diff --git a/unsloth/models/_utils.py b/unsloth/models/_utils.py
index 76952b00a5..301cdf7b80 100644
--- a/unsloth/models/_utils.py
+++ b/unsloth/models/_utils.py
@@ -59,6 +59,7 @@ __all__ = [
     "unsloth_fused_ce_loss",
     "patch_unsloth_smart_gradient_checkpointing",
     "unpatch_unsloth_smart_gradient_checkpointing",
+    "apply_unsloth_gradient_checkpointing",
     "patch_compiled_autograd",
     "process_vision_info",
     "unsloth_compile_transformers",
@@ -148,6 +149,41 @@ from unsloth_zoo.temporary_patches import (
     TEMPORARY_PATCHES,
 )
 
+
+def apply_unsloth_gradient_checkpointing(
+    use_gradient_checkpointing, max_seq_length, dtype
+):
+    """
+    Apply gradient checkpointing with smart heuristics.
+
+    For seq < 512, the overhead of gradient offloading in gc="unsloth" mode
+    is not worth it. Benchmarks show standard gc is faster for small sequences.
+
+    Args:
+        use_gradient_checkpointing: "unsloth", True, False, or None
+        max_seq_length: The maximum sequence length
+        dtype: The model dtype for patching
+
+    Returns:
+        The effective use_gradient_checkpointing value (may change from "unsloth" to True)
+    """
+    if use_gradient_checkpointing == "unsloth":
+        # Gradient offloading overhead is not worth it for small sequences.
+        # Benchmarks show crossover point is around seq_len 384-512.
+        # For seq < 512, standard gradient checkpointing is faster.
+        if max_seq_length < 512:
+            unpatch_unsloth_smart_gradient_checkpointing()
+            return True
+        else:
+            patch_unsloth_smart_gradient_checkpointing(dtype = dtype)
+            return "unsloth"
+    elif use_gradient_checkpointing in (True, False):
+        # User explicitly set True or False - unpatch any previous "unsloth" patching
+        unpatch_unsloth_smart_gradient_checkpointing()
+        return use_gradient_checkpointing
+    return use_gradient_checkpointing
+
+
 for temporary_patch in TEMPORARY_PATCHES:
     temporary_patch()
 
diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py
index 39f2ba1460..fb3b8133a5 100644
--- a/unsloth/models/llama.py
+++ b/unsloth/models/llama.py
@@ -19,7 +19,7 @@ import functools
 from typing import Optional, Tuple, List, Union
 
 from ._utils import *
-from ._utils import patch_unsloth_smart_gradient_checkpointing
+from ._utils import apply_unsloth_gradient_checkpointing
 from ._utils import __version__, importlib_version
 from ._utils import move_to_device
 from ._utils import (
@@ -2693,10 +2693,12 @@ class FastLlamaModel:
             return model
         transformers_set_seed(random_state)
 
-        if use_gradient_checkpointing == "unsloth":
-            patch_unsloth_smart_gradient_checkpointing(
-                dtype = model.get_input_embeddings().weight.dtype
-            )
+        # Apply gradient checkpointing with smart heuristics
+        max_seq = getattr(model, "max_seq_length", 512)
+        dtype = model.get_input_embeddings().weight.dtype
+        use_gradient_checkpointing = apply_unsloth_gradient_checkpointing(
+            use_gradient_checkpointing, max_seq, dtype
+        )
 
         if type(r) is not int:
             raise TypeError(f"Unsloth: Rank of {str(r)} must be an integer.")
diff --git a/unsloth/models/loader.py b/unsloth/models/loader.py
index eb3b21e206..ef7e67b468 100644
--- a/unsloth/models/loader.py
+++ b/unsloth/models/loader.py
@@ -88,7 +88,7 @@ from ._utils import (
     patch_compiling_bitsandbytes,
     patch_model_and_tokenizer,
     prepare_model_for_kbit_training,
-    patch_unsloth_smart_gradient_checkpointing,
+    apply_unsloth_gradient_checkpointing,
     patch_compiled_autograd,
     process_vision_info,
     unsloth_compile_transformers,
@@ -559,8 +559,10 @@ class FastLanguageModel(FastLlamaModel):
                 **kwargs,
             )
 
-        if use_gradient_checkpointing == "unsloth":
-            patch_unsloth_smart_gradient_checkpointing(dtype = dtype)
+        # Apply gradient checkpointing with smart heuristics
+        use_gradient_checkpointing = apply_unsloth_gradient_checkpointing(
+            use_gradient_checkpointing, max_seq_length, dtype
+        )
 
         # Check if this is local model since the tokenizer gets overwritten
         if (
@@ -1188,9 +1190,10 @@ class FastModel(FastBaseModel):
                 os.environ["UNSLOTH_FORCE_FLOAT32"] = "1"
                 dtype = torch.bfloat16  # Change to bfloat16 loading
                 break
-        # Patch gradient checkpointing
-        if use_gradient_checkpointing == "unsloth":
-            patch_unsloth_smart_gradient_checkpointing(dtype = dtype)
+        # Apply gradient checkpointing with smart heuristics
+        use_gradient_checkpointing = apply_unsloth_gradient_checkpointing(
+            use_gradient_checkpointing, max_seq_length, dtype
+        )
         with redirector:
             patch_loss_functions(torch_compile = False)
             model_types, supports_sdpa = unsloth_compile_transformers(