diff --git a/src/lightning/pytorch/CHANGELOG.md b/src/lightning/pytorch/CHANGELOG.md
index 22cf899496ed6..065dab60dcaec 100644
--- a/src/lightning/pytorch/CHANGELOG.md
+++ b/src/lightning/pytorch/CHANGELOG.md
@@ -41,6 +41,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fix `_generate_seed_sequence_sampling` function not producing unique seeds ([#21399](https://github.com/Lightning-AI/pytorch-lightning/pull/21399))
 
 
+- Fix `ThroughputMonitor` callback emitting warnings too frequently ([#21453](https://github.com/Lightning-AI/pytorch-lightning/pull/21453))
+
+
 ---
 
 
diff --git a/src/lightning/pytorch/callbacks/throughput_monitor.py b/src/lightning/pytorch/callbacks/throughput_monitor.py
index d38928d33de75..ecd5349a3eb68 100644
--- a/src/lightning/pytorch/callbacks/throughput_monitor.py
+++ b/src/lightning/pytorch/callbacks/throughput_monitor.py
@@ -89,6 +89,7 @@ def __init__(
         self._lengths: dict[RunningStage, int] = {}
         self._samples: dict[RunningStage, int] = {}
         self._batches: dict[RunningStage, int] = {}
+        self._module_has_flops: bool | None = None
 
     @override
     def setup(self, trainer: "Trainer", pl_module: "LightningModule", stage: str) -> None:
@@ -133,14 +134,15 @@ def _update(self, trainer: "Trainer", pl_module: "LightningModule", batch: Any,
         if self.length_fn is not None:
             self._lengths[stage] += self.length_fn(batch)
 
-        if hasattr(pl_module, "flops_per_batch"):
-            flops_per_batch = pl_module.flops_per_batch
-        else:
-            rank_zero_warn(
-                "When using the `ThroughputMonitor`, you need to define a `flops_per_batch` attribute or property"
-                f" in {type(pl_module).__name__} to compute the FLOPs."
-            )
-            flops_per_batch = None
+        if self._module_has_flops is None:
+            self._module_has_flops = hasattr(pl_module, "flops_per_batch")
+            if not self._module_has_flops:
+                rank_zero_warn(
+                    "When using the `ThroughputMonitor`, you need to define a `flops_per_batch` attribute or property"
+                    f" in {type(pl_module).__name__} to compute the FLOPs."
+                )
+
+        flops_per_batch = pl_module.flops_per_batch if self._module_has_flops else None
 
         self._samples[stage] += self.batch_size_fn(batch)
         self._batches[stage] += 1
diff --git a/tests/tests_pytorch/callbacks/test_throughput_monitor.py b/tests/tests_pytorch/callbacks/test_throughput_monitor.py
index 93bfe4e844c3a..d213f167fdbcc 100644
--- a/tests/tests_pytorch/callbacks/test_throughput_monitor.py
+++ b/tests/tests_pytorch/callbacks/test_throughput_monitor.py
@@ -1,3 +1,4 @@
+import warnings
 from unittest import mock
 from unittest.mock import ANY, Mock, call
 
@@ -482,3 +483,31 @@ def test_throughput_monitor_validation_with_many_epochs(tmp_path):
                     batch_num = 1
                 if end_train_timings_idx < len(timings):
                     cur_train += timings[end_train_timings_idx] - timings[start_train_timings_idx]
+
+
+def test_throughput_monitor_warn_once():
+    monitor = ThroughputMonitor(batch_size_fn=lambda x: 1)
+    model = BoringModel()
+
+    trainer = Trainer(
+        devices=1,
+        logger=False,
+        callbacks=[monitor],
+        max_epochs=1,
+        limit_train_batches=2,
+        enable_checkpointing=False,
+        enable_model_summary=False,
+        enable_progress_bar=False,
+    )
+
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        trainer.fit(model)
+
+    throughput_warnings = [
+        warn
+        for warn in w
+        if "When using the `ThroughputMonitor`, you need to define a `flops_per_batch`" in warn.message.args[0]
+    ]
+
+    assert len(throughput_warnings) == 1, "Expected exactly one warning about missing `flops_per_batch`."