diff --git a/unsloth/models/vision.py b/unsloth/models/vision.py
index a8adba99e7..5db65115a3 100644
--- a/unsloth/models/vision.py
+++ b/unsloth/models/vision.py
@@ -1343,18 +1343,130 @@ def post_patch_model(
         # See: https://github.com/unslothai/unsloth/issues/3713.
         use_reentrant = not is_distributed()
         if not use_reentrant:
-            # Under DDP, avoid the offloaded/re-entrant checkpoint patch.
+            # Under DDP, switch to non-reentrant checkpointing with CPU
+            # activation offloading via saved_tensors_hooks. This preserves
+            # memory savings from offloading while being DDP-safe.
             unpatch_unsloth_gradient_checkpointing()
             unpatch_unsloth_smart_gradient_checkpointing()
-            # Force native checkpoint to default to non-reentrant for downstream calls.
             _orig_checkpoint = torch_checkpoint.checkpoint
 
-            def _nonre_checkpoint(function, *args, **kwargs):
+            def _nonre_offloaded_checkpoint(function, *args, **kwargs):
                 kwargs["use_reentrant"] = False
-                return _orig_checkpoint(function, *args, **kwargs)
 
-            torch_checkpoint.checkpoint = _nonre_checkpoint
-            hf_modeling_utils.checkpoint = _nonre_checkpoint
+                def _pack(x):
+                    if isinstance(x, torch.Tensor) and x.device.type != "cpu":
+                        return ("offload", x.device, x.to("cpu", non_blocking = True))
+                    return ("pass", x)
+
+                def _unpack(packed):
+                    if packed[0] == "offload":
+                        return packed[2].to(packed[1], non_blocking = True)
+                    return packed[1]
+
+                with torch.autograd.graph.saved_tensors_hooks(_pack, _unpack):
+                    return _orig_checkpoint(function, *args, **kwargs)
+
+            torch_checkpoint.checkpoint = _nonre_offloaded_checkpoint
+            hf_modeling_utils.checkpoint = _nonre_offloaded_checkpoint
+
+            # Fix TiledMLP for DDP: its backward calls torch.autograd.backward()
+            # per sequence chunk, triggering DDP hooks multiple times. Patch it
+            # to use functional torch.autograd.grad() for all but the last chunk,
+            # then .backward() for the final chunk (fires DDP hooks exactly once).
+            from unsloth_zoo.tiled_mlp import TiledMLP, torch_amp_custom_bwd
+            from unsloth_zoo.gradient_checkpointing import (
+                set_device_states as _set_dev_states,
+            )
+
+            @staticmethod
+            @torch_amp_custom_bwd
+            def _ddp_safe_backward(ctx, grad_output, *args):
+                rng_devices = []
+                x = ctx.saved_tensors[0]
+                B, S, H = x.shape
+                if ctx.preserve_rng_state and ctx.had_device_in_fwd:
+                    rng_devices = ctx.fwd_devices
+                with torch.random.fork_rng(
+                    devices = rng_devices,
+                    enabled = ctx.preserve_rng_state,
+                    device_type = ctx.device_type,
+                ):
+                    if ctx.preserve_rng_state:
+                        torch.set_rng_state(ctx.fwd_cpu_state)
+                        if ctx.had_device_in_fwd:
+                            _set_dev_states(
+                                ctx.fwd_devices,
+                                ctx.fwd_device_states,
+                                device_type = ctx.device_type,
+                            )
+
+                    mlp_params = [
+                        p for p in ctx.mlp_module.parameters() if p.requires_grad
+                    ]
+                    x_gradients = torch.zeros_like(
+                        x, memory_format = torch.preserve_format
+                    )
+                    x_flat = x.view(-1, H)
+                    x_splits = torch.split(x_flat, ctx.split_sizes, dim = 0)
+                    start_idx = 0
+                    extra_outputs = []
+                    n_chunks = len(x_splits)
+
+                    for i, x_split in enumerate(x_splits):
+                        x_split = x_split.unsqueeze(0)
+                        split_size = x_split.numel()
+                        x_grad_slice = (
+                            x_gradients.view(-1)
+                            .narrow(
+                                dim = 0,
+                                start = start_idx,
+                                length = split_size,
+                            )
+                            .view_as(x_split)
+                        )
+                        grad_shard = (
+                            grad_output.view(-1)
+                            .narrow(
+                                dim = 0,
+                                start = start_idx,
+                                length = split_size,
+                            )
+                            .view_as(x_split)
+                        )
+
+                        x_split.requires_grad_(True)
+                        with torch.enable_grad():
+                            outputs = TiledMLP.handle_output(
+                                ctx.mlp_forward(x_split),
+                                extra_outputs,
+                            )
+
+                        if i < n_chunks - 1:
+                            # Functional grad: no DDP hooks fired
+                            grads = torch.autograd.grad(
+                                outputs,
+                                [x_split] + mlp_params,
+                                grad_shard,
+                                allow_unused = True,
+                            )
+                            x_grad_slice.copy_(grads[0])
+                            for p, g in zip(mlp_params, grads[1:]):
+                                if g is not None:
+                                    if p.grad is None:
+                                        p.grad = g
+                                    else:
+                                        p.grad.add_(g)
+                        else:
+                            # Last chunk: .backward() fires DDP hooks once
+                            x_split.grad = x_grad_slice
+                            torch.autograd.backward(outputs, grad_shard)
+
+                        start_idx += split_size
+
+                return None, None, x_gradients, None, None, None
+
+            TiledMLP.backward = _ddp_safe_backward
+            logger.info("Unsloth: Patched TiledMLP backward for DDP compatibility")
 
         model = prepare_model_for_training(
             model,