diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py
index a708d8deb4e..b1a95b43ada 100755
--- a/src/transformers/trainer.py
+++ b/src/transformers/trainer.py
@@ -2251,7 +2251,7 @@ class Trainer:
             else:
                 debug_overflow = DebugUnderflowOverflow(self.model)  # noqa
 
-        delay_optimizer_creation = is_sagemaker_mp_enabled() or self.is_fsdp_xla_enabled
+        delay_optimizer_creation = is_sagemaker_mp_enabled() or self.is_fsdp_xla_enabled or self.is_fsdp_enabled
 
         # We need to reset the scheduler, as its parameters may be different on subsequent calls
         if self._created_lr_scheduler:
@@ -2304,12 +2304,13 @@ class Trainer:
             # In case of auto_find_batch_size=True
             # Remove FSDP wrapping from sub-models.
             self.model = unwrap_model(self.model, recursive=True)
-            # configure fsdp plugin for qlora if any
-            self._fsdp_qlora_plugin_updates()
 
         if delay_optimizer_creation:
             if use_accelerator_prepare:
-                self.model = self.accelerator.prepare(self.model)
+                # configure fsdp plugin for qlora if any
+                self._fsdp_qlora_plugin_updates()
+                if self.accelerator.mixed_precision != "fp8":
+                    self.model = self.accelerator.prepare(self.model)
             self.create_optimizer_and_scheduler(num_training_steps=max_steps)
 
         # prepare using `accelerator` prepare
@@ -4187,7 +4188,7 @@ class Trainer:
             start_time = time.time()
             model = (
                 self.accelerator.prepare(model)
-                if self.is_deepspeed_enabled or self.is_fsdp_enabled
+                if self.is_deepspeed_enabled or (self.is_fsdp_enabled and self.accelerator.mixed_precision != "fp8")
                 else self.accelerator.prepare_model(model, evaluation_mode=True)
             )
             self.model_preparation_time = round(time.time() - start_time, 4)