diff --git a/torchtitan/components/metrics.py b/torchtitan/components/metrics.py
index 72ab5b8f1..5fc2a9f65 100644
--- a/torchtitan/components/metrics.py
+++ b/torchtitan/components/metrics.py
@@ -12,6 +12,7 @@
 
 import torch
 from torch.utils.tensorboard import SummaryWriter
+
 from torchtitan.components.lr_scheduler import LRSchedulersContainer
 from torchtitan.components.optimizer import OptimizersContainer
 from torchtitan.config_manager import JobConfig
@@ -347,6 +348,7 @@ def log(
         step: int,
         global_avg_loss: float,
         global_max_loss: float,
+        grad_norm: float,
         extra_metrics: dict[str, Any] | None = None,
     ):
         assert self.num_flops_per_token > 0, "num_flops_per_token must be set"
@@ -372,6 +374,7 @@ def log(
         metrics = {
             "loss_metrics/global_avg_loss": global_avg_loss,
             "loss_metrics/global_max_loss": global_max_loss,
+            "optimizer/grad_norm": grad_norm,
             "throughput(tps)": tps,
             "tflops": tflops,
             "mfu(%)": mfu,
@@ -394,7 +397,7 @@ def log(
         color = self.color
         logger.info(
             f"{color.red}step: {step:2}  "
-            f"{color.green}loss: {global_avg_loss:7.4f}  "
+            f"{color.green}loss: {global_avg_loss:7.4f} gnorm: {grad_norm:.2f} "
             f"{color.yellow}memory: {device_mem_stats.max_reserved_gib:5.2f}GiB"
             f"({device_mem_stats.max_reserved_pct:.2f}%)  "
             f"{color.blue}tps: {round(tps):,}  "
diff --git a/torchtitan/train.py b/torchtitan/train.py
index 375a41260..6f8cdc79e 100644
--- a/torchtitan/train.py
+++ b/torchtitan/train.py
@@ -15,20 +15,20 @@
 
 import torchtitan.components.ft as ft
 import torchtitan.protocols.train_spec as train_spec_module
-
 from torchtitan.components.checkpoint import CheckpointManager
 from torchtitan.components.metrics import (
     build_metrics_processor,
-    ensure_pp_loss_visible,
+    ensure_pp_loss_visible
 )
 from torchtitan.config_manager import ConfigManager, JobConfig
-from torchtitan.distributed import ParallelDims, utils as dist_utils
+from torchtitan.distributed import ParallelDims
+from torchtitan.distributed import utils as dist_utils
 from torchtitan.protocols.model_converter import build_model_converters
 from torchtitan.tools import utils
 from torchtitan.tools.logging import init_logger, logger
 from torchtitan.tools.profiling import (
     maybe_enable_memory_snapshot,
-    maybe_enable_profiling,
+    maybe_enable_profiling
 )
 
 
@@ -357,7 +357,7 @@ def train_step(self, input_dict: dict[str, torch.Tensor], labels: torch.Tensor):
                 del pred
                 loss.backward()
 
-        dist_utils.clip_grad_norm_(
+        grad_norm = dist_utils.clip_grad_norm_(
             [p for m in model_parts for p in m.parameters()],
             self.job_config.training.max_norm,
             foreach=True,
@@ -386,7 +386,12 @@ def train_step(self, input_dict: dict[str, torch.Tensor], labels: torch.Tensor):
         else:
             global_avg_loss = global_max_loss = loss.detach().item()
 
-        self.metrics_processor.log(self.step, global_avg_loss, global_max_loss)
+        self.metrics_processor.log(
+            self.step,
+            global_avg_loss,
+            global_max_loss,
+            grad_norm.item(),
+        )
 
     @record
     def train(self):