diff --git a/python/cudnn/gemm/cutedsl/grouped/unfused/jax_api.py b/python/cudnn/gemm/cutedsl/grouped/unfused/jax_api.py
index 259549402..b8fb7fbf6 100644
--- a/python/cudnn/gemm/cutedsl/grouped/unfused/jax_api.py
+++ b/python/cudnn/gemm/cutedsl/grouped/unfused/jax_api.py
@@ -28,7 +28,7 @@
 
 from cudnn.datatypes import _convert_to_cutlass_data_type
 from cudnn.tensor_adapter import framework_dtype
-from cudnn.jax import TensorSpec, call, gemm_operand_spec, zeros_init
+from cudnn.jax import TensorSpec, call, gemm_operand_spec
 from ..moe_utils import MoEWeightMode
 from .moe_grouped_gemm import MoEGroupedGemmBf16Kernel
 
@@ -108,9 +108,8 @@ def grouped_gemm_jax_sm100(
     row offsets, ``alpha (experts,)`` float32, ``prob (m, 1, 1)`` float32, and
     ``b_ptrs`` holding per-expert ``(n, k)`` k-major bfloat16 weight base addresses
     (packed little-endian uint8, 8 bytes per pointer — or int64 with x64 mode).
-    Returns ``(d_tensor, c_tensor)`` with ``c_tensor`` None unless ``generate_c``;
-    rows at/past ``padded_offsets[-1]`` come back zero-filled (the outputs are
-    donated zero-initialized buffers).
+    Returns ``(d_tensor, c_tensor)`` with ``c_tensor`` None unless ``generate_c``.
+    Rows at/past ``padded_offsets[-1]`` are unspecified.
     """
     c_dtype = _convert_to_cutlass_data_type(c_dtype)
     d_dtype = _convert_to_cutlass_data_type(d_dtype)
@@ -207,10 +206,6 @@ def grouped_gemm_jax_sm100(
         ),
         input_spec=(operand, None, None, None, _prob_spec()),
         output_spec=(operand, operand, None),
-        # All three donated: c/d for the trailing-unit-dim layout spec (and defined
-        # bytes past the last offset); the workspace because the helper kernel writes
-        # the per-expert TMA descriptors into it (XLA inputs are immutable).
-        initialized_outputs={0: zeros_init, 1: zeros_init, 2: zeros_init},
         kernel=kernel,
         n=int(n),
         k=int(k),