diff --git a/nemo/collections/llm/api.py b/nemo/collections/llm/api.py
index 71e006472db9..a9b3d4361f5b 100644
--- a/nemo/collections/llm/api.py
+++ b/nemo/collections/llm/api.py
@@ -436,7 +436,7 @@ def export_ckpt(
 def generate(
     path: Union[Path, str],
     prompts: list[str],
-    trainer: Optional[nl.Trainer] = None,
+    trainer: nl.Trainer,
     params_dtype: torch.dtype = torch.bfloat16,
     max_batch_size: int = 4,
     random_seed: Optional[int] = None,
diff --git a/nemo/collections/llm/inference/base.py b/nemo/collections/llm/inference/base.py
index 95da536fde06..0171f1c2dd5c 100644
--- a/nemo/collections/llm/inference/base.py
+++ b/nemo/collections/llm/inference/base.py
@@ -16,6 +16,7 @@
 
 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
 
@@ -44,6 +45,7 @@ def _setup_trainer_and_restore_model(path: Path, trainer: nl.Trainer, model: pl.
         load_optim_state=False,
     )
     trainer.strategy.restore_config = restore_config
+    trainer.strategy._setup_optimizers = False
     trainer.ckpt_path = None
     trainer.strategy.connect(model)
     if trainer.strategy.launcher is not None:
@@ -61,16 +63,22 @@ def _setup_trainer_and_restore_model(path: Path, trainer: nl.Trainer, model: pl.
 
 def setup_model_and_tokenizer(
     path: Path,
-    trainer: Optional[nl.Trainer] = None,
+    trainer: nl.Trainer,
     params_dtype: torch.dtype = torch.bfloat16,
     inference_batch_times_seqlen_threshold: int = 1000,
 ) -> tuple[MCoreGPTModel, MCoreTokenizerWrappper]:
-    model: io.TrainerContext = io.load_context(path=path, subpath="model")
-    trainer = trainer or io.load_context(path=path, subpath="trainer")
+    model: io.TrainerContext = io.load_context(path=ckpt_to_context_subdir(path), subpath="model")
     _setup_trainer_and_restore_model(path=path, trainer=trainer, model=model)
 
     # This is to get the MCore model required in GPTInferenceWrapper.
-    mcore_model = model.module.module.module
+    mcore_model = model
+    while mcore_model:
+        if type(mcore_model) is MCoreGPTModel:
+            break
+        mcore_model = getattr(mcore_model, "module", None)
+    if mcore_model is None or type(mcore_model) is not MCoreGPTModel:
+        raise ValueError("Exact McoreGPTModel instance not found in the model structure.")
+
     inference_wrapped_model = GPTInferenceWrapper(
         mcore_model,
         InferenceWrapperConfig(