diff --git a/docs/config.qmd b/docs/config.qmd
index f01a2ce267..dd2da41b9a 100644
--- a/docs/config.qmd
+++ b/docs/config.qmd
@@ -239,6 +239,9 @@ sample_packing_group_size: 100000
 # The number of samples which can be packed into one sequence. Increase if using a large sequence_len with many short samples.
 sample_packing_bin_size: 200
 
+# Use batch flattening for speedups when not using sample_packing
+batch_flattening:
+
 # Passed through to transformers when loading the model when launched without accelerate
 # Use `sequential` when training w/ model parallelism to limit memory
 device_map:
diff --git a/src/axolotl/utils/config/models/input/v0_4_1/__init__.py b/src/axolotl/utils/config/models/input/v0_4_1/__init__.py
index 8860e06404..3862351522 100644
--- a/src/axolotl/utils/config/models/input/v0_4_1/__init__.py
+++ b/src/axolotl/utils/config/models/input/v0_4_1/__init__.py
@@ -696,7 +696,7 @@ class Config:
     curriculum_sampling: Optional[bool] = None
     multipack_real_batches: Optional[bool] = None
 
-    batch_flattening: Optional[bool] = None
+    batch_flattening: Optional[Union[Literal["auto"], bool]] = None
 
     # for PoSE context length extension
     use_pose: Optional[bool] = None
@@ -929,13 +929,24 @@ def check_sample_packing_wo_flash(cls, data):
     @classmethod
     def check_batch_flattening_fa(cls, data):
         if data.get("batch_flattening"):
-            if not data.get("flash_attention"):
+            batch_flattening_auto = data.get("batch_flattening") == "auto"
+            if not data.get("flash_attention") and not batch_flattening_auto:
                 raise ValueError("batch_flattening requires flash attention")
-            if data.get("sample_packing"):
+            if data.get("sample_packing") and not batch_flattening_auto:
                 raise ValueError("batch_flattening not compatible with sample_packing")
-            if data.get("micro_batch_size") == 1:
+            if data.get("micro_batch_size") == 1 and not batch_flattening_auto:
                 LOG.warning("batch_flattening has no effect with micro_batch_size == 1")
 
+            if (
+                batch_flattening_auto
+                and data.get("flash_attention")
+                and not data.get("sample_packing")
+                and data.get("micro_batch_size") > 1
+            ):
+                data["batch_flattening"] = True
+            elif batch_flattening_auto:
+                data["batch_flattening"] = False
+
         return data
 
     @model_validator(mode="before")
diff --git a/tests/patched/test_validation.py b/tests/patched/test_validation.py
index 2e6fbab101..7745fbfeae 100644
--- a/tests/patched/test_validation.py
+++ b/tests/patched/test_validation.py
@@ -1196,6 +1196,76 @@ def test_torch_version_adopt_req(self, minimal_cfg):
         )
 
 
+class TestSampleOptimConfigValidation(BaseValidation):
+    """
+    test configurations for sample optimizations like batch flattening
+    """
+
+    def test_batch_flattening_auto_enables(self, minimal_cfg):
+        cfg = (
+            DictDefault(
+                {
+                    "flash_attention": True,
+                    "sample_packing": None,
+                    "micro_batch_size": 2,
+                    "batch_flattening": "auto",
+                }
+            )
+            | minimal_cfg
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg["batch_flattening"] is True
+
+    def test_batch_flattening_auto_no_fa(self, minimal_cfg):
+        cfg = (
+            DictDefault(
+                {
+                    "flash_attention": False,
+                    "sample_packing": None,
+                    "micro_batch_size": 2,
+                    "batch_flattening": "auto",
+                }
+            )
+            | minimal_cfg
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg["batch_flattening"] is False
+
+    def test_batch_flattening_auto_mbsz_1(self, minimal_cfg):
+        cfg = (
+            DictDefault(
+                {
+                    "flash_attention": True,
+                    "sample_packing": None,
+                    "micro_batch_size": 1,
+                    "batch_flattening": "auto",
+                }
+            )
+            | minimal_cfg
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg["batch_flattening"] is False
+
+    def test_batch_flattening_auto_packing(self, minimal_cfg):
+        cfg = (
+            DictDefault(
+                {
+                    "flash_attention": True,
+                    "sample_packing": True,
+                    "micro_batch_size": 2,
+                    "batch_flattening": "auto",
+                }
+            )
+            | minimal_cfg
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg["batch_flattening"] is False
+
+
 class TestValidationCheckModelConfig(BaseValidation):
     """
     Test the validation for the config when the model config is available