diff --git a/src/nanotron/generation/decode.py b/src/nanotron/generation/decode.py
index 6ab71fad..45121b6d 100644
--- a/src/nanotron/generation/decode.py
+++ b/src/nanotron/generation/decode.py
@@ -236,13 +236,17 @@ def decode_text(
             )
 
             if is_bench:
-                start_time, elapsed_time_first_iteration = time.perf_counter(), 0
+                start_time = torch.cuda.Event(enable_timing=True)
+                end_time = torch.cuda.Event(enable_timing=True)
+                start_time.record()
+                elapsed_time_first_iteration = 0
 
             for generation_iter in range(max_new_tokens):
 
                 if is_bench and generation_iter == 0:
+                    end_time.record()
                     torch.cuda.synchronize()
-                    elapsed_time_first_iteration = start_time - time.perf_counter()
+                    elapsed_time_first_iteration = start_time.elapsed_time(end_time) / 1000
 
                 all_new_decoder_input_ids_and_mask_same_rank: List[
                     Tuple[Union[torch.LongTensor, TensorPointer], Union[torch.BoolTensor, TensorPointer]]
@@ -393,8 +397,9 @@ def generator():
 
             if is_bench:
                 # Compute throughput (tok/s/gpu). Note that the first generation is done with full seq_len, so we don't count it.
+                end_time.record()
                 torch.cuda.synchronize()
-                total_time_sec = time.perf_counter() - start_time - elapsed_time_first_iteration
+                total_time_sec = start_time.elapsed_time(end_time) / 1000 - elapsed_time_first_iteration
                 # We generate 1 token per iteration per batch (batch=microbatch)
                 # Number of tokens generated every iteration: gbs/iteration_time
                 global_batch_size = len(batches) * parallel_context.dp_pg.size()
diff --git a/src/nanotron/helpers.py b/src/nanotron/helpers.py
index f7bf63e5..28dddacf 100644
--- a/src/nanotron/helpers.py
+++ b/src/nanotron/helpers.py
@@ -554,14 +554,15 @@ def test_all_pair_to_pair(
                 continue
             test_tensor = torch.zeros((int(throughput_size),), dtype=torch.uint8, device=torch.device("cuda"))
             for k in range(throughput_iters):
-                pre = time.perf_counter()
-                torch.cuda.synchronize()
+                pre = torch.cuda.Event(enable_timing=True)
+                post = torch.cuda.Event(enable_timing=True)
+                pre.record()
                 if wr == a:
                     dist.send(test_tensor, b, group=parallel_context.world_pg, tag=i + k)
                 elif wr == b:
                     dist.recv(test_tensor, a, group=parallel_context.world_pg, tag=i + k)
                 torch.cuda.synchronize()
-                duration = time.perf_counter() - pre
+                duration = pre.elapsed_time(post) / 1000 # time is reported in milliseconds
             del test_tensor
             gc.collect()
             torch.cuda.empty_cache()
diff --git a/src/nanotron/trainer.py b/src/nanotron/trainer.py
index 0eda00dc..3ec05a02 100644
--- a/src/nanotron/trainer.py
+++ b/src/nanotron/trainer.py
@@ -420,7 +420,8 @@ def train(
                 if isinstance(prof, torch.profiler.profile):
                     prof.step()
 
-                self.iteration_start_time = time.time()
+                self.iteration_start_time = torch.cuda.Event(enable_timing=True)
+                self.iteration_start_time.record()
                 self._update_dataloader_based_on_training_stages(dataloader_or_dls)
 
                 # Training step
@@ -553,9 +554,11 @@ def train_step_logs(
         loss_avg: Optional[torch.Tensor],
     ) -> None:
         # TODO @nouamanetazi: Megatron-LM seems to be using a barrier to report their interval time. Check if this is necessary. https://github.com/NouamaneTazi/Megatron-LM/blob/e241a96c3085b18e36c6cee1d68a8155de77b5a6/megatron/training.py#L607
-        dist.barrier()
+        iteration_end_time = torch.cuda.Event(enable_timing=True)
+        iteration_end_time.record()
+        # dist.barrier()
         torch.cuda.synchronize()
-        elapsed_time_per_iteration_ms = (time.time() - self.iteration_start_time) * 1000
+        elapsed_time_per_iteration_ms = self.iteration_start_time.elapsed_time(iteration_end_time) # time reported in milliseconds
         tokens_per_sec = (
             self.global_batch_size * self.sequence_length / (elapsed_time_per_iteration_ms / 1000)
         )  # tokens_per_sec is calculated using sequence_length