diff --git a/src/axolotl/utils/trainer.py b/src/axolotl/utils/trainer.py
index 2e3728cc8..fdf86e567 100644
--- a/src/axolotl/utils/trainer.py
+++ b/src/axolotl/utils/trainer.py
@@ -330,7 +330,6 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                         / cfg.sample_packing_eff_est
                         / cfg.sequence_len
                         // cfg.batch_size
-                        // int(os.environ.get("WORLD_SIZE", 1))
                     )
                     - 1
                 )
@@ -359,18 +358,14 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                 train_dataset.remove_columns(["length"]),
                 batch_sampler=sampler,
             )
-            data_loader_len = len(data_loader) // cfg.batch_size
+            data_loader_len = len(data_loader) // (
+                cfg.world_size * cfg.gradient_accumulation_steps
+            )
             actual_eff = sampler.efficiency()
             LOG.debug(f"data_loader_len: {data_loader_len}", main_process_only=True)
             # FIXME: is there a bug here somewhere? the total num steps depends
             # on the agreed on value for sample_packing_eff_est
-            total_num_steps = int(
-                math.floor(
-                    data_loader_len
-                    * cfg.num_epochs
-                    / int(os.environ.get("WORLD_SIZE", 1))
-                )
-            )
+            total_num_steps = int(math.floor(data_loader_len * cfg.num_epochs))
 
             def calc_sample_packing_eff_est(estimates: List[float]):
                 LOG.info(f"sample_packing_eff_est across ranks: {repr(estimates)}")
@@ -391,12 +386,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             )
     else:
         total_num_steps = int(
-            math.ceil(
-                len(train_dataset)
-                * cfg.num_epochs
-                / int(os.environ.get("WORLD_SIZE", 1))
-                / cfg.batch_size
-            )
+            math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
         )
     LOG.debug(f"total_num_steps: {total_num_steps}", main_process_only=True)
     return total_num_steps