diff --git a/tests/pytorch/distributed/run_fsdp2_model.py b/tests/pytorch/distributed/run_fsdp2_model.py
index c3432992429..841c7761e59 100644
--- a/tests/pytorch/distributed/run_fsdp2_model.py
+++ b/tests/pytorch/distributed/run_fsdp2_model.py
@@ -15,6 +15,7 @@
     Float8CurrentScaling,
     MXFP8BlockScaling,
 )
+from transformer_engine.pytorch.optimizers.fused_adam import FusedAdam
 
 import torch
 import torch.distributed as dist
@@ -84,6 +85,9 @@ def _parse_args(argv=None, namespace=None):
         nargs="+",
         help='FSDP/HSDP sharding dimensions ("replicate", "shard")',
     )
+    parser.add_argument(
+        "--adam", type=str, choices=["fused", "torch"], default="torch", help="Optimizer type."
+    )
     args = parser.parse_args(argv, namespace)
     if args.sharding_dims:
         assert len(args.sharding_dims) <= 2
@@ -322,7 +326,10 @@ def _train(args):
         f"FSDP2 model in cuda, memory allocated: {torch.cuda.memory_allocated(device)/1e6} MB"
     )
 
-    optimizer = optim.Adam(model.parameters(), lr=1e-3)
+    if args.adam == "fused":
+        optimizer = FusedAdam(model.parameters(), lr=1e-3)
+    else:
+        optimizer = optim.Adam(model.parameters(), lr=1e-3)
 
     for iteration in range(args.iter):
         # Zero the parameter gradients
diff --git a/tests/pytorch/distributed/test_torch_fsdp2.py b/tests/pytorch/distributed/test_torch_fsdp2.py
index 91d6fc6ed11..ecfe5da2182 100644
--- a/tests/pytorch/distributed/test_torch_fsdp2.py
+++ b/tests/pytorch/distributed/test_torch_fsdp2.py
@@ -16,7 +16,7 @@
 NUM_PROCS: int = torch.cuda.device_count()
 
 
-def _run_test(fp_init, sharding_dims, recipe, layer_type):
+def _run_test(fp_init, sharding_dims, recipe, layer_type, optim_type="fused"):
     test_path = Path(__file__).parent.resolve() / "run_fsdp2_model.py"
     test_cmd = ["torchrun", f"--nproc_per_node={NUM_PROCS}", str(test_path)]
 
@@ -31,6 +31,7 @@ def _run_test(fp_init, sharding_dims, recipe, layer_type):
         assert False
     test_cmd += ["--recipe", recipe]
     test_cmd += ["--layer-type", layer_type]
+    test_cmd += ["--adam", optim_type]
 
     result = subprocess.run(test_cmd, env=os.environ, check=True)
 
@@ -42,8 +43,8 @@ def _run_test(fp_init, sharding_dims, recipe, layer_type):
 @pytest.mark.parametrize("fp8_init", (False, True))
 @pytest.mark.parametrize("recipe", ("delayed_scaling", "current_scaling", "mx_fp8_block_scaling"))
 @pytest.mark.parametrize("layer_type", ("LayerNormLinear", "TransformerLayer"))
-def test_distributed(fp8_init, sharding_dims, recipe, layer_type):
-
+@pytest.mark.parametrize("optim_type", ("fused", "torch"))
+def test_distributed(fp8_init, sharding_dims, recipe, layer_type, optim_type):
     # Skip invalid configurations
     if torch.cuda.device_count() < 4:
         pytest.skip("FSDP2 test requires at least 4 GPUs")
@@ -53,7 +54,11 @@ def test_distributed(fp8_init, sharding_dims, recipe, layer_type):
     elif not fp8_available:
         pytest.skip(reason_for_no_fp8)
 
-    _run_test(fp8_init, sharding_dims, recipe, layer_type)
+    # Skip incompatible optimizer + recipe combinations
+    if optim_type == "fused" and recipe in ["mx_fp8_block_scaling"] and fp8_init:
+        pytest.skip("Fused Adam does not support FP8 with MX FP8 Block Scaling")
+
+    _run_test(fp8_init, sharding_dims, recipe, layer_type, optim_type)
 
 
 def test_dummy() -> None:
diff --git a/transformer_engine/pytorch/optimizers/fused_adam.py b/transformer_engine/pytorch/optimizers/fused_adam.py
index b5c87b4815c..935af8ee0e3 100644
--- a/transformer_engine/pytorch/optimizers/fused_adam.py
+++ b/transformer_engine/pytorch/optimizers/fused_adam.py
@@ -373,9 +373,9 @@ def _initialize_state(
         """
         dtype = self.name_to_dtype_map[state_name]
         if store_param_remainders:
-            data = torch.zeros(param.shape, dtype=torch.int16, device=param.device)
+            data = torch.zeros_like(param, dtype=torch.int16, device=param.device)
         else:
-            data = torch.empty(param.shape, dtype=dtype, device=param.device)
+            data = torch.empty_like(param, dtype=dtype, device=param.device)
         if zero_buffer:
             data.zero_()