diff --git a/python/tvm/relay/frontend/mxnet.py b/python/tvm/relay/frontend/mxnet.py
index b272ead9737d..0c9d2c4381ac 100644
--- a/python/tvm/relay/frontend/mxnet.py
+++ b/python/tvm/relay/frontend/mxnet.py
@@ -495,6 +495,19 @@ def _mx_layer_norm(inputs, attrs):
     return _op.nn.layer_norm(*inputs, **new_attrs)
 
 
+def _mx_group_norm(inputs, attrs):
+    assert len(inputs) == 3
+    if attrs.get_bool("output_mean_var", False):
+        raise tvm.error.OpAttributeUnimplemented(
+            'Attribute "output_mean_var" is not supported for operator Group Norm.'
+        )
+    new_attrs = {}
+    new_attrs["axis"] = 1
+    new_attrs["num_groups"] = attrs.get_int("num_groups", 1)
+    new_attrs["epsilon"] = attrs.get_float("eps", 1e-5)
+    return _op.nn.group_norm(*inputs, **new_attrs)
+
+
 def _mx_slice(inputs, attrs):
     new_attrs = {}
     begin = list(attrs.get_int_tuple("begin", None))
@@ -2599,6 +2612,7 @@ def _mx_npi_where_rscalar(inputs, attrs):
     "_contrib_SyncBatchNorm": _mx_batch_norm,
     "InstanceNorm": _mx_instance_norm,
     "LayerNorm": _mx_layer_norm,
+    "GroupNorm": _mx_group_norm,
     "LRN": _mx_lrn,
     "L2Normalization": _mx_l2_normalize,
     "slice": _mx_slice,
diff --git a/tests/python/frontend/mxnet/test_forward.py b/tests/python/frontend/mxnet/test_forward.py
index 537349e073e1..3e652cfc69e3 100644
--- a/tests/python/frontend/mxnet/test_forward.py
+++ b/tests/python/frontend/mxnet/test_forward.py
@@ -1263,6 +1263,38 @@ def verify(shape, axis=-1):
     verify((2, 5, 6))
 
 
+@tvm.testing.uses_gpu
+def test_forward_group_norm():
+    def verify(shape, num_groups=1):
+        x = np.random.uniform(size=shape).astype("float32")
+        gamma = np.random.uniform(size=(shape[1])).astype("float32")
+        beta = np.random.uniform(size=(shape[1])).astype("float32")
+        ref_res = mx.nd.GroupNorm(
+            data=mx.nd.array(x),
+            gamma=mx.nd.array(gamma),
+            beta=mx.nd.array(beta),
+            num_groups=num_groups,
+        )
+        mx_sym = mx.sym.GroupNorm(
+            mx.sym.var("x"), mx.sym.var("gamma"), mx.sym.var("beta"), num_groups=num_groups
+        )
+        shape_dict = {"x": x.shape, "gamma": gamma.shape, "beta": beta.shape}
+        mod, _ = relay.frontend.from_mxnet(mx_sym, shape_dict)
+        for target, ctx in tvm.testing.enabled_targets():
+            for kind in ["graph", "debug"]:
+                intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
+                op_res = intrp.evaluate()(x, gamma, beta)
+                tvm.testing.assert_allclose(
+                    op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-3, atol=1e-5
+                )
+
+    verify((1, 4, 2), num_groups=4)
+    # TODO(trevmorr): MXNet GroupNorm implementation is bugged for cases when num_groups != num_channels
+    # https://github.com/apache/incubator-mxnet/pull/18199
+    # verify((1, 4, 2, 3), num_groups=2)
+    # verify((1, 4, 2, 3))
+
+
 @tvm.testing.uses_gpu
 def test_forward_one_hot():
     def verify(indices_shape, depth, on_value, off_value, dtype):