diff --git a/torchtune/utils/__init__.py b/torchtune/utils/__init__.py
index 2dcffade4..da6cb53d7 100644
--- a/torchtune/utils/__init__.py
+++ b/torchtune/utils/__init__.py
@@ -46,7 +46,6 @@
     register_optim_in_bwd_hooks,
     set_activation_checkpointing,
 )
-from .metric_logging import compute_grad_norm
 from .precision import (
     get_dtype,
     list_dtypes,
@@ -84,5 +83,4 @@
     "register_optim_in_bwd_hooks",
     "profiler",
     "get_quantizer_mode",
-    "compute_grad_norm",
 ]
diff --git a/torchtune/utils/metric_logging.py b/torchtune/utils/metric_logging.py
index f631e4c2f..01e5e188e 100644
--- a/torchtune/utils/metric_logging.py
+++ b/torchtune/utils/metric_logging.py
@@ -12,7 +12,7 @@
 
 from numpy import ndarray
 from omegaconf import DictConfig, OmegaConf
-from torch import nn, Tensor
+from torch import Tensor
 
 from torchtune.utils import get_logger
 from torchtune.utils._distributed import get_world_size_and_rank
@@ -23,17 +23,6 @@
 log = get_logger("DEBUG")
 
 
-def compute_grad_norm(model: nn.Module) -> float:
-    """Compute models grad norm"""
-    total_norm = 0.0
-    for p in model.parameters():
-        if p.grad is not None:
-            param_norm = p.grad.detach().data.norm(2)
-            total_norm += param_norm.item() ** 2
-
-    return total_norm**0.5
-
-
 class MetricLoggerInterface(Protocol):
     """Abstract metric logger."""