """Loader for native BF16 expert weights (no quantization, no scales).
 
     Supported formats:
     - DeepSeek style: {base}.mlp.experts.{id}.{gate,up,down}_proj.weight
     - Mixtral/MiniMax style: {base}.block_sparse_moe.experts.{id}.{w1,w3,w2}.weight
+    - Mistral style: {base}.experts.{id}.{w1,w3,w2}.weight
 
     The format is auto-detected during initialization.
     """
@@ -437,6 +470,7 @@ class BF16SafeTensorLoader(SafeTensorLoader):
     MOE_FORMATS = {
         "deepseek": ("{base}.mlp.experts", "gate_proj", "up_proj", "down_proj"),
         "mixtral": ("{base}.block_sparse_moe.experts", "w1", "w3", "w2"),
+        "mistral": ("{base}.experts", "w1", "w3", "w2"),
     }
 
     def __init__(self, file_path: str):
@@ -466,14 +500,24 @@ class BF16SafeTensorLoader(SafeTensorLoader):
                         self._detected_format = fmt_name
                         print(f"[BF16SafeTensorLoader] Detected format: {fmt_name}")
                         return
+                    elif fmt_name == "mistral" and ".mlp.experts" not in key and ".block_sparse_moe.experts" not in key:
+                        self._detected_format = fmt_name
+                        print(f"[BF16SafeTensorLoader] Detected format: {fmt_name}")
+                        return
 
         self._detected_format = "deepseek"
         print("[BF16SafeTensorLoader] No MoE format detected, defaulting to: deepseek")
 
-    def _get_experts_prefix(self, base_key: str) -> str:
-        """Get the experts prefix based on detected format."""
+    def _get_experts_prefix_candidates(self, base_key: str) -> list[str]:
+        """Get candidate experts prefixes based on detected format and base key variants."""
         path_tpl, _, _, _ = self.MOE_FORMATS[self._detected_format]
-        return path_tpl.format(base=base_key)
+        candidates = [path_tpl.format(base=base_key)]
+
+        # Some model weights (e.g., Mistral native format) do not have "model." prefix.
+        if base_key.startswith("model."):
+            candidates.append(path_tpl.format(base=base_key[len("model.") :]))
+
+        return list(dict.fromkeys(candidates))
 
     def _get_proj_names(self):
         """Get projection names (gate, up, down) based on detected format."""
@@ -497,15 +541,21 @@ class BF16SafeTensorLoader(SafeTensorLoader):
         if self._detected_format == "packed":
             return self._load_experts_packed(base_key, device)
 
-        experts_prefix = self._get_experts_prefix(base_key)
+        experts_prefix_candidates = self._get_experts_prefix_candidates(base_key)
         gate_name, up_name, down_name = self._get_proj_names()
 
         expert_count = 0
-        while self.has_tensor(f"{experts_prefix}.{expert_count}.{gate_name}.weight"):
-            expert_count += 1
+        experts_prefix = None
+        for prefix in experts_prefix_candidates:
+            expert_count = 0
+            while self.has_tensor(f"{prefix}.{expert_count}.{gate_name}.weight"):
+                expert_count += 1
+            if expert_count > 0:
+                experts_prefix = prefix
+                break
 
-        if expert_count == 0:
-            raise ValueError(f"No experts found for key {experts_prefix}")
+        if expert_count == 0 or experts_prefix is None:
+            raise ValueError(f"No experts found for keys: {experts_prefix_candidates}")
 
         gate_weights = [None] * expert_count
         up_weights = [None] * expert_count