diff --git a/examples/python/CuTeDSL/cute/tvm_ffi/fp4_x2_tensor.py b/examples/python/CuTeDSL/cute/tvm_ffi/fp4_x2_tensor.py
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+++ b/examples/python/CuTeDSL/cute/tvm_ffi/fp4_x2_tensor.py
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+# Copyright (c) 2025 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+"""Example demonstrating how to pass torch.float4_e2m1fn_x2 tensors to CuTe JIT functions.
+
+This example shows how to:
+1. Use make_fake_compact_tensor with Float4E2M1FN dtype for compilation
+2. Compile the function with "--enable-tvm-ffi" option
+3. Pass torch tensors with dtype=float4_e2m1fn_x2 to the compiled function
+4. Use recast_tensor to reinterpret a Uint8 tensor as Float4E2M1FN
+
+Note: Float4E2M1FN is a 4-bit floating point type (2-bit exponent, 1-bit mantissa).
+In PyTorch, this is represented as torch.float4_e2m1fn_x2, where two float4 values
+are packed into a single byte. The tensor is stored as uint8 and viewed as float4_e2m1fn_x2.
+
+To run this example:
+
+.. code-block:: bash
+
+    python examples/python/CuTeDSL/cute/tvm_ffi/fp4_x2_tensor.py
+"""
+
+import torch
+
+import cutlass
+import cutlass.cute as cute
+from cutlass.cute.runtime import make_fake_compact_tensor
+
+
+@cute.kernel
+def print_fp4_x2_tensor_info_kernel(t_f4: cute.Tensor, t_uint8: cute.Tensor):
+    print("[Compile INFO] Float4E2M1FN:", t_f4)
+    print("[Compile INFO] Uint8:", t_uint8)
+    t_f4_casted = cute.recast_tensor(t_uint8, cutlass.Float4E2M1FN)
+    print("[Compile INFO] Float4E2M1FN casted from Uint8:", t_f4_casted)
+
+
+@cute.jit
+def print_fp4_x2_tensor_info(t_f4: cute.Tensor, t_uint8: cute.Tensor):
+    """Process a fp4_x2 tensor - prints its layout information.
+
+    Note: Float4E2M1FN is a sub-byte type (4-bit), so individual element
+    dereference operations are not supported. This function demonstrates
+    passing float4 tensors through the TVM-FFI interface.
+    """
+    print_fp4_x2_tensor_info_kernel(t_f4, t_uint8).launch(grid=(1, 1, 1), block=(1, 1, 1))
+
+
+def torch_float4_x2_tensor():
+    """Demonstrate passing torch.float4_e2m1fn_x2 tensors to compiled function."""
+    print("=" * 60)
+    print("Pass torch.float4_e2m1fn_x2 tensor to compiled function")
+    print("=" * 60)
+
+    if not torch.cuda.is_available():
+        print("CUDA not available, skipping runtime example")
+        return
+
+    m = cute.sym_int()
+    # float4_e2m1fn_x2 packs two 4-bit values per byte, so the float4
+    # dimension must be even.
+    k_f4 = cute.sym_int(divisibility=2)
+    # The uint8 dimension is half the float4 dimension (1 byte = 2 float4 values).
+    k_uint8 = cute.sym_int()
+    fake_tensor_f4 = make_fake_compact_tensor(
+        cutlass.Float4E2M1FN,
+        (m, k_f4),
+        stride_order=(1, 0),
+        assumed_align=16,
+    )
+    fake_tensor_uint8 = make_fake_compact_tensor(
+        cutlass.Uint8,
+        (m, k_uint8),
+        stride_order=(1, 0),
+        assumed_align=16,
+    )
+
+    print(f"[Compile INFO] Compiling function for Float4E2M1FN tensor")
+
+    compiled_fn = cute.compile(
+        print_fp4_x2_tensor_info, fake_tensor_f4, fake_tensor_uint8, options="--enable-tvm-ffi"
+    )
+
+    tensor_uint8 = torch.randint(0, 256, (16, 16), dtype=torch.uint8, device="cuda")
+    tensor_f4 = tensor_uint8.view(torch.float4_e2m1fn_x2)
+
+    print(f"\n[Runtime INFO] Created torch tensor:")
+    print(f"  Underlying uint8 shape: {tensor_uint8.shape}")
+    print(f"  Float4 view shape: {tensor_f4.shape}")
+    print(f"  Device: {tensor_f4.device}")
+
+    print("\n[Runtime INFO] Calling compiled function with float4 tensor...")
+    # TVM-FFI allows passing torch tensors directly (no DLPack conversion needed).
+    compiled_fn(tensor_f4, tensor_uint8)
+    torch.cuda.synchronize()
+
+    print("[Runtime INFO] Function call completed successfully!")
+
+
+if __name__ == "__main__":
+    torch_float4_x2_tensor()