diff --git a/olmo/config.py b/olmo/config.py
index 6da7dc03d..811809ffc 100644
--- a/olmo/config.py
+++ b/olmo/config.py
@@ -1200,6 +1200,11 @@ class TrainConfig(BaseConfig):
     Weights & Biases configuration.
     """
 
+    tensorboard_path: Optional[str] = None
+    """
+    Path to tensorbard log output directory.
+    """
+
     speed_monitor: SpeedMonitorConfig = field(default_factory=SpeedMonitorConfig)
     """
     Speed monitor configuration.
diff --git a/olmo/tensorbard_logger.py b/olmo/tensorbard_logger.py
new file mode 100644
index 000000000..d6708932c
--- /dev/null
+++ b/olmo/tensorbard_logger.py
@@ -0,0 +1,43 @@
+import logging
+import os.path as osp
+
+logger = logging.getLogger(__name__)
+
+try:
+    from torch.utils.tensorboard import SummaryWriter
+    HAS_TENSORBOARD = True
+except ImportError:
+    HAS_TENSORBOARD = False
+
+
+# create a new class inheriting from SummaryWriter
+class TBNewSummaryWriter(SummaryWriter):
+
+    def __init__(self, log_dir=None, comment="", **kwargs):
+        super().__init__(log_dir, comment, **kwargs)
+
+
+    # create a new function that will take dictionary as input
+    # and uses built-in add_scalar() function
+    # that function combines all plots into one subgroup by a tag
+    def add_scalar_dict(self, dictionary, global_step, tag=None):
+        for name, val in dictionary.items():
+            if tag is not None:
+                name = osp.join(tag, name)
+            self.add_scalar(name, val, global_step)
+
+
+    def log(self, dictionary, global_step, tag=None):
+        self.add_scalar_dict(dictionary, global_step, tag)
+
+
+    def write_args_to_tensorboard(self, args, iteration, prefix=""):
+        """Write arguments to tensorboard."""
+        if prefix:
+            prefix = f"{prefix}."
+        for arg in args.keys():
+            arg_text = f"{prefix}{arg}"
+            if isinstance(args[arg], dict):
+                self.write_args_to_tensorboard(args[arg], iteration, prefix=arg_text)
+            else:
+                self.add_text(arg_text, str(args[arg]), global_step=iteration)
diff --git a/olmo/train.py b/olmo/train.py
index a4f9919c8..5891f7bee 100644
--- a/olmo/train.py
+++ b/olmo/train.py
@@ -58,6 +58,7 @@
     synchronize_value,
 )
 from .util import upload
+from olmo.tensorbard_logger import TBNewSummaryWriter, HAS_TENSORBOARD
 
 __all__ = ["SpeedMonitor", "LRMonitor", "Trainer"]
 
@@ -240,6 +241,15 @@ def __post_init__(self):
                 self.loss_fn = fused_loss_fn
             else:
                 raise NameError("`fused_loss_fn` is not defined. Please ensure that `flash_attn` is installed.")
+        self.logger = None
+        if self.cfg.tensorboard_path:
+            if HAS_TENSORBOARD:
+                log_dir = Path(self.cfg.tensorboard_path)
+                log_dir.mkdir(parents=True, exist_ok=True)
+                self.logger = TBNewSummaryWriter(log_dir=log_dir)
+                self.logger.write_args_to_tensorboard(self.cfg.asdict())
+            else:
+                logger.warn("Failed to import tensorbard writer, will not write tensorbard logs.")
 
     @property
     def dataset(self) -> IterableDataset:
@@ -1126,6 +1136,8 @@ def fit(self):
             eval_metrics = self.eval()
             if wandb.run is not None:
                 wandb.log(eval_metrics, step=self.global_step)
+            if self.logger is not None:
+                self.logger.log(eval_metrics, global_step=self.global_step)
 
         # Set model to 'train' mode.
         self.dist_model.train()
@@ -1141,6 +1153,8 @@ def fit(self):
             self.log_metrics_to_console("Pre-train system metrics", sys_metrics)
             if wandb.run is not None:
                 wandb.log(sys_metrics, step=0)
+            if self.logger is not None:
+                self.logger.log(sys_metrics, global_step=0)
 
         # Python Profiler stuff
         if self.cfg.python_profiling:
@@ -1251,6 +1265,8 @@ def on_trace_ready(p):
                         and self.global_step % self.cfg.wandb.log_interval == 0
                     ):
                         wandb.log(metrics, step=self.global_step)
+                    if self.logger is not None:
+                        self.logger.log(metrics, global_step=self.global_step)
 
                     # Check if/when run should be canceled.
                     if not cancel_initiated and self.global_step % self.cfg.canceled_check_interval == 0:
@@ -1317,6 +1333,8 @@ def on_trace_ready(p):
                         # Log metrics to W&B.
                         if wandb.run is not None:
                             wandb.log(eval_metrics, step=self.global_step)
+                        if self.logger is not None:
+                            self.logger.log(eval_metrics, global_step=self.global_step)
 
                         # Reset speed monitor so that we don't count the time taken to run evaluations.
                         speed_monitor.reset()
@@ -1387,6 +1405,8 @@ def close(self, exit_code: int = 0) -> None:
             gc.disable()
         if wandb.run is not None:
             wandb.finish(exit_code=exit_code, quiet=True)
+        if self.logger is not None:
+            self.logger.close()
 
     def __enter__(self) -> Trainer:
         return self