diff --git a/comfy_kitchen/tensor/base.py b/comfy_kitchen/tensor/base.py
index 92e8778f..11ba712d 100644
--- a/comfy_kitchen/tensor/base.py
+++ b/comfy_kitchen/tensor/base.py
@@ -159,10 +159,14 @@ def __new__(
         qdata: torch.Tensor,
         layout_cls: str,
         params: Any,
+        outer_stride: Any = None,
     ):
+        # outer_stride is set by __tensor_unflatten__ under torch.compile dynamic shapes, where the
+        # rebuilt wrapper must report the exact (symbolic) stride PyTorch asserts. None -> contiguous.
         return torch.Tensor._make_wrapper_subclass(
             cls,
             params.orig_shape,
+            strides=outer_stride,
             device=qdata.device,
             dtype=params.orig_dtype,
             requires_grad=False,
@@ -173,6 +177,7 @@ def __init__(
         qdata: torch.Tensor,
         layout_cls: str,
         params: Any,
+        outer_stride: Any = None,
     ):
         assert isinstance(layout_cls, str)
         self._qdata = qdata
@@ -339,6 +344,16 @@ def __tensor_unflatten__(inner_tensors, ctx, outer_size, outer_stride):
             params_kwargs[field_name] = inner_tensors[attr_name]
 
         params = ctx["params_class"](**params_kwargs)
+        # torch.compile passes outer_size/outer_stride as sequences the rebuilt subclass must report
+        # exactly (asserted in torch._subclasses.meta_utils), and under dynamic shapes they can differ
+        # from the flatten-time orig_shape -- so adopt outer_size as orig_shape and forward the stride.
+        # Other callers (e.g. comfy.memory_management.interpret_gathered_like) pass scalar 0
+        # placeholders meaning "keep stored shape", so only act on real sequences.
+        if isinstance(outer_size, (tuple, list, torch.Size)):
+            params = dataclasses.replace(params, orig_shape=tuple(outer_size))
+            return QuantizedTensor(
+                inner_tensors["_qdata"], ctx["layout_cls"], params, outer_stride=outer_stride,
+            )
         return QuantizedTensor(inner_tensors["_qdata"], ctx["layout_cls"], params)
 
     # ==================== Torch Dispatch ====================
diff --git a/tests/test_unflatten_dynamic_shapes.py b/tests/test_unflatten_dynamic_shapes.py
new file mode 100644
index 00000000..f9c8cd50
--- /dev/null
+++ b/tests/test_unflatten_dynamic_shapes.py
@@ -0,0 +1,171 @@
+"""Regression tests: QuantizedTensor.__tensor_unflatten__ must honor outer_size/outer_stride.
+
+PyTorch's subclass fakeification (torch._subclasses.meta_utils) asserts that a tensor subclass
+rebuilt via __tensor_unflatten__ reports exactly the outer_size/outer_stride it was given. Under
+torch.compile with dynamic shapes these are (possibly symbolic, e.g. a fused activation row dim
+s31*s81) and differ from the static orig_shape captured at quantize time. Ignoring them raises:
+
+    AssertionError: Expected return value from ...__tensor_unflatten__() to have shape
+    equal to (s31, 2560), but got: torch.Size([s31*s81, 2560])
+
+which crashed mxfp8 models under torch.compile. All tests except TestUnflattenRealCompileCUDA are
+CPU-only and use eager-quantizable real layouts.
+"""
+import dataclasses
+
+import pytest
+import torch
+
+from comfy_kitchen.tensor import QuantizedTensor, get_layout_class
+from comfy_kitchen.tensor.base import register_layout_class
+
+# INT8 weight-quant and FP8 both have CPU code paths -> usable as real layouts on CPU.
+CPU_LAYOUTS = [
+    pytest.param("TensorWiseINT8Layout", id="int8"),
+    pytest.param("TensorCoreFP8Layout", id="fp8"),
+]
+
+
+def _require(layout_cls):
+    try:
+        get_layout_class(layout_cls)
+    except KeyError:
+        pytest.skip(f"{layout_cls} not registered in this build")
+
+
+def _flatten(qt):
+    names, ctx = qt.__tensor_flatten__()
+    return {n: getattr(qt, n) for n in names}, ctx
+
+
+class _DummyLayout:
+    pass
+
+
+register_layout_class("Dummy", _DummyLayout)
+
+
+class TestUnflattenOuterSizeCPU:
+    """Synthetic + real-layout coverage of the outer_size/outer_stride threading (CPU-only)."""
+
+    @dataclasses.dataclass(frozen=True)
+    class _DummyParams:
+        scale: torch.Tensor
+        orig_dtype: torch.dtype
+        orig_shape: tuple
+
+        def _tensor_fields(self):
+            return ["scale"]
+
+    def _dummy_qt(self):
+        return QuantizedTensor(
+            torch.zeros(8, 16, dtype=torch.uint8),
+            "Dummy",
+            self._DummyParams(scale=torch.ones(8, 1), orig_dtype=torch.bfloat16, orig_shape=(8, 16)),
+        )
+
+    def test_honors_concrete_outer_size_and_stride(self):
+        inner, ctx = _flatten(self._dummy_qt())
+        outer_size, outer_stride = (5, 16), (16, 1)
+        rebuilt = QuantizedTensor.__tensor_unflatten__(inner, ctx, outer_size, outer_stride)
+        assert tuple(rebuilt.shape) == outer_size
+        assert tuple(rebuilt.stride()) == outer_stride
+        # orig_shape must follow outer_size so layout slice-back stays consistent.
+        assert tuple(rebuilt._params.orig_shape) == outer_size
+
+    def test_scalar_placeholder_keeps_orig_shape(self):
+        # comfy.memory_management.interpret_gathered_like calls __tensor_unflatten__(actuals, ctx, 0, 0)
+        # meaning "keep the stored shape"; a scalar 0 must NOT be treated as a size.
+        inner, ctx = _flatten(self._dummy_qt())
+        rebuilt = QuantizedTensor.__tensor_unflatten__(inner, ctx, 0, 0)
+        assert tuple(rebuilt.shape) == (8, 16)
+
+    @pytest.mark.parametrize("layout_cls", CPU_LAYOUTS)
+    def test_eager_roundtrip_placeholder(self, layout_cls):
+        _require(layout_cls)
+        torch.manual_seed(0)
+        w = torch.randn(48, 96, dtype=torch.float32)
+        qt = QuantizedTensor.from_float(w, layout_cls)
+        inner, ctx = _flatten(qt)
+        rebuilt = QuantizedTensor.__tensor_unflatten__(inner, ctx, 0, 0)
+        assert tuple(rebuilt.shape) == tuple(qt.shape)
+        assert rebuilt._layout_cls == qt._layout_cls
+        assert torch.equal(rebuilt.dequantize(), qt.dequantize())
+
+
+class TestUnflattenSymbolicShapesCPU:
+    """The production trigger: rebuild a REAL-layout QuantizedTensor under FakeTensorMode with a
+    COMPOUND symbolic outer_size (s_b*s_s), exactly what automatic-dynamic produces. Drives the real
+    torch._subclasses.meta_utils path. FAILS before the fix (rebuilt reports static shape), passes
+    after. CPU-only."""
+
+    @pytest.mark.parametrize("layout_cls", CPU_LAYOUTS)
+    def test_unflatten_compound_symbolic_outer_size(self, layout_cls):
+        _require(layout_cls)
+        from torch._subclasses.fake_tensor import FakeTensorMode
+        from torch.fx.experimental.symbolic_shapes import ShapeEnv
+
+        torch.manual_seed(0)
+        w = torch.randn(64, 128, dtype=torch.float32)
+        qt = QuantizedTensor.from_float(w, layout_cls)
+        _, ctx = qt.__tensor_flatten__()
+        cols = qt.shape[1]
+
+        shape_env = ShapeEnv()
+        fake_mode = FakeTensorMode(shape_env=shape_env)
+        with fake_mode:
+            # Harvest two backed dynamic symbols (like s31, s81) from a dynamic helper tensor.
+            helper = torch.randn(2, 3)
+            torch._dynamo.maybe_mark_dynamic(helper, 0)
+            torch._dynamo.maybe_mark_dynamic(helper, 1)
+            fh = fake_mode.from_tensor(helper, symbolic_context=None)
+            compound = fh.shape[0] * fh.shape[1]  # e.g. s26*s49, like production s31*s81
+
+            inner = {n: fake_mode.from_tensor(getattr(qt, n)) for n in qt.__tensor_flatten__()[0]}
+            outer_size = (compound, cols)
+            outer_stride = (cols, 1)
+
+            rebuilt = QuantizedTensor.__tensor_unflatten__(inner, ctx, outer_size, outer_stride)
+
+            # This is exactly the assertion torch._subclasses.meta_utils makes internally.
+            assert tuple(rebuilt.shape) == tuple(outer_size)
+            assert tuple(rebuilt.stride()) == tuple(outer_stride)
+            assert tuple(rebuilt._params.orig_shape) == tuple(outer_size)
+
+
+@pytest.mark.cuda
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
+class TestUnflattenRealCompileCUDA:
+    """End-to-end production reproduction: torch.compile over a real-layout model run across TWO
+    different batch shapes forces automatic-dynamic, which fakeifies the QuantizedTensor with a
+    symbolic outer_size. Pre-fix this raises the meta_utils AssertionError. Requires a GPU whose
+    SM supports the layout's fast matmul (Blackwell SM12.0)."""
+
+    @pytest.mark.parametrize("layout_cls", ["TensorWiseINT8Layout", "TensorCoreFP8Layout"])
+    def test_compile_dynamic_two_shapes(self, layout_cls):
+        from comfy_kitchen.tensor import get_layout_class
+
+        lc = get_layout_class(layout_cls)
+        if not lc.supports_fast_matmul():
+            pytest.skip(f"{layout_cls} fast matmul unsupported on this GPU")
+
+        torch.manual_seed(0)
+        w = torch.randn(128, 64, device="cuda", dtype=torch.bfloat16)
+        qw = QuantizedTensor.from_float(w, layout_cls)
+
+        class M(torch.nn.Module):
+            def __init__(self, weight):
+                super().__init__()
+                self.w = weight
+
+            def forward(self, x):
+                # Runtime-quantize the activation -> the subclass carries the dynamic row dim.
+                xq = QuantizedTensor.from_float(x, layout_cls)
+                return torch.nn.functional.linear(xq, self.w)
+
+        cm = torch.compile(M(qw), dynamic=None)  # automatic-dynamic on 2nd shape
+        with torch.no_grad():
+            o1 = cm(torch.randn(8, 64, device="cuda", dtype=torch.bfloat16))
+            o2 = cm(torch.randn(16, 64, device="cuda", dtype=torch.bfloat16))  # recompile -> symbolic
+        assert o1.shape == (8, 128)
+        assert o2.shape == (16, 128)