diff --git a/examples/python/CuTeDSL/blackwell/dense_gemm_persistent.py b/examples/python/CuTeDSL/blackwell/dense_gemm_persistent.py
index d5b48f45..979609c3 100644
--- a/examples/python/CuTeDSL/blackwell/dense_gemm_persistent.py
+++ b/examples/python/CuTeDSL/blackwell/dense_gemm_persistent.py
@@ -1715,7 +1715,6 @@ def compile_bmm(
     max_active_clusters: cutlass.Constexpr,
     stream: cuda.CUstream,
     epilogue_op: cutlass.Constexpr = lambda x: x,
-    options: str = "",
 ):
     from cutlass.cute.runtime import make_fake_compact_tensor
 
@@ -1749,7 +1748,15 @@ def compile_bmm(
     )
 
     return cute.compile(
-        bmm, gemm_op, a, b, c, max_active_clusters, stream, epilogue_op, options=options
+        bmm,
+        gemm_op,
+        a,
+        b,
+        c,
+        max_active_clusters,
+        stream,
+        epilogue_op,
+        options="--enable-tvm-ffi",
     )
 
 
@@ -1811,7 +1818,6 @@ def run(
     iterations: int = 1,
     skip_ref_check: bool = False,
     use_cold_l2: bool = False,
-    use_tvm_ffi: bool = False,
     benchmark: bool = False,
     **kwargs,
 ):
@@ -1853,8 +1859,6 @@ def run(
     :type skip_ref_check: bool, optional
     :param use_cold_l2: Whether to use circular buffer strategy to ensure cold L2 cache, defaults to False.
     :type use_cold_l2: bool, optional
-    :param use_tvm_ffi: Whether to use TVM FFI for the kernel, defaults to False.
-    :type use_tvm_ffi: bool, optional
     :param benchmark: Whether to only benchmark the kernel, defaults to False.
     :type benchmark: bool, optional
     :raises RuntimeError: If CUDA GPU is not available.
@@ -1874,7 +1878,7 @@ def run(
     print(f"Iterations: {iterations}")
     print(f"Skip reference checking: {skip_ref_check}")
     print(f"Use cold L2: {'True' if use_cold_l2 else 'False'}")
-    print(f"Use TVM FFI: {'True' if use_tvm_ffi else 'False'}")
+    print(f"Use TVM FFI")
 
     import torch
     from cutlass.torch import dtype as torch_dtype
@@ -1906,10 +1910,6 @@ def run(
         cluster_shape_mn[0] * cluster_shape_mn[1]
     )
 
-    options = []
-    if use_tvm_ffi:
-        options.append("--enable-tvm-ffi")
-
     compiled_fn = compile_bmm(
         gemm,
         ab_dtype,
@@ -1920,7 +1920,6 @@ def run(
         c_major,
         max_active_clusters,
         current_stream,
-        options=",".join(options),
     )
 
     # Run and verify BMM with torch
@@ -2043,12 +2042,6 @@ def prepare_parser():
         default=False,
         help="Use circular buffer tensor sets to ensure L2 cold cache",
     )
-    parser.add_argument(
-        "--use_tvm_ffi",
-        action="store_true",
-        default=False,
-        help="Enable TVM FFI for the kernel, defaults to False using CuTe DSL's native runtime",
-    )
 
     return parser
 
@@ -2090,7 +2083,6 @@ if __name__ == "__main__":
         args.iterations,
         args.skip_ref_check,
         args.use_cold_l2,
-        args.use_tvm_ffi,
         args.benchmark,
     )
     print("PASS")
diff --git a/media/docs/pythonDSL/cute_dsl_general/compile_with_tvm_ffi.rst b/media/docs/pythonDSL/cute_dsl_general/compile_with_tvm_ffi.rst
index 42d0fdea..2b8a8bc5 100644
--- a/media/docs/pythonDSL/cute_dsl_general/compile_with_tvm_ffi.rst
+++ b/media/docs/pythonDSL/cute_dsl_general/compile_with_tvm_ffi.rst
@@ -4,7 +4,8 @@
 Compile with TVM FFI
 ====================
 
-Apache TVM FFI is an open ABI and FFI for machine learning systems. More information can be found in the `official documentation <https://tvm.apache.org/ffi/>`_.
+Apache TVM FFI is an open ABI and FFI for machine learning systems. More information can be found in 
+the `official documentation <https://tvm.apache.org/ffi/>`_.
 
 To install TVM FFI, you can run the following command:
 
@@ -14,7 +15,9 @@ To install TVM FFI, you can run the following command:
    # optional package for improved torch tensor calling performance
    pip install torch-c-dlpack-ext
 
-In |DSL|, TVM FFI can be enabled as an option for JIT-compiled functions. Using TVM FFI can lead to faster JIT function invocation and provides better interoperability with machine learning frameworks (e.g., directly take ``torch.Tensor`` as arguments).
+In |DSL|, TVM FFI can be enabled as an option for JIT-compiled functions. Using TVM FFI can lead to faster 
+JIT function invocation and provides better interoperability with machine learning frameworks 
+(e.g., directly take ``torch.Tensor`` as arguments).
 
 
 Enable Apache TVM FFI in |DSL|
@@ -40,7 +43,8 @@ There are two ways to enable TVM FFI in |DSL|:
 
 Note that the object returned by ``cute.compile`` is a Python function specific to TVM FFI.
 
-2. Alternatively, you can enable TVM FFI globally by setting the environment variable ``CUTE_DSL_ENABLE_TVM_FFI=1``. Please note that this setting will apply to all JIT compilations within the environment.
+2. Alternatively, you can enable TVM FFI globally by setting the environment variable ``CUTE_DSL_ENABLE_TVM_FFI=1``. 
+Please note that this setting will apply to all JIT compilations within the environment.
 
 
 Minimizing Host Overhead
@@ -129,7 +133,8 @@ stride via the ``stride`` argument in the ``make_fake_tensor`` API.
 ``cute.Tensor`` adapter for TVM FFI
 -----------------------------------
 
-To adapt the ``cute.Tensor`` to the TVM FFI function, you can use the ``cute.runtime.from_dlpack`` function with the ``enable_tvm_ffi=True`` option or the environment variable ``CUTE_DSL_ENABLE_TVM_FFI=1``. For example:
+To adapt the ``cute.Tensor`` to the TVM FFI function, you can use the ``cute.runtime.from_dlpack`` function with the 
+``enable_tvm_ffi=True`` option or the environment variable ``CUTE_DSL_ENABLE_TVM_FFI=1``. For example:
 
 .. code-block:: python
 
@@ -288,6 +293,33 @@ composed of the types that are supported by TVM FFI. The example below shows how
    example_add_one_with_tuple()
 
 
+Limitations
+-----------
+
+The Fake Tensor flow is ONLY compatible with TVM FFI because TVM FFI support more flexible constraints on Tensor arguments.
+For instance, fake tensor can specify per-mode static shape or constraints on shape and strides which is not supported by 
+existing `from_dlpack` flow. It's expected that JIT function compiled with fake tensor may have different ABI with
+tensor converted by `from_dlpack`.
+
+.. code-block:: python
+
+   import cutlass.cute as cute
+   import torch
+
+   n = cute.sym_int()
+   # Dynamic Shape
+   fake_a = cute.runtime.make_fake_compact_tensor(cute.Float32, (n,))
+
+   # Compile without tvm-ffi
+   compiled_fn = cute.compile(foo, fake_a)
+
+   # Wrong, in compatible ABI
+   compiled_fn(from_dlpack(a))
+
+
+In order to avoid such issue, it's recommended fake tensor is only used with TVM FFI.
+
+
 Supported types
 ---------------