diff --git a/src/lightning/pytorch/callbacks/throughput_monitor.py b/src/lightning/pytorch/callbacks/throughput_monitor.py
index a2d73d83184b1..5b4e9d39b4a06 100644
--- a/src/lightning/pytorch/callbacks/throughput_monitor.py
+++ b/src/lightning/pytorch/callbacks/throughput_monitor.py
@@ -87,6 +87,7 @@ def __init__(
         self._throughputs: Dict[RunningStage, Throughput] = {}
         self._t0s: Dict[RunningStage, float] = {}
         self._lengths: Dict[RunningStage, int] = {}
+        self._samples: Dict[RunningStage, int] = {}
 
     @override
     def setup(self, trainer: "Trainer", pl_module: "LightningModule", stage: str) -> None:
@@ -109,6 +110,7 @@ def _start(self, trainer: "Trainer") -> None:
         self._throughputs[stage].reset()
         self._lengths[stage] = 0
         self._t0s[stage] = time.perf_counter()
+        self._samples[stage] = 0
 
     @torch.inference_mode()  # in case `length_fn` or `batch_size_fn` computes grads
     def _update(self, trainer: "Trainer", pl_module: "LightningModule", batch: Any, iter_num: int) -> None:
@@ -133,12 +135,13 @@ def _update(self, trainer: "Trainer", pl_module: "LightningModule", batch: Any,
             )
             flops_per_batch = None
 
-        batch_size = self.batch_size_fn(batch)
+        self._samples[stage] += self.batch_size_fn(batch)
+
         throughput.update(
             time=elapsed,
             batches=iter_num,
             # this assumes that all iterations used the same batch size
-            samples=iter_num * batch_size,
+            samples=self._samples[stage],
             lengths=None if self.length_fn is None else self._lengths[stage],
             flops=flops_per_batch,
         )