diff --git a/nemo/collections/llm/inference/base.py b/nemo/collections/llm/inference/base.py
index d1ceb462a2a8..196e89fdb33e 100644
--- a/nemo/collections/llm/inference/base.py
+++ b/nemo/collections/llm/inference/base.py
@@ -13,10 +13,10 @@
 )
 from megatron.core.models.gpt.gpt_model import GPTModel as MCoreGPTModel
 from pytorch_lightning.trainer.states import TrainerFn
-from nemo.lightning.ckpt_utils import ckpt_to_context_subdir
 
 import nemo.lightning as nl
 from nemo.lightning import io
+from nemo.lightning.ckpt_utils import ckpt_to_context_subdir
 from nemo.lightning.pytorch.strategies.megatron_strategy import MegatronStrategy
 from nemo.lightning.pytorch.strategies.utils import RestoreConfig
 
@@ -71,10 +71,10 @@ def setup_model_and_tokenizer(
     model: io.TrainerContext = io.load_context(path=ckpt_to_context_subdir(path), subpath="model")
     trainer = trainer or io.load_context(path=ckpt_to_context_subdir(path), subpath="trainer")
     if tensor_parallel_size > 0:
-        trainer.strategy.tensor_model_parallel_size=tensor_parallel_size
-        trainer.devices=tensor_parallel_size
-        trainer.strategy.parallel_devices=[torch.device(f"cuda:{i}") for i in range(tensor_parallel_size)]
-        trainer.strategy.launcher.num_processes=len(trainer.strategy.parallel_devices)
+        trainer.strategy.tensor_model_parallel_size = tensor_parallel_size
+        trainer.devices = tensor_parallel_size
+        trainer.strategy.parallel_devices = [torch.device(f"cuda:{i}") for i in range(tensor_parallel_size)]
+        trainer.strategy.launcher.num_processes = len(trainer.strategy.parallel_devices)
     _setup_trainer_and_restore_model(path=path, trainer=trainer, model=model)
 
     inference_wrapped_model = GPTInferenceWrapper(