diff --git a/src/transformers/quantizers/quantizer_awq.py b/src/transformers/quantizers/quantizer_awq.py
index 7b81c93edf1..4dd818f6465 100644
--- a/src/transformers/quantizers/quantizer_awq.py
+++ b/src/transformers/quantizers/quantizer_awq.py
@@ -87,6 +87,7 @@ def validate_environment(self, device_map, **kwargs):
     def update_torch_dtype(self, torch_dtype):
         if torch_dtype is None:
             torch_dtype = torch.float16
+            logger.info("Loading the model in `torch.float16`. To overwrite it, set `torch_dtype` manually.")
         elif torch_dtype != torch.float16:
             logger.warning("We suggest you to set `torch_dtype=torch.float16` for better efficiency with AWQ.")
         return torch_dtype
diff --git a/src/transformers/quantizers/quantizer_gptq.py b/src/transformers/quantizers/quantizer_gptq.py
index 233a5279d3f..bf5079435d6 100644
--- a/src/transformers/quantizers/quantizer_gptq.py
+++ b/src/transformers/quantizers/quantizer_gptq.py
@@ -64,6 +64,7 @@ def validate_environment(self, *args, **kwargs):
     def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
         if torch_dtype is None:
             torch_dtype = torch.float16
+            logger.info("Loading the model in `torch.float16`. To overwrite it, set `torch_dtype` manually.")
         elif torch_dtype != torch.float16:
             logger.info("We suggest you to set `torch_dtype=torch.float16` for better efficiency with GPTQ.")
         return torch_dtype
diff --git a/src/transformers/quantizers/quantizer_torchao.py b/src/transformers/quantizers/quantizer_torchao.py
index 10d2b184ef1..bcc9c57dfa0 100644
--- a/src/transformers/quantizers/quantizer_torchao.py
+++ b/src/transformers/quantizers/quantizer_torchao.py
@@ -114,6 +114,9 @@ def update_torch_dtype(self, torch_dtype):
                 torch_dtype = torch.bfloat16
         if self.quantization_config.quant_type == "int8_dynamic_activation_int8_weight":
             if torch_dtype is None:
+                logger.info(
+                    "Setting torch_dtype to torch.float32 for int8_dynamic_activation_int8_weight quantization as no torch_dtype was specified in from_pretrained"
+                )
                 # we need to set the torch_dtype, otherwise we have dtype mismatch when performing the quantized linear op
                 torch_dtype = torch.float32
         return torch_dtype