diff --git a/gptqmodel/utils/model.py b/gptqmodel/utils/model.py
index a03c98430..b9acd973a 100644
--- a/gptqmodel/utils/model.py
+++ b/gptqmodel/utils/model.py
@@ -50,7 +50,7 @@
 from ..nn_modules.qlinear import BaseQuantLinear
 from ..nn_modules.qlinear.exllama import ExllamaQuantLinear
 from ..nn_modules.qlinear.exllamav2 import ExllamaV2QuantLinear
-from ..nn_modules.qlinear.ipex import IPEXQuantLinear
+from ..nn_modules.qlinear.ipex import IPEXQuantLinear, HAS_IPEX
 from ..quantization import FORMAT, QuantizeConfig
 from ..quantization.config import FORMAT_FIELD_JSON, QUANT_METHOD, dynamic_get
 from .backend import BACKEND
@@ -892,28 +892,36 @@ def auto_dtype(config: PretrainedConfig,
 
     assert isinstance(device, DEVICE)
 
-    # for inference, DynamicCuda, Exllama, Triton, and Marlin are all fp16 kernels
-    if quant_inference and device != DEVICE.CPU:
-        return torch.float16
-
     # TODO: both MPS and XPU are locked to float16
     # XPU stack is missing bfloat16 (hardware supports it)
     # MPS stack has bfloat16 bugs in pytorch
     if device in [DEVICE.MPS, DEVICE.XPU]:
+        log.info("Loader: Auto dtype (MPS or XPU): `torch.float16`")
         return torch.float16
 
+    # IPEX for CPU is optimized for bfloat16
+    if device in [DEVICE.CPU] and HAS_IPEX:
+        log.info("Loader: Auto dtype (CPU + IPEX): `torch.bfloat16`")
+        return torch.bfloat16
+
     # get dtype from config
     dtype = getattr(config, "torch_dtype")
     if dtype and not isinstance(dtype, torch.dtype):
         raise ValueError(f"torch_dtype in config must be a torch.dtype, but got {dtype}")
 
-    if dtype == torch.float32:
+    if dtype in [torch.float32, torch.float64]:
+        log.info("Loader: Auto dtype (float32 down-cast): `torch.bfloat16`")
         return torch.bfloat16
     elif dtype == torch.float16:
+        log.info("Loader: Auto dtype (native float16): `torch.float16`")
         return torch.float16
+    elif dtype == torch.bfloat16:
+        log.info("Loader: Auto dtype (native bfloat16): `torch.bfloat16`")
+        return torch.bfloat16
     else:
         # TODO: extract weights from model file to check their original type, instead of forcing bfloat16
         # up/down-cast everything else to bfloat16 if not already in bfloat16
+        log.info(f"Loader: Auto dtype (native = `{dtype}`): `torch.bfloat16`")
         return torch.bfloat16
 
 
diff --git a/tests/test_q4_marlin.py b/tests/test_q4_marlin.py
index 29e55053e..7f28a1fd5 100644
--- a/tests/test_q4_marlin.py
+++ b/tests/test_q4_marlin.py
@@ -57,7 +57,7 @@ class TestQ4Marlin(ModelTest):
     )
     def test_generation(self, model_id):
         try:
-            model_q = GPTQModel.load(model_id, device="cuda:0", backend=BACKEND.MARLIN, torch_dtype=torch.bfloat16)
+            model_q = GPTQModel.load(model_id, device="cuda:0", backend=BACKEND.MARLIN)
         except ValueError as e:
             raise e