diff --git a/tests/pytorch/test_numerics.py b/tests/pytorch/test_numerics.py
index a718ea2a8a..a682fb2f19 100644
--- a/tests/pytorch/test_numerics.py
+++ b/tests/pytorch/test_numerics.py
@@ -2797,6 +2797,60 @@ def test_grouped_gemm(shape, dtype, layout, accumulate, use_cutlass):
         os.environ.pop("NVTE_USE_CUTLASS_GROUPED_GEMM", None)
 
 
+@pytest.mark.skipif(
+    torch.cuda.get_device_capability() != (9, 0),
+    reason="Only enable CUTLASS grouped gemm on Hopper",
+)
+@pytest.mark.parametrize("layout", ["TN", "NN", "NT"])
+def test_grouped_gemm_cutlass_empty_groups(layout):
+    dtype = torch.bfloat16
+    z, k, n = 1, 2048, 1536
+    m_splits = [0] * z
+
+    if layout == "TN":
+        A = [torch.randn(n, k, dtype=dtype, device="cuda") for _ in range(z)]  # weight
+        B = [torch.empty(0, k, dtype=dtype, device="cuda") for _ in range(z)]  # input
+        out = [torch.empty(0, n, dtype=dtype, device="cuda")]  # output
+        grad = False
+        single_output = True
+    elif layout == "NN":
+        A = [torch.randn(n, k, dtype=dtype, device="cuda") for _ in range(z)]  # weight
+        B = [torch.empty(0, n, dtype=dtype, device="cuda") for _ in range(z)]  # grad_output
+        out = [torch.empty(0, k, dtype=dtype, device="cuda")]  # dgrad
+        grad = True
+        single_output = True
+    else:
+        A = [torch.empty(0, k, dtype=dtype, device="cuda") for _ in range(z)]  # input
+        B = [torch.empty(0, n, dtype=dtype, device="cuda") for _ in range(z)]  # grad_output
+        out = [torch.randn(n, k, dtype=dtype, device="cuda") for _ in range(z)]  # wgrad
+        grad = True
+        single_output = False
+
+    old_cutlass_env = os.environ.get("NVTE_USE_CUTLASS_GROUPED_GEMM")
+    os.environ["NVTE_USE_CUTLASS_GROUPED_GEMM"] = "1"
+    try:
+        general_grouped_gemm(
+            A,
+            B,
+            out,
+            [None] * z,
+            dtype,
+            m_splits=m_splits,
+            grad=grad,
+            layout=layout,
+            single_output=single_output,
+        )
+        torch.cuda.synchronize()
+    finally:
+        if old_cutlass_env is None:
+            os.environ.pop("NVTE_USE_CUTLASS_GROUPED_GEMM", None)
+        else:
+            os.environ["NVTE_USE_CUTLASS_GROUPED_GEMM"] = old_cutlass_env
+
+    for tensor in out:
+        torch.testing.assert_close(tensor, torch.zeros_like(tensor), rtol=0, atol=0)
+
+
 def _pack_grouped_tensor(grouped_tensor: GroupedTensor, tensors: List[torch.Tensor]) -> None:
     data = grouped_tensor.rowwise_data
     if data is None:
diff --git a/transformer_engine/common/gemm/cublaslt_gemm.cu b/transformer_engine/common/gemm/cublaslt_gemm.cu
index 8589d7045d..68efd9793b 100644
--- a/transformer_engine/common/gemm/cublaslt_gemm.cu
+++ b/transformer_engine/common/gemm/cublaslt_gemm.cu
@@ -1058,6 +1058,10 @@ void nvte_multi_tensor_gemm(const NVTETensor *A, const NVTETensor *B, NVTETensor
                             cudaStream_t stream) {
   NVTE_API_CALL(nvte_multi_tensor_gemm);
 
+  if (num_gemms <= 0) {
+    return;
+  }
+
   const int current_device = transformer_engine::cuda::current_device();
   const bool is_hopper = (transformer_engine::cuda::sm_arch(current_device) == 90);
   const bool use_cutlass = transformer_engine::getenv<bool>("NVTE_USE_CUTLASS_GROUPED_GEMM", false);
diff --git a/transformer_engine/pytorch/csrc/extensions/gemm.cpp b/transformer_engine/pytorch/csrc/extensions/gemm.cpp
index 9cb1fb7f54..dd1ad905fd 100644
--- a/transformer_engine/pytorch/csrc/extensions/gemm.cpp
+++ b/transformer_engine/pytorch/csrc/extensions/gemm.cpp
@@ -535,6 +535,10 @@ std::optional<std::vector<at::Tensor>> te_general_grouped_gemm(
     te_pre_gelu_out_wrappers.emplace_back(std::move(te_pre_gelu_out));
   }
 
+  if (te_A_wrappers.empty()) {
+    return bias;
+  }
+
   // Keep the swizzled scaling factor tensors alive during the GEMM.
   std::vector<std::optional<at::Tensor>> swizzled_scale_inverses_list;