diff --git a/tests/unit_tests/transformer/moe/test_latent_moe_layer.py b/tests/unit_tests/transformer/moe/test_latent_moe_layer.py
new file mode 100644
index 00000000000..f62de67860a
--- /dev/null
+++ b/tests/unit_tests/transformer/moe/test_latent_moe_layer.py
@@ -0,0 +1,104 @@
+# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
+
+import pytest
+import torch
+
+from megatron.core.models.gpt.gpt_layer_specs import (
+    get_gpt_decoder_block_spec,
+    get_gpt_layer_local_spec,
+    get_gpt_layer_with_transformer_engine_spec,
+)
+from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
+from megatron.core.transformer.moe.moe_layer import MoELayer
+from megatron.core.transformer.moe.router import Router
+from megatron.core.transformer.transformer_block import TransformerBlock
+from megatron.core.transformer.transformer_config import TransformerConfig
+from megatron.core.utils import is_te_min_version
+from megatron.training.initialize import _set_random_seed
+from tests.unit_tests.test_utilities import Utils
+
+
+class TestLatentMoELayer:
+    def setup_method(self, method):
+        pass
+
+    @pytest.mark.skipif(
+        not is_te_min_version("1.7.0.dev0"),
+        reason="Expert with TE Linear is only supported in TE 1.7.0 and later.",
+    )
+    @pytest.mark.parametrize("moe_token_dispatcher_type", ["allgather", "alltoall"])
+    @pytest.mark.parametrize("num_moe_experts", [4])
+    @pytest.mark.parametrize("use_te,grouped_gemm", [(True, True), (True, False), (False, False)])
+    @pytest.mark.parametrize("moe_latent_size", [8, 16])
+    def test_latent_moe_layer(
+        self, num_moe_experts, moe_token_dispatcher_type, use_te, grouped_gemm, moe_latent_size
+    ):
+        Utils.initialize_model_parallel(1, 1)
+        _set_random_seed(seed_=123, data_parallel_random_init=False)
+        self.transformer_config = TransformerConfig(
+            num_layers=1,
+            hidden_size=32,
+            num_attention_heads=4,
+            num_moe_experts=num_moe_experts,
+            use_cpu_initialization=True,
+            moe_token_dispatcher_type=moe_token_dispatcher_type,
+            moe_router_topk=2,
+            moe_aux_loss_coeff=0.01,
+            moe_grouped_gemm=grouped_gemm,
+            moe_ffn_hidden_size=128,
+            moe_shared_expert_intermediate_size=128,
+            activation_func=torch.nn.functional.silu,
+            gated_linear_unit=True,
+            add_bias_linear=False,
+            moe_latent_size=moe_latent_size,
+        )
+        if use_te:
+            transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
+                num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm
+            )
+        else:
+            transformer_layer_spec = get_gpt_layer_local_spec(
+                num_experts=num_moe_experts, moe_grouped_gemm=grouped_gemm
+            )
+        moe_layer = MoELayer(
+            self.transformer_config, transformer_layer_spec.submodules.mlp.submodules
+        )
+        moe_layer.cuda()
+        config = moe_layer.config
+
+        assert (
+            moe_layer.shared_experts.linear_fc1.weight.shape[1] == config.hidden_size
+        ), "Shared expert computation has to happen in hidden dimension."
+        assert (
+            moe_layer.shared_experts.linear_fc2.weight.shape[0] == config.hidden_size
+        ), "Shared expert computation has to happen in hidden dimension."
+        if grouped_gemm:
+            for i in range(num_moe_experts):
+                fc1_weight = getattr(moe_layer.experts.linear_fc1, f"weight{i}")
+                fc2_weight = getattr(moe_layer.experts.linear_fc2, f"weight{i}")
+                assert (
+                    fc1_weight.shape[1] == config.moe_latent_size
+                ), f"Shape mismatch for expert {i} {fc1_weight.shape=}"
+                assert (
+                    fc2_weight.shape[0] == config.moe_latent_size
+                ), f"Shape mismatch for expert {i} {fc2_weight.shape=}"
+        else:
+            for i in range(num_moe_experts):
+                expert = moe_layer.experts.local_experts[i]
+                assert (
+                    expert.linear_fc1.weight.shape[1] == config.moe_latent_size
+                ), f"Shape mismatch for expert {i} {fc1_weight.shape=}"
+                assert (
+                    expert.linear_fc2.weight.shape[0] == config.moe_latent_size
+                ), f"Shape mismatch for expert {i} {fc2_weight.shape=}"
+        assert (
+            moe_layer.router.weight.shape[1] == config.hidden_size
+        ), "MoE routing has to happen in hidden dimension."
+
+        # [sequence length, batch size, hidden size]
+        hidden_states = torch.ones((32, 2, config.hidden_size))
+        hidden_states = hidden_states.cuda()
+        output, _ = moe_layer(hidden_states)
+        assert output.shape[2] == config.hidden_size
+
+        Utils.destroy_model_parallel()