diff --git a/test/run_tests.sh b/test/run_tests.sh
index 1a24bba4b92..36e2dd0c3ab 100755
--- a/test/run_tests.sh
+++ b/test/run_tests.sh
@@ -188,6 +188,7 @@ function run_xla_op_tests {
   run_test "$CDIR/pjrt/test_mesh_service.py"
   run_test "$CDIR/spmd/test_xla_sharding.py"
   run_test "$CDIR/spmd/test_xla_virtual_device.py"
+  run_test "$CDIR/spmd/test_dynamo_spmd.py"
   run_test "$CDIR/test_operations_hlo.py" "$@" --verbosity=$VERBOSITY
   run_test "$CDIR/test_input_output_aliases.py"
   run_test "$CDIR/test_torch_distributed_xla_backend.py"
diff --git a/test/spmd/test_dynamo_spmd.py b/test/spmd/test_dynamo_spmd.py
new file mode 100644
index 00000000000..2be24e1f78a
--- /dev/null
+++ b/test/spmd/test_dynamo_spmd.py
@@ -0,0 +1,58 @@
+import os
+import sys
+
+import torch
+import torch.nn as nn
+import torch_xla
+import torch_xla.core.xla_model as xm
+import torch_xla.experimental.xla_sharding as xs
+import torch_xla.debug.metrics as met
+import unittest
+
+import test_xla_sharding_base
+
+
+class SimpleLinear(nn.Module):
+
+  def __init__(self):
+    super(SimpleLinear, self).__init__()
+    self.fc1 = nn.Linear(128, 128)
+    self.relu = nn.ReLU()
+    self.fc2 = nn.Linear(128, 1)
+    # Add an additional 1x1 layer at the end to ensure the final layer
+    # is not sharded.
+    self.fc3 = nn.Linear(1, 1)
+
+  def forward(self, x):
+    y = self.relu(self.fc1(x))
+    z = self.fc2(y)
+    return self.fc3(z)
+
+
+class DynamoSpmdInferenceTest(test_xla_sharding_base.XlaShardingTest):
+
+  @classmethod
+  def setUpClass(cls):
+    os.environ["XLA_USE_SPMD"] = "1"
+    super().setUpClass()
+
+  def test_dynamo_spmd_basic(self):
+    device = xm.xla_device()
+    linear = SimpleLinear().to(device)
+    linear.eval()
+    xla_x = torch.randn(1, 128, device=device)
+    xs.mark_sharding(linear.fc2.weight, self._get_mesh((1, self.n_devices)),
+                     (1, 0))
+    xla_res = linear(xla_x)
+    xm.mark_step()
+
+    dynamo_linear = torch.compile(linear, backend="torchxla_trace_once")
+    dynamo_res = dynamo_linear(xla_x)
+    torch.allclose(xla_res.cpu(), dynamo_res.cpu())
+    # TODO(JackCaoG): add counter checks after ExecuteReplicated also creates
+    # a ExecuteMetric.
+
+
+if __name__ == '__main__':
+  test = unittest.main()
+  sys.exit(0 if test.result.wasSuccessful() else 1)
diff --git a/torch_xla/csrc/aten_xla_bridge.cpp b/torch_xla/csrc/aten_xla_bridge.cpp
index 892e5bbf8e5..3c7a5dc6fdc 100644
--- a/torch_xla/csrc/aten_xla_bridge.cpp
+++ b/torch_xla/csrc/aten_xla_bridge.cpp
@@ -305,6 +305,11 @@ torch::lazy::BackendDevice AtenDeviceToXlaDevice(const c10::Device& device) {
 }
 
 c10::Device XlaDeviceToAtenDevice(const torch::lazy::BackendDevice& device) {
+  // TODO(yeounoh) until we expose SPMD virtual device to the frontend, this
+  // will just be `XLA:0`.
+  if (device.type() == (int8_t)XlaDeviceType::SPMD) {
+    return c10::Device(at::kXLA, (size_t)0);
+  }
   return c10::Device(at::kXLA,
                      AtenXlaDeviceMapper::Get()->GetDeviceOrdinal(device));
 }
diff --git a/torch_xla/csrc/init_python_bindings.cpp b/torch_xla/csrc/init_python_bindings.cpp
index 47709a83b60..e1bebb1cb19 100644
--- a/torch_xla/csrc/init_python_bindings.cpp
+++ b/torch_xla/csrc/init_python_bindings.cpp
@@ -1728,7 +1728,10 @@ void InitXlaModuleBindings(py::module m) {
             -> std::vector<at::Tensor> {
           XLA_CHECK(hash_str.size() == sizeof(torch::lazy::hash_t));
           torch::lazy::hash_t hash = *(torch::lazy::hash_t*)(hash_str.c_str());
-          torch::lazy::BackendDevice device = torch_xla::GetCurrentDevice();
+          // Device will be Virtual device if SPMD is enabled.
+          torch::lazy::BackendDevice device =
+              ShardingUtil::UseVirtualDevice() ? ParseDeviceString("SPMD:0")
+                                               : torch_xla::GetCurrentDevice();
           auto results = XLAGraphExecutor::Get()->ExecuteComputationWithBarrier(
               hash, graph_inputs, device);
           std::vector<at::Tensor> retlist;
diff --git a/torch_xla/csrc/xla_graph_executor.cpp b/torch_xla/csrc/xla_graph_executor.cpp
index 9d7cab34aed..56056f7e769 100644
--- a/torch_xla/csrc/xla_graph_executor.cpp
+++ b/torch_xla/csrc/xla_graph_executor.cpp
@@ -574,6 +574,9 @@ XLAGraphExecutor::ExecuteComputationWithBarrier(
   MaybeDumpGraph("dynamo", hash);
   auto cachedComputation =
       XLAGraphExecutor::Get()->GetComputationCache()->Get(hash);
+  TF_VLOG(5) << "Cached computation (hash: " << torch::lazy::HashToString(hash)
+             << ") is_sharded=" << cachedComputation->is_sharded << std::endl;
+
   // TODO implement a fallback mechanism, or make sure those entries
   // never get kicked out
   XLA_CHECK(cachedComputation)
@@ -590,6 +593,16 @@ XLAGraphExecutor::ExecuteComputationWithBarrier(
     torch::lazy::BackendDataPtr handle =
         WrapXlaData(xla::ComputationClient::Get()->CreateDataPlaceholder(
             device.toString(), std::move(shape)));
+    // If SPMD is enabled, we assume all output will be sharded or replicated
+    // and wrapped inside PjRtShardedData handle.
+    if (ShardingUtil::UseVirtualDevice()) {
+      XLATensor::ShardingSpecPtr sharding =
+          std::make_shared<XLATensor::ShardingSpec>(
+              xla::HloSharding::Replicate().ToProto(), shape);
+      handle = WrapXlaData(xla::ComputationClient::Get()->WrapDataShards(
+          {UnwrapXlaData(handle)}, GetVirtualDevice().toString(),
+          sharding->shape.value(), sharding->sharding));
+    }
     placeholders.push_back(handle);
   }
 
@@ -608,6 +621,9 @@ XLAGraphExecutor::ExecuteComputationWithBarrier(
       if (auto xla_tensor_ptr = bridge::TryGetXlaTensor(ivalue.toTensor())) {
         dataptr = xla_tensor_ptr->GetXlaData();
       } else {
+        XLA_CHECK(device.type() != (int8_t)XlaDeviceType::SPMD)
+            << "SPMD device data should already be on the XLA backend "
+               "(XLATensor).";
         dataptr = torch_xla::TensorToXlaData(ivalue.toTensor(), device);
       }
 
@@ -619,21 +635,65 @@ XLAGraphExecutor::ExecuteComputationWithBarrier(
   std::shared_ptr<XLAGraphExecutor::Async> async = std::make_shared<Async>(
       &coll, std::move(arguments), placeholders, std::move(cachedComputation));
 
-  auto syncfn = [async, hash]() {
-    TF_VLOG(3) << "Executing Dynamo IR graph hash "
-               << torch::lazy::HashToString(hash) << " on device "
-               << async->device << " ...";
-    std::vector<torch::lazy::BackendDataPtr> results =
-        torch::lazy::getBackend()->ExecuteComputation(
+  // TODO(yeounoh) supply proper sharding specs for sharded results.
+  std::vector<XLATensor::ShardingSpecPtr> sharding_specs(placeholders.size());
+
+  auto syncfn = [async, hash, sharding_specs]() {
+    try {
+      TF_VLOG(3) << "Executing Dynamo IR graph hash "
+                 << torch::lazy::HashToString(hash) << " on device "
+                 << async->device << " ...";
+
+      std::vector<torch::lazy::BackendDataPtr> results;
+      if (async->cached_computation->is_sharded) {
+        std::vector<std::string> devices =
+            xla::ComputationClient::Get()->GetLocalDevices();
+        std::vector<std::vector<xla::ComputationClient::DataPtr>>
+            device_arguments = ShardingUtil::InputHandler(
+                UnwrapXlaData(async->parameters_data), devices);
+        xla::ComputationClient::ExecuteReplicatedOptions execute_options;
+        // OutputHandler creates sharded data for sharded
+        // tensor results. Both sharded and unsharded results should be
+        // "Assign"ed to the corresponding data placeholders.
+        std::vector<xla::ComputationClient::DataPtr> outputs =
+            ShardingUtil::OutputHandler(
+                xla::ComputationClient::Get()->ExecuteReplicated(
+                    *async->cached_computation->computation
+                         ->client_computation(),
+                    device_arguments, devices, execute_options),
+                sharding_specs);
+        results = WrapXlaData(outputs);
+        TF_VLOG(3) << "Executing Dynamo IR sharded graph hash "
+                   << torch::lazy::HashToString(hash) << " on devices "
+                   << absl::StrJoin(devices, ",") << " done!";
+      } else {
+        results = torch::lazy::getBackend()->ExecuteComputation(
             async->cached_computation->computation, async->parameters_data,
             async->device);
-    TF_VLOG(3) << "Executing Dynamo IR graph hash "
-               << torch::lazy::HashToString(hash) << " on device "
-               << async->device << " done!";
-    // Updating placeholder with actual output handle.
-    for (size_t i = 0; i < results.size(); ++i) {
-      XLA_CHECK(async->tensors_data[i] != nullptr);
-      async->tensors_data[i]->Assign(*results[i]);
+        TF_VLOG(3) << "Executing Dynamo IR graph hash "
+                   << torch::lazy::HashToString(hash) << " on device "
+                   << async->device << " done!";
+      }
+
+      // Updating placeholder with actual output handle.
+      for (size_t i = 0; i < results.size(); ++i) {
+        XLA_CHECK(async->tensors_data[i] != nullptr);
+        async->tensors_data[i]->Assign(*results[i]);
+      }
+    } catch (...) {
+      // There are two paths of discovery of an exception happening on an
+      // asynchronous task. One happens if the creator of the asynchronous task
+      // explicitly waits for completion, in which case the exception will be
+      // thrown from the Wait() API. Re-throwing the exception below makes sure
+      // this will be captured by the completer function created below, and
+      // surfaced by the Wait() API. But we also need to surface the exception
+      // even in case the caller does not wait, and that is accomplished by
+      // setting the unlockers status. In that case the exception will be
+      // surfaced when the user tries to acquire the device locks the next time.
+      for (auto& unlocker : async->unlocker) {
+        unlocker.SetStatus(std::current_exception());
+      }
+      throw;
     }
   };