diff --git a/tests/pytorch/test_grouped_linear.py b/tests/pytorch/test_grouped_linear.py
index 0dc253c18c..d51796046c 100644
--- a/tests/pytorch/test_grouped_linear.py
+++ b/tests/pytorch/test_grouped_linear.py
@@ -866,6 +866,53 @@ def test_grouped_gemm(shape, dtype, layout, accumulate, use_cutlass, monkeypatch
             torch.testing.assert_close(o, o_ref, rtol=1.5e-2, atol=1.5e-2)
 
 
+@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, monkeypatch):
+    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:  # layout == "NT"
+        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
+
+    monkeypatch.setenv("NVTE_USE_CUTLASS_GROUPED_GEMM", "1")
+    general_grouped_gemm(
+        A,
+        B,
+        out,
+        [None] * z,
+        dtype,
+        m_splits=m_splits,
+        grad=grad,
+        layout=layout,
+        single_output=single_output,
+    )
+    torch.cuda.synchronize()
+
+    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 e59e9c00c9..a0529c80c0 100644
--- a/transformer_engine/common/gemm/cublaslt_gemm.cu
+++ b/transformer_engine/common/gemm/cublaslt_gemm.cu
@@ -1056,6 +1056,10 @@ 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 53ea76d83b..47ece517b4 100644
--- a/transformer_engine/pytorch/csrc/extensions/gemm.cpp
+++ b/transformer_engine/pytorch/csrc/extensions/gemm.cpp
@@ -538,6 +538,10 @@ 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;