diff --git a/jax_scaled_arithmetics/core/__init__.py b/jax_scaled_arithmetics/core/__init__.py
index 199a74a..aefd0dc 100644
--- a/jax_scaled_arithmetics/core/__init__.py
+++ b/jax_scaled_arithmetics/core/__init__.py
@@ -7,6 +7,7 @@
     asarray,
     get_scale_dtype,
     is_scaled_leaf,
+    is_static_anyscale,
     is_static_one_scalar,
     is_static_zero,
     make_scaled_scalar,
diff --git a/jax_scaled_arithmetics/core/datatype.py b/jax_scaled_arithmetics/core/datatype.py
index 2d293fa..dba692f 100644
--- a/jax_scaled_arithmetics/core/datatype.py
+++ b/jax_scaled_arithmetics/core/datatype.py
@@ -268,6 +268,32 @@ def is_static_zero(val: Union[Array, ScaledArray]) -> Array:
     return np.zeros(val.shape, dtype=np.bool_)
 
 
+def is_static_anyscale(val: Union[Array, ScaledArray]) -> Array:
+    """Is a scaled array a static anyscale values (i.e. 0/inf/-inf during JAX tracing as well)?
+
+    Returns a boolean Numpy array of the shape of the input.
+    """
+
+    def np_anyscale(arr):
+        # Check if 0, np.inf or -np.inf
+        absarr = np.abs(arr)
+        return np.logical_or(np.equal(absarr, 0), np.equal(absarr, np.inf))
+
+    if is_numpy_scalar_or_array(val):
+        return np_anyscale(val)
+    if isinstance(val, ScaledArray):
+        # TODO: deal with 0 * inf issue?
+        data_mask = (
+            np_anyscale(val.data) if is_numpy_scalar_or_array(val.data) else np.zeros(val.data.shape, dtype=np.bool_)
+        )
+        scale_mask = (
+            np_anyscale(val.scale) if is_numpy_scalar_or_array(val.scale) else np.zeros(val.scale.shape, dtype=np.bool_)
+        )
+        return np.logical_or(data_mask, scale_mask)
+    # By default: can't decide.
+    return np.zeros(val.shape, dtype=np.bool_)
+
+
 def is_static_one_scalar(val: Array) -> Union[bool, np.bool_]:
     """Is a scaled array a static one scalar value (i.e. one during JAX tracing as well)?"""
     if isinstance(val, (int, float)):
diff --git a/tests/core/test_datatype.py b/tests/core/test_datatype.py
index 1d664f6..512877e 100644
--- a/tests/core/test_datatype.py
+++ b/tests/core/test_datatype.py
@@ -13,6 +13,7 @@
     asarray,
     get_scale_dtype,
     is_scaled_leaf,
+    is_static_anyscale,
     is_static_one_scalar,
     is_static_zero,
     make_scaled_scalar,
@@ -304,6 +305,25 @@ def test__is_static_zero__proper_all_result(self, val, result):
         all_zero = np.all(is_static_zero(val))
         assert all_zero == result
 
+    @parameterized.parameters(
+        {"val": 0, "result": True},
+        {"val": 0.0, "result": True},
+        {"val": np.inf, "result": True},
+        {"val": np.int32(0), "result": True},
+        {"val": np.float16(0), "result": True},
+        {"val": np.float16(-np.inf), "result": True},
+        {"val": np.array([1, 2]), "result": False},
+        {"val": np.array([0, 0.0, -np.inf, np.inf]), "result": True},
+        {"val": jnp.array([0, 0.0, np.inf]), "result": False},
+        {"val": ScaledArray(np.array([0, 0.0]), jnp.array(2.0)), "result": True},
+        {"val": ScaledArray(jnp.array([3, 4.0]), np.array(0.0)), "result": True},
+        {"val": ScaledArray(jnp.array([3, 4.0]), np.array(np.inf)), "result": True},
+        {"val": ScaledArray(jnp.array([3, 4.0]), jnp.array(0.0)), "result": False},
+    )
+    def test__is_static_anyscale__proper_all_result(self, val, result):
+        all_zero = np.all(is_static_anyscale(val))
+        assert all_zero == result
+
     @parameterized.parameters(
         {"val": 0, "result": False},
         {"val": 1, "result": True},