diff --git a/docs/api/pytorch.rst b/docs/api/pytorch.rst
index 99d850d04d..1612b3234b 100644
--- a/docs/api/pytorch.rst
+++ b/docs/api/pytorch.rst
@@ -55,6 +55,10 @@ PyTorch
 
 .. autoapifunction:: transformer_engine.pytorch.parallel_cross_entropy
 
+.. autoapifunction:: transformer_engine.pytorch.interleave_glu_tensor
+
+.. autoapifunction:: transformer_engine.pytorch.deinterleave_glu_tensor
+
 Recipe availability
 -------------------
 
diff --git a/transformer_engine/pytorch/__init__.py b/transformer_engine/pytorch/__init__.py
index 7653d5992e..775200ce01 100644
--- a/transformer_engine/pytorch/__init__.py
+++ b/transformer_engine/pytorch/__init__.py
@@ -54,6 +54,8 @@
 from transformer_engine.pytorch.utils import get_cudnn_version
 from transformer_engine.pytorch.utils import get_device_compute_capability
 from transformer_engine.pytorch.utils import is_bf16_available
+from transformer_engine.pytorch.utils import deinterleave_glu_tensor
+from transformer_engine.pytorch.utils import interleave_glu_tensor
 from transformer_engine.pytorch.graph import make_graphed_callables
 from transformer_engine.pytorch.distributed import checkpoint
 from transformer_engine.pytorch.distributed import CudaRNGStatesTracker
diff --git a/transformer_engine/pytorch/utils.py b/transformer_engine/pytorch/utils.py
index fd8f817b33..cfb21e7bff 100644
--- a/transformer_engine/pytorch/utils.py
+++ b/transformer_engine/pytorch/utils.py
@@ -17,7 +17,13 @@
 from ..debug.pytorch.debug_quantization import DebugQuantizedTensor
 
 
-__all__ = ["get_device_compute_capability", "get_cudnn_version", "is_bf16_available"]
+__all__ = [
+    "get_device_compute_capability",
+    "get_cudnn_version",
+    "is_bf16_available",
+    "deinterleave_glu_tensor",
+    "interleave_glu_tensor",
+]
 
 
 @functools.lru_cache(maxsize=None)
@@ -82,6 +88,103 @@ def get_device_compute_capability() -> Tuple[int, int]:
     return _get_device_compute_capability(torch.cuda.current_device())
 
 
+def deinterleave_glu_tensor(tensor: torch.Tensor, interleave_size: int) -> torch.Tensor:
+    """Convert a block-interleaved GLU fc1 tensor to contiguous gate/linear layout.
+
+    Fused GLU kernels (for example :class:`~transformer_engine.pytorch.ops.SwiGLU`
+    with ``glu_interleave_size`` set) expect fc1 weights in a block-interleaved
+    layout along dimension 0. Checkpoints and frameworks such as Megatron-LM typically
+    store the gate (``W``) and linear (``V``) halves as two contiguous blocks
+    ``[W_all, V_all]``. This helper reorders along dimension 0 without changing
+    the total shape.
+
+    **Layouts along dimension 0** (``k = interleave_size``):
+
+    * **Block-interleaved (input):** ``[W0:k, V0:k, Wk:2k, Vk:2k, ...]``
+    * **Contiguous (output):** ``[W_all, V_all]``
+
+    The same convention applies to ``linear_fc1.weight`` (dimension 0 plus any
+    remaining dimensions) and ``linear_fc1.bias`` (dimension 0 only).
+
+    Parameters
+    ----------
+    tensor : torch.Tensor
+        Tensor in block-interleaved layout. The length of dimension 0 must be
+        divisible by ``2 * interleave_size``.
+    interleave_size : int
+        Number of rows (for weights) or elements (for bias) per gate/linear block.
+        Fused TE GLU paths commonly use ``32``.
+
+    Returns
+    -------
+    torch.Tensor
+        A new tensor with the same shape as ``tensor`` and contiguous
+        ``[W_all, V_all]`` ordering along dimension 0.
+
+    See Also
+    --------
+    :func:`interleave_glu_tensor` : Inverse transformation (contiguous -> block-interleaved).
+    """
+    shape = tensor.shape
+    x = tensor.reshape(
+        shape[0] // (2 * interleave_size),
+        2,
+        interleave_size,
+        *shape[1:],
+    )
+    x = x.transpose(0, 1).contiguous()
+    return x.reshape(shape)
+
+
+def interleave_glu_tensor(tensor: torch.Tensor, interleave_size: int) -> torch.Tensor:
+    """Convert a contiguous SwiGLU fc1 tensor to block-interleaved layout.
+
+    This is the inverse of :func:`deinterleave_glu_tensor`. Use it when loading
+    contiguous ``[W_all, V_all]`` checkpoints into a module that uses fused
+    interleaved SwiGLU (``glu_interleave_size`` on the activation op).
+
+    **Layouts along dimension 0** (``k = interleave_size``):
+
+    * **Contiguous (input):** ``[W_all, V_all]``
+    * **Block-interleaved (output):** ``[W0:k, V0:k, Wk:2k, Vk:2k, ...]``
+
+    Parameters
+    ----------
+    tensor : torch.Tensor
+        Tensor in contiguous gate/linear layout. The length of dimension 0 must be
+        divisible by ``2 * interleave_size``.
+    interleave_size : int
+        Number of rows (for weights) or elements (for bias) per gate/linear block.
+        Must match the ``glu_interleave_size`` used by the fused SwiGLU op.
+
+    Returns
+    -------
+    torch.Tensor
+        A new tensor with the same shape as ``tensor`` and block-interleaved
+        ordering along dimension 0.
+
+    See Also
+    --------
+    :func:`deinterleave_glu_tensor` : Inverse transformation (block-interleaved -> contiguous).
+    """
+    if interleave_size <= 0:
+        raise ValueError(f"interleave_size must be a positive integer, got {interleave_size}")
+    if tensor.shape[0] % (2 * interleave_size) != 0:
+        raise ValueError(
+            f"tensor dimension 0 ({tensor.shape[0]}) must be divisible by "
+            f"2 * interleave_size ({2 * interleave_size})"
+        )
+    shape = tensor.shape
+    x = tensor.reshape(
+        2,
+        shape[0] // (2 * interleave_size),
+        interleave_size,
+        *shape[1:],
+    )
+    x = x.transpose(0, 1).contiguous()
+    return x.reshape(shape)
+
+
 def resolve_grouped_linear_single_param_flags(
     single_grouped_weight: bool,
     single_grouped_bias: bool,