diff --git a/backends/webgpu/test/op_tests/cases.py b/backends/webgpu/test/op_tests/cases.py
index 6fa7125b68c..3ac13727afd 100644
--- a/backends/webgpu/test/op_tests/cases.py
+++ b/backends/webgpu/test/op_tests/cases.py
@@ -362,3 +362,50 @@ def _layer_norm_suite() -> WebGPUTestSuite:
         atol=1e-4,
         rtol=1e-3,
     )
+from executorch.backends.webgpu.test.ops.test_linear_fp32 import (
+    _ramp as _lin_ramp,
+    make_linear,
+)
+
+
+@register_op_test("linear_fp32")
+def _linear_fp32_suite() -> WebGPUTestSuite:
+    # fp32 linear (BART + DaViT projections); bias + no-bias, and shapes whose
+    # M*N exceeds the 65535 1D ceiling to exercise the 2D-dispatch spill.
+    return WebGPUTestSuite(
+        module_factory=make_linear,
+        cases=[
+            Case(
+                name="bias_mat",
+                construct={"in_features": 64, "out_features": 32},
+                inputs=((4, 64),),
+            ),
+            Case(
+                name="no_bias",
+                construct={"in_features": 64, "out_features": 32, "bias": False},
+                inputs=((4, 64),),
+            ),
+            Case(
+                name="rank3",
+                construct={"in_features": 768, "out_features": 768},
+                inputs=(InputSpec(shape=(1, 16, 768), gen=_lin_ramp),),
+            ),
+            Case(
+                name="tall_m",
+                construct={"in_features": 128, "out_features": 64},
+                inputs=((256, 128),),
+            ),
+            Case(
+                name="bart_proj",
+                construct={"in_features": 1024, "out_features": 1024},
+                inputs=(InputSpec(shape=(1, 8, 1024), gen=_lin_ramp),),
+            ),
+            Case(
+                name="odd_k",
+                construct={"in_features": 63, "out_features": 32},
+                inputs=((4, 63),),
+            ),
+        ],
+        atol=1e-4,
+        rtol=1e-3,
+    )
diff --git a/backends/webgpu/test/ops/test_linear_fp32.py b/backends/webgpu/test/ops/test_linear_fp32.py
new file mode 100644
index 00000000000..c635b4f9a3e
--- /dev/null
+++ b/backends/webgpu/test/ops/test_linear_fp32.py
@@ -0,0 +1,77 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""`aten.linear.default` (fp32) module + inputs for the WebGPU op-test framework.
+
+`make_linear` and `_ramp` are imported by `cases.py` to drive the declarative
+op-test suite. `LinearFp32Test` is the export-delegation smoke test. fp32 linear
+is the projection used throughout BART + the DaViT vision encoder (Florence-2);
+both the bias and no-bias paths are covered.
+"""
+
+import unittest
+
+import torch
+
+from executorch.backends.vulkan.partitioner.vulkan_partitioner import VulkanPartitioner
+from executorch.exir import to_edge_transform_and_lower
+
+
+class LinearFp32Module(torch.nn.Module):
+    """fp32 linear; lowers to aten.linear.default with a prepacked [N, K] weight."""
+
+    def __init__(self, in_features: int, out_features: int, bias: bool = True):
+        super().__init__()
+        self.fc = torch.nn.Linear(in_features, out_features, bias=bias)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.fc(x)
+
+
+def make_linear(
+    in_features: int, out_features: int, bias: bool = True
+) -> torch.nn.Module:
+    """Factory with deterministic weight (+ bias): a normalized ramp per row."""
+    m = LinearFp32Module(in_features, out_features, bias=bias)
+    with torch.no_grad():
+        w = torch.linspace(
+            -1.0, 1.0, out_features * in_features, dtype=torch.float32
+        ).reshape(out_features, in_features)
+        m.fc.weight.copy_(w / in_features)
+        if bias:
+            m.fc.bias.copy_(
+                torch.linspace(-0.5, 0.5, out_features, dtype=torch.float32)
+            )
+    return m
+
+
+def _ramp(shape) -> torch.Tensor:
+    """Deterministic linear ramp in [-1, 1] reshaped to `shape`."""
+    n = 1
+    for d in shape:
+        n *= d
+    return torch.linspace(-1.0, 1.0, n, dtype=torch.float32).reshape(shape)
+
+
+def _export(m: torch.nn.Module, x: torch.Tensor):
+    ep = torch.export.export(m, (x,))
+    return to_edge_transform_and_lower(
+        ep, partitioner=[VulkanPartitioner()]
+    ).to_executorch()
+
+
+class LinearFp32Test(unittest.TestCase):
+    def test_export_delegates(self) -> None:
+        for bias in (True, False):
+            et = _export(make_linear(64, 32, bias=bias).eval(), _ramp((4, 64)))
+            found = any(
+                d.id == "VulkanBackend"
+                for plan in et.executorch_program.execution_plan
+                for d in plan.delegates
+            )
+            self.assertTrue(
+                found, f"Expected a VulkanBackend delegate (linear bias={bias})"
+            )