diff --git a/mlos_bench/mlos_bench/tests/tunables/test_tunables_size_props.py b/mlos_bench/mlos_bench/tests/tunables/test_tunables_size_props.py
new file mode 100644
index 00000000000..de966536c4b
--- /dev/null
+++ b/mlos_bench/mlos_bench/tests/tunables/test_tunables_size_props.py
@@ -0,0 +1,97 @@
+#
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+#
+"""
+Unit tests for checking tunable size properties.
+"""
+
+import numpy as np
+import pytest
+
+from mlos_bench.tunables.tunable import Tunable
+
+
+# Note: these test do *not* check the ConfigSpace conversions for those same Tunables.
+# That is checked indirectly via grid_search_optimizer_test.py
+
+def test_tunable_int_size_props() -> None:
+    """Test tunable int size properties"""
+    tunable = Tunable(
+        name="test",
+        config={
+            "type": "int",
+            "range": [1, 5],
+            "default": 3,
+        })
+    assert tunable.span == 4
+    assert tunable.cardinality == 5
+    expected = [1, 2, 3, 4, 5]
+    assert list(tunable.quantized_values or []) == expected
+    assert list(tunable.values or []) == expected
+
+
+def test_tunable_float_size_props() -> None:
+    """Test tunable float size properties"""
+    tunable = Tunable(
+        name="test",
+        config={
+            "type": "float",
+            "range": [1.5, 5],
+            "default": 3,
+        })
+    assert tunable.span == 3.5
+    assert tunable.cardinality == np.inf
+    assert tunable.quantized_values is None
+    assert tunable.values is None
+
+
+def test_tunable_categorical_size_props() -> None:
+    """Test tunable categorical size properties"""
+    tunable = Tunable(
+        name="test",
+        config={
+            "type": "categorical",
+            "values": ["a", "b", "c"],
+            "default": "a",
+        })
+    with pytest.raises(AssertionError):
+        _ = tunable.span
+    assert tunable.cardinality == 3
+    assert tunable.values == ["a", "b", "c"]
+    with pytest.raises(AssertionError):
+        _ = tunable.quantized_values
+
+
+def test_tunable_quantized_int_size_props() -> None:
+    """Test quantized tunable int size properties"""
+    tunable = Tunable(
+        name="test",
+        config={
+            "type": "int",
+            "range": [100, 1000],
+            "default": 100,
+            "quantization": 100
+        })
+    assert tunable.span == 900
+    assert tunable.cardinality == 10
+    expected = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]
+    assert list(tunable.quantized_values or []) == expected
+    assert list(tunable.values or []) == expected
+
+
+def test_tunable_quantized_float_size_props() -> None:
+    """Test quantized tunable float size properties"""
+    tunable = Tunable(
+        name="test",
+        config={
+            "type": "float",
+            "range": [0, 1],
+            "default": 0,
+            "quantization": .1
+        })
+    assert tunable.span == 1
+    assert tunable.cardinality == 11
+    expected = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
+    assert pytest.approx(list(tunable.quantized_values or []), 0.0001) == expected
+    assert pytest.approx(list(tunable.values or []), 0.0001) == expected
diff --git a/mlos_bench/mlos_bench/tunables/tunable.py b/mlos_bench/mlos_bench/tunables/tunable.py
index f04dbea8729..1e6f2023372 100644
--- a/mlos_bench/mlos_bench/tunables/tunable.py
+++ b/mlos_bench/mlos_bench/tunables/tunable.py
@@ -9,7 +9,9 @@
 import collections
 import logging
 
-from typing import Any, Dict, List, Literal, Optional, Sequence, Tuple, Type, TypedDict, Union
+from typing import Any, Dict, Iterable, List, Literal, Optional, Sequence, Tuple, Type, TypedDict, Union
+
+import numpy as np
 
 _LOG = logging.getLogger(__name__)
 
@@ -370,6 +372,7 @@ def is_valid(self, value: TunableValue) -> bool:
         is_valid : bool
             True if the value is valid, False otherwise.
         """
+        # FIXME: quantization check?
         if self.is_categorical and self._values:
             return value in self._values
         elif self.is_numerical and self._range:
@@ -563,19 +566,79 @@ def range(self) -> Union[Tuple[int, int], Tuple[float, float]]:
         assert self._range is not None
         return self._range
 
+    @property
+    def span(self) -> Union[int, float]:
+        """
+        Gets the span of the range.
+
+        Note: this does not take quantization into account.
+
+        Returns
+        -------
+        Union[int, float]
+            (max - min) for numerical tunables.
+        """
+        num_range = self.range
+        return num_range[1] - num_range[0]
+
     @property
     def quantization(self) -> Optional[Union[int, float]]:
         """
-        Get the number of quantization points, if specified.
+        Get the quantization factor, if specified.
 
         Returns
         -------
         quantization : int, float, None
-            Number of quantization points or None.
+            The quantization factor, or None.
         """
-        assert self.is_numerical
+        if self.is_categorical:
+            return None
         return self._quantization
 
+    @property
+    def quantized_values(self) -> Optional[Union[Iterable[int], Iterable[float]]]:
+        """
+        Get a sequence of quanitized values for this tunable.
+
+        Returns
+        -------
+        Optional[Union[Iterable[int], Iterable[float]]]
+            If the Tunable is quantizable, returns a sequence of those elements,
+            else None (e.g., for unquantized float type tunables).
+        """
+        num_range = self.range
+        if self.type == "float":
+            if not self._quantization:
+                return None
+            # Be sure to return python types instead of numpy types.
+            cardinality = self.cardinality
+            assert isinstance(cardinality, int)
+            return (float(x) for x in np.linspace(start=num_range[0],
+                                                  stop=num_range[1],
+                                                  num=cardinality,
+                                                  endpoint=True))
+        assert self.type == "int", f"Unhandled tunable type: {self}"
+        return range(int(num_range[0]), int(num_range[1]) + 1, int(self._quantization or 1))
+
+    @property
+    def cardinality(self) -> Union[int, float]:
+        """
+        Gets the cardinality of elements in this tunable, or else infinity.
+
+        If the tunable has quantization set, this
+
+        Returns
+        -------
+        cardinality : int, float
+            Either the number of points in the tunable or else infinity.
+        """
+        if self.is_categorical:
+            return len(self.categories)
+        if not self.quantization and self.type == "float":
+            return np.inf
+        q_factor = self.quantization or 1
+        return int(self.span / q_factor) + 1
+
     @property
     def is_log(self) -> Optional[bool]:
         """
@@ -629,6 +692,21 @@ def categories(self) -> List[Optional[str]]:
         assert self._values is not None
         return self._values
 
+    @property
+    def values(self) -> Optional[Union[Iterable[Optional[str]], Iterable[int], Iterable[float]]]:
+        """
+        Gets the categories or quantized values for this tunable.
+
+        Returns
+        -------
+        Optional[Union[Iterable[Optional[str]], Iterable[int], Iterable[float]]]
+            Categories or quantized values.
+        """
+        if self.is_categorical:
+            return self.categories
+        assert self.is_numerical
+        return self.quantized_values
+
     @property
     def meta(self) -> Dict[str, Any]:
         """