diff --git a/examples/vector_add/vector_add.py b/examples/vector_add/vector_add.py
new file mode 100644
index 0000000..25f92be
--- /dev/null
+++ b/examples/vector_add/vector_add.py
@@ -0,0 +1,87 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Elementwise vector addition on the GPU.
+
+Each thread block loads a contiguous tile of ``block_elems`` values from ``a`` and ``b``,
+computes ``c = a + b``, and stores the result. This is a minimal Tilus example: one
+:class:`tilus.Script`, :meth:`global_view`, :meth:`load_global`, elementwise ``+``, and
+:meth:`store_global`.
+
+``n`` must be divisible by ``block_elems`` (enforced in :func:`main`).
+"""
+
+import pandas
+import tilus
+import torch
+from tilus import float32, int32
+from tilus.utils import benchmark_func, cdiv
+
+
+class VectorAdd(tilus.Script):
+    """``c[i] = a[i] + b[i]`` for ``i in range(n)``."""
+
+    def __init__(self):
+        super().__init__()
+        self.block_elems = 1024
+
+    def __call__(
+        self,
+        n: int32,
+        a_ptr: ~float32,
+        b_ptr: ~float32,
+        c_ptr: ~float32,
+    ):
+        self.attrs.blocks = (cdiv(n, self.block_elems),)
+        self.attrs.warps = 4
+
+        offset: int32 = self.block_elems * self.blockIdx.x
+
+        ga = self.global_view(a_ptr, dtype=float32, shape=[n])
+        gb = self.global_view(b_ptr, dtype=float32, shape=[n])
+        gc = self.global_view(c_ptr, dtype=float32, shape=[n])
+
+        ra = self.load_global(ga, offsets=[offset], shape=[self.block_elems])
+        rb = self.load_global(gb, offsets=[offset], shape=[self.block_elems])
+        rc = ra + rb
+        self.store_global(gc, rc, offsets=[offset])
+
+
+def _nbytes_fp32_vector_add(n_elts: int) -> int:
+    # 3 x fp32: read a, read b, write c
+    return n_elts * 4 * 3
+
+
+def main():
+    headers = ["n", "name", "latency (ms)", "GB/s"]
+    workloads = [1 << 20, 1 << 24]
+
+    rows = []
+    for n in workloads:
+        assert n % 1024 == 0, "n must be divisible by block_elems (1024)"
+
+        kernel = VectorAdd()
+        a = torch.randn(n, dtype=torch.float32, device="cuda")
+        b = torch.randn(n, dtype=torch.float32, device="cuda")
+        c_actual = torch.empty(n, dtype=torch.float32, device="cuda")
+        c_expect = a + b
+        torch.cuda.synchronize()
+
+        kernel(n, a, b, c_actual)
+        torch.cuda.synchronize()
+
+        torch.testing.assert_close(c_expect, c_actual)
+
+        for name, func in [
+            ("torch", lambda: torch.add(a, b, out=c_actual)),
+            ("tilus", lambda: kernel(n, a, b, c_actual)),
+        ]:
+            latency = benchmark_func(func, warmup=5, repeat=20)
+            gbps = _nbytes_fp32_vector_add(n) / (latency * 1e-3) / 1e9
+            rows.append([n, name, latency, gbps])
+
+    df = pandas.DataFrame(rows, columns=headers)
+    print(df)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/examples/test_examples.py b/tests/examples/test_examples.py
index ef61ae8..67431e2 100644
--- a/tests/examples/test_examples.py
+++ b/tests/examples/test_examples.py
@@ -43,6 +43,8 @@
     ("norm", "layer_norm.py", None),
     # softmax example
     ("softmax", "softmax.py", None),
+    # vector add
+    ("vector_add", "vector_add.py", None),
     # attention examples (SM 8.0+)
     ("attention", "flash_attention_v1.py", nvgpu_sm80),
     ("attention", "flash_attention_v2.py", nvgpu_sm80),