diff --git a/transformer_engine/common/normalization/common.cpp b/transformer_engine/common/normalization/common.cpp
index 11f12775c5..7dd942b314 100644
--- a/transformer_engine/common/normalization/common.cpp
+++ b/transformer_engine/common/normalization/common.cpp
@@ -395,6 +395,23 @@ CudnnNormalizationPlan::CudnnNormalizationPlan(NVTE_Norm_Type NormType, NVTE_Nor
       std::tie(_dx, _dgamma, _dbeta) = std::make_tuple(ret[0], ret[1], ret[2]);
       if (_dbeta != nullptr) NVTE_ERROR("cuDNN rmsnorm dbias incorrectly returned.");
     }
+    // Fuse the add for BackwardAdd stage
+    if (_norm_stage == NVTE_Norm_Stage::BackwardAdd) {
+      NVTE_CHECK(cudnnGetVersion() >= 92100,
+                 "Fused BackwardAdd requires cuDNN >= 9.21.0, but found ", cudnnGetVersion());
+
+      _add = _graph.tensor(fe::graph::Tensor_attributes()
+                               .set_name("add")
+                               .set_dim({batch_dim, hidden_dim, 1, 1})
+                               .set_stride({hidden_dim, 1, hidden_dim, hidden_dim})
+                               .set_data_type(get_cudnn_fe_dtype(wtype)));
+      auto add_options = fe::graph::Pointwise_attributes()
+                             .set_mode(fe::PointwiseMode_t::ADD)
+                             .set_compute_data_type(get_cudnn_fe_dtype(ctype));
+      auto _dx_with_add = _graph.pointwise(_dx, _add, add_options);
+      _dx->set_output(false).set_data_type(get_cudnn_fe_dtype(itype));
+      _dx = _dx_with_add;
+    }
     _dx->set_output(true).set_data_type(get_cudnn_fe_dtype(otype));
     _dgamma->set_output(true).set_data_type(get_cudnn_fe_dtype(otype));
   }
@@ -467,13 +484,16 @@ void CudnnNormalizationPlan::execute(void* x_dptr, void* gamma_dptr, void* mean_
                                      void* rsigma_dptr, void* dx_dptr, void* dz_dptr,
                                      void* add_dptr, void* dbeta_dptr, void* dgamma_dptr,
                                      void* workspace_dptr, cudaStream_t stream) {
-  // cuDNN does not currently support fused backward+add
-  NVTE_CHECK(add_dptr == nullptr);
-
   // Binding data pointers to graph tensors
   _variant_pack = {
       {_x, x_dptr}, {_rsigma, rsigma_dptr}, {_dz, dz_dptr}, {_dgamma, dgamma_dptr}, {_dx, dx_dptr}};
 
+  // Bind the add tensor for fused backward+add
+  if (_norm_stage == NVTE_Norm_Stage::BackwardAdd) {
+    NVTE_CHECK(add_dptr != nullptr, "add_dptr must not be null for BackwardAdd");
+    _variant_pack.insert({{_add, add_dptr}});
+  }
+
   if (_zero_centered)
     _variant_pack.insert({{_scalar_offset, reinterpret_cast<void*>(this->_scalar_dptr.get())},
                           {_gamma_zero, gamma_dptr}});
diff --git a/transformer_engine/common/normalization/common.h b/transformer_engine/common/normalization/common.h
index 79de2ac140..0cbd5a99f9 100644
--- a/transformer_engine/common/normalization/common.h
+++ b/transformer_engine/common/normalization/common.h
@@ -294,7 +294,7 @@ class CudnnNormalizationPlan : public NormalizationPlanBase {
   std::shared_ptr<fe::graph::Tensor_attributes> _z_mx_row, _z_mx_col, _sf_row, _sf_col;
   const bool _training;
   // BWD
-  std::shared_ptr<fe::graph::Tensor_attributes> _dz, _dx, _dgamma, _dbeta;
+  std::shared_ptr<fe::graph::Tensor_attributes> _dz, _dx, _dgamma, _dbeta, _add;
 
   fe::graph::Graph _graph;
   std::unordered_map<std::shared_ptr<fe::graph::Tensor_attributes>, void*> _variant_pack;
diff --git a/transformer_engine/common/normalization/rmsnorm/rmsnorm_api.cpp b/transformer_engine/common/normalization/rmsnorm/rmsnorm_api.cpp
index 6f6656534a..adf2ccee04 100644
--- a/transformer_engine/common/normalization/rmsnorm/rmsnorm_api.cpp
+++ b/transformer_engine/common/normalization/rmsnorm/rmsnorm_api.cpp
@@ -206,16 +206,21 @@ void rmsnorm_bwd_add(const Tensor &dz, const Tensor &x, const Tensor &add, const
     CheckOutputTensor(*dgamma, "dgamma");
   }
 
-  // cuDNN does not currently support fused backward+add
-  NVTE_Norm_Backend norm_backend = NVTE_Norm_Backend::Te;
-
-  // TE backend does not currently support zero_centered_gamma_in_weight_dtype
-  NVTE_CHECK(!use_zero_centered_gamma_in_weight_dtype(),
-             "zero_centered_gamma_in_weight_dtype is currently not supported for rmsnorm_bwd_add");
-
-  bool is_aligned = is_ptr_aligned(x.data.dptr, gamma.data.dptr, rsigma.data.dptr, dx->data.dptr,
-                                   dz.data.dptr, dgamma->data.dptr, add.data.dptr);
+  NVTE_Norm_Backend norm_backend;
+  bool is_aligned = true;
   bool gamma_in_weight_dtype = false;
+  if (use_cudnn_norm_bwd()) {
+    norm_backend = NVTE_Norm_Backend::Cudnn;
+    gamma_in_weight_dtype = use_zero_centered_gamma_in_weight_dtype();
+  } else {
+    norm_backend = NVTE_Norm_Backend::Te;
+    // TE backend does not currently support zero_centered_gamma_in_weight_dtype
+    NVTE_CHECK(!use_zero_centered_gamma_in_weight_dtype(),
+               "zero_centered_gamma_in_weight_dtype is currently not supported "
+               "for rmsnorm_bwd_add with TE backend");
+    is_aligned = is_ptr_aligned(x.data.dptr, gamma.data.dptr, rsigma.data.dptr, dx->data.dptr,
+                                dz.data.dptr, dgamma->data.dptr, add.data.dptr);
+  }
 
   auto plan = NormalizationPlanRegistry::getInstance().getNormalizationPlan(
       norm_backend, NVTE_Norm_Type::RMSNorm, NVTE_Norm_Stage::BackwardAdd,