diff --git a/examples/llm/megatron_gpt_pretraining.py b/examples/llm/megatron_gpt_pretraining.py
index d3d049e4296e..73e96a23bf81 100644
--- a/examples/llm/megatron_gpt_pretraining.py
+++ b/examples/llm/megatron_gpt_pretraining.py
@@ -92,7 +92,7 @@ def get_args():
         callbacks=callbacks,
         log_every_n_steps=1,
         limit_val_batches=2,
-        plugins=nl.MegatronMixedPrecision(precision="bf16-mixed", amp_O2=False),
+        plugins=nl.MegatronMixedPrecision(precision="bf16-mixed"),
     )
 
     nemo_logger = NeMoLogger(
diff --git a/nemo/lightning/pytorch/plugins/mixed_precision.py b/nemo/lightning/pytorch/plugins/mixed_precision.py
index 5e43e09c0420..65b7c6292249 100644
--- a/nemo/lightning/pytorch/plugins/mixed_precision.py
+++ b/nemo/lightning/pytorch/plugins/mixed_precision.py
@@ -26,11 +26,17 @@
 AnyT = TypeVar("AnyT")
 
 
+def get_optim_config(optimizer: Optimizer):
+    try:
+        return optimizer.mcore_optimizer.config
+    except:
+        raise ValueError("Failed to extract optimizer config from module.")
+
+
 class MegatronMixedPrecision(MixedPrecision):
     def __init__(
         self,
         precision: Literal["16-mixed", "bf16-mixed"],
-        amp_O2: bool = False,
         device="cuda",
     ) -> None:
         if precision == "bf16-mixed":
@@ -39,21 +45,6 @@ def __init__(
             scaler = GradScaler(init_scale=2**32, growth_interval=1000, hysteresis=2)
 
         super().__init__(precision, device, scaler)
-        self.amp_O2 = amp_O2
-
-    def connect(
-        self, model: Module, optimizers: List[Optimizer], lr_schedulers: List[Any]
-    ) -> Tuple[Module, List[Optimizer], List[Any]]:
-        """Connects this plugin to the accelerator and the training process."""
-        from nemo.core.optim import MainParamsOptimizerWrapper
-
-        if not optimizers or not self.amp_O2 or isinstance(optimizers[0], MainParamsOptimizerWrapper):
-            return model, optimizers, lr_schedulers
-
-        _optimizers = [*optimizers]
-        _optimizers[0] = self.convert_optimizer(_optimizers[0])
-
-        return model, _optimizers, lr_schedulers
 
     def convert_module(self, module: Module) -> Module:
         """Convert the module parameters to the precision type this plugin handles.
@@ -68,11 +59,11 @@ def convert_module(self, module: Module) -> Module:
             config = get_model_config(module.module)
             config.fp16 = self.precision == "16-mixed"
             config.bf16 = self.precision == "bf16-mixed"
-            if isinstance(module.module, Float16Module):
-                new_float16_module = Float16Module(config, module.module.module)
-                module.module = new_float16_module
-            else:
+            config.autocast = False
+            if hasattr(module, 'module'):
                 module.module = Float16Module(config, module.module)
+            else:
+                module = Float16Module(config, module)
 
         return module
 
@@ -82,16 +73,10 @@ def convert_optimizer(self, optimizer: Optimizer) -> Optimizer:
         This is optional and depends on the precision limitations during optimization.
 
         """
-        from nemo.core.optim import MainParamsOptimizerWrapper
-
-        if isinstance(optimizer, MainParamsOptimizerWrapper) or not self.amp_O2:
-            return optimizer
-
-        return MainParamsOptimizerWrapper(
-            optimizer,
-            fp32_grad_accum=True,
-            contiguous_grad_bucket=True,
-        )
+        optim_config = get_optim_config(optimizer)
+        assert optim_config.bf16 == (self.precision == "bf16-mixed"), "BF16 enabled on model but not on optimizer"
+        assert optim_config.fp16 == (self.precision == "fp16-mixed"), "BF16 enabled on model but not on optimizer"
+        return optimizer
 
     def convert_input(self, data: AnyT) -> AnyT:
         """Convert model inputs (forward) to the floating point precision type of this plugin.
@@ -120,7 +105,7 @@ def optimizer_step(
     ) -> None:
         from nemo.core.optim import MainParamsOptimizerWrapper
 
-        if not self.amp_O2 and not isinstance(optimizer, MainParamsOptimizerWrapper):
+        if not isinstance(optimizer, MainParamsOptimizerWrapper):
             return super().optimizer_step(optimizer, model, closure, **kwargs)
 
         if self.scaler is None:
diff --git a/nemo/lightning/pytorch/strategies.py b/nemo/lightning/pytorch/strategies.py
index a208fac4017f..f22ed5b40a20 100644
--- a/nemo/lightning/pytorch/strategies.py
+++ b/nemo/lightning/pytorch/strategies.py
@@ -621,6 +621,8 @@ def load_model_state_dict(self, checkpoint: Mapping[str, Any], strict: bool = Tr
         assert self.megatron_parallel is not None
 
         _strategy_lib.load_model_state_dict(self.megatron_parallel, checkpoint, strict=strict)
+        for opt in self.optimizers:
+            opt.reload_model_params()
 
     @property
     @override