diff --git a/benchmarks/run.py b/benchmarks/run.py
index 745414e27..c695dfade 100644
--- a/benchmarks/run.py
+++ b/benchmarks/run.py
@@ -61,6 +61,11 @@ class RunResult:
 KERNEL_MAPPINGS: dict[str, tuple[str, ...]] = {  # pyright: ignore[reportAssignmentType]
     # <tritonbench_op_name>: (<tritonbench_module_path>, <helion_kernel_module_path>, <helion_kernel_function_name>)
     "vector_add": ("tritonbench.operators.vector_add.operator", "examples.add", "add"),
+    "addmm": (
+        "tritonbench.operators.addmm.operator",
+        "examples.matmul",
+        "addmm_tritonbench",
+    ),
     "ragged_attention": (
         "tritonbench.operators.ragged_attention.operator",
         "examples.jagged_hstu_attn",
diff --git a/examples/matmul.py b/examples/matmul.py
index bffbde7ee..2e9d8c563 100644
--- a/examples/matmul.py
+++ b/examples/matmul.py
@@ -90,9 +90,19 @@ def check(m: int, k: int, n: int) -> None:
     x = torch.randn([m, k], device="cuda", dtype=torch.float16)
     y = torch.randn([k, n], device="cuda", dtype=torch.float16)
     bias = torch.randn([n], device="cuda", dtype=torch.float16)
+    bias_scalar = torch.randn([1], device="cuda", dtype=torch.float16)
     # Test without bias
     run_example(matmul, torch.matmul, (x, y))
 
+    # Test for addmm with scalar bias
+    def addmm(bias: Tensor, mat1: Tensor, mat2: Tensor) -> Tensor:
+        m, k = mat1.size()
+        k2, n = mat2.size()
+        bias = torch.broadcast_to(bias, [m, n])
+        return matmul(mat1, mat2, lambda acc, tile: acc + bias[tile[0], tile[1]])
+
+    run_example(addmm, torch.addmm, (bias_scalar, x, y))
+
     # Test with bias
     def helion_linear(x: Tensor, y: Tensor, bias: Tensor) -> Tensor:
         return matmul(x, y, lambda acc, tile: acc + bias[tile[1]])
@@ -138,6 +148,23 @@ def matmul_tritonbench(
     return lambda: matmul(a, b)
 
 
+def addmm_tritonbench(bias: Tensor, mat1: Tensor, mat2: Tensor) -> Callable:
+    """
+    Wrapper for tritonbench that performs a matrix multiplication of the matrices
+    `mat1` and `mat2` followed by adding `bias` to the result.
+    Args:
+        bias (torch.Tensor): Bias to add in the epilogue.
+        mat1 (torch.Tensor): Left matrix.
+        mat2 (torch.Tensor): Right matrix.
+    Returns:
+        Callable: A callable that runs the matmul kernel with bias.
+    """
+    m, k = mat1.size()
+    k2, n = mat2.size()
+    bias = torch.broadcast_to(bias, [m, n])
+    return lambda: matmul(mat1, mat2, lambda acc, tile: acc + bias[tile[0], tile[1]])
+
+
 # %%
 def main() -> None:
     """