diff --git a/src/accelerate/accelerator.py b/src/accelerate/accelerator.py
old mode 100755
new mode 100644
index a1cbfaa8d68..4f86e05b714
--- a/src/accelerate/accelerator.py
+++ b/src/accelerate/accelerator.py
@@ -129,7 +129,7 @@
     SCALER_NAME,
 )
 from .utils.modeling import get_state_dict_offloaded_model
-from .utils.other import compile_regions, compile_regions_deepspeed, is_compiled_module
+from .utils.other import compile_regions, compile_regions_deepspeed, has_compiled_regions, is_compiled_module
 
 
 if is_deepspeed_available():
@@ -1885,6 +1885,15 @@ def prepare_model(
                             device_ids, output_device = [self.local_process_index], self.local_process_index
                     else:
                         device_ids, output_device = None, None
+                    # When torch.compile is enabled, compile the inner model before wrapping with DDP.
+                    # This prevents DDP's internal methods (e.g. logger.set_runtime_stats_and_log)
+                    # from being traced by Dynamo, which would cause an "Unsupported method call" error.
+                    # This follows the PyTorch-recommended pattern for DDP + torch.compile.
+                    if self.state.dynamo_plugin.backend != DynamoBackend.NO and not is_compiled_module(model):
+                        if self.state.dynamo_plugin.use_regional_compilation:
+                            model = compile_regions(model, **self.state.dynamo_plugin.to_kwargs())
+                        else:
+                            model = torch.compile(model, **self.state.dynamo_plugin.to_kwargs())
                     model = torch.nn.parallel.DistributedDataParallel(
                         model, device_ids=device_ids, output_device=output_device, **kwargs
                     )
@@ -2046,6 +2055,13 @@ def prepare_model(
                 self._models[-1] = model
             elif self.distributed_type == DistributedType.MULTI_CPU:
                 kwargs = self.ddp_handler.to_kwargs() if self.ddp_handler else {}
+                # When torch.compile is enabled, compile the inner model before wrapping with DDP
+                # to avoid Dynamo tracing DDP internals (see MULTI_GPU path above for details).
+                if self.state.dynamo_plugin.backend != DynamoBackend.NO and not is_compiled_module(model):
+                    if self.state.dynamo_plugin.use_regional_compilation:
+                        model = compile_regions(model, **self.state.dynamo_plugin.to_kwargs())
+                    else:
+                        model = torch.compile(model, **self.state.dynamo_plugin.to_kwargs())
                 model = torch.nn.parallel.DistributedDataParallel(model, **kwargs)
                 if self.ddp_handler is not None:
                     self.ddp_handler.register_comm_hook(model)
@@ -2054,8 +2070,11 @@ def prepare_model(
         # Now we can apply the FP8 autocast
         if self.fp8_backend == FP8BackendType.TE and self.delayed_fp8_autocast:
             model = apply_fp8_autowrap(model, self.te_recipe_handler or self.fp8_recipe_handler)
-        # torch.compile should be called last and only if the model isn't already compiled
-        if self.state.dynamo_plugin.backend != DynamoBackend.NO and not is_compiled_module(model):
+        # torch.compile should be called last and only if the model isn't already compiled.
+        # Also skip if the model has compiled submodules (e.g. the inner module of DDP was already
+        # compiled before DDP wrapping to avoid Dynamo tracing DDP internals like
+        # logger.set_runtime_stats_and_log which would cause an "Unsupported method call" error).
+        if self.state.dynamo_plugin.backend != DynamoBackend.NO and not is_compiled_module(model) and not has_compiled_regions(model):
             if self.state.dynamo_plugin.use_regional_compilation:
                 model = compile_regions(model, **self.state.dynamo_plugin.to_kwargs())
             else: