diff --git a/thunder/executors/nvfuserex_impl.py b/thunder/executors/nvfuserex_impl.py
index 73a1bb636..126c208fc 100644
--- a/thunder/executors/nvfuserex_impl.py
+++ b/thunder/executors/nvfuserex_impl.py
@@ -2209,6 +2209,12 @@ def _linear_check(a: TensorProxy, b: TensorProxy, bias: TensorProxy | None) -> b
     if bias is not None and not is_supported_tensor(bias):
         return False
 
+    # nvFuser supports only fp16 and bf16 inputs. Only checking the first tensor
+    # dtype, as all tensors should have the same dtype which is checked by
+    # linear_meta.
+    if a.dtype not in (dtypes.float16, dtypes.bfloat16):
+        return False
+
     # nvFuser only supports 2D inputs in v0.2.3.
     if not a.ndim == 2:
         return False
diff --git a/thunder/tests/test_nvfuser.py b/thunder/tests/test_nvfuser.py
index 276ae8314..10e518054 100644
--- a/thunder/tests/test_nvfuser.py
+++ b/thunder/tests/test_nvfuser.py
@@ -34,7 +34,7 @@
     TorchExecutor,
 )
 from thunder.tests.make_tensor import make_tensor, make_tensor_like
-from thunder.tests.opinfos import opinfos, push_away_from_singularities, tensor_creation_ops, get_opinfo
+from thunder.tests.opinfos import linear_opinfo
 from looseversion import LooseVersion
 
 
@@ -854,32 +854,35 @@ def get_num_fusions(cfn):
 
 
 @instantiate(
-    dtypes=(thunder.float16, thunder.bfloat16), devicetypes=(devices.DeviceType.CUDA,), executors=(nvFuserExecutor,)
+    dtypes=(thunder.float16, thunder.bfloat16),
+    devicetypes=(devices.DeviceType.CUDA,),
+    executors=(nvFuserExecutor,),
+    decorators=(
+        pytest.mark.skipif(nvfuser_version() < LooseVersion("0.2.3"), reason="Requires nvFuser version 0.2.3 or later"),
+    ),
 )
 def test_linear(executor, device: str, dtype: dtypes.dtype):
 
     def fn(a, b, bias=None):
         return torch.nn.functional.linear(a, b, bias)
 
-    m, n, k = 128, 64, 32
-    torch_dtype = ltorch.to_torch_dtype(dtype)
-    a = torch.randn((m, k), dtype=torch_dtype, device=device)
-    b = torch.randn((n, k), dtype=torch_dtype, device=device)
+    at_least_one_tested = False
 
-    for has_bias in [True, False]:
-        bias = None
-
-        if has_bias:
-            bias = torch.randn(n, dtype=torch_dtype, device=device)
+    for sample in linear_opinfo.sample_inputs(device, dtype):
+        # nvFuser doesn't support batched input yet
+        if sample.args[0].ndim != 2:
+            continue
 
         compiled_func = thunder.jit(fn, executors_list=executor.executors_list(), nv_enable_linear=True)
 
-        out = compiled_func(a, b, bias)
+        out = compiled_func(*sample.args)
         traces = thunder.last_traces(compiled_func)
         fusions = examine.get_fusions(traces[-1])
-        nv_version = nvfuser_version()
 
-        expected_fusions = 1 if nv_version >= LooseVersion("0.2.3") else 0
+        expected_fusions = 1
 
         assert len(fusions) == expected_fusions
-        torch.testing.assert_close(out, torch.nn.functional.linear(a, b, bias))
+        torch.testing.assert_close(out, torch.nn.functional.linear(*sample.args))
+        at_least_one_tested = True
+
+    assert at_least_one_tested