diff --git a/examples/specdec_bench/specdec_bench/datasets/speed.py b/examples/specdec_bench/specdec_bench/datasets/speed.py
index fe544bb353e..3552d71a1ad 100644
--- a/examples/specdec_bench/specdec_bench/datasets/speed.py
+++ b/examples/specdec_bench/specdec_bench/datasets/speed.py
@@ -737,10 +737,40 @@ def _load_dataset(self, config_name_or_dataset_path: config_type | str) -> "Data
                     }
                     table = table.replace_schema_metadata(new_meta or None)
                 dataset = HFDataset(table)
-        if self.num_samples is not None:
-            dataset = dataset.select(range(self.num_samples))
+        if self.num_samples is not None and self.num_samples < len(dataset):
+            dataset = self._stratified_select(dataset, self.num_samples)
         return dataset
 
+    @staticmethod
+    def _stratified_select(dataset: "Dataset", n: int) -> "Dataset":
+        """Select ``n`` samples uniformly across the ``category`` column.
+
+        Round-robin across categories until ``n`` rows are collected. The
+        resulting prefix is balanced; once a smaller category is exhausted
+        the remaining categories continue contributing, so exactly ``n``
+        rows are returned whenever ``n`` does not exceed the dataset size.
+        Falls back to ``range(n)`` when ``category`` is absent or there is
+        only one category. Indices come from ``range(category_size)`` (not
+        random) so behavior is deterministic.
+        """
+        if "category" not in dataset.column_names:
+            return dataset.select(range(n))
+        cat_to_rows: dict[str, list[int]] = {}
+        for i, c in enumerate(dataset["category"]):
+            cat_to_rows.setdefault(c, []).append(i)
+        if len(cat_to_rows) <= 1:
+            return dataset.select(range(n))
+        cat_lists = list(cat_to_rows.values())
+        interleaved: list[int] = []
+        max_len = max(len(c) for c in cat_lists)
+        for i in range(max_len):
+            for c in cat_lists:
+                if i < len(c):
+                    interleaved.append(c[i])
+                    if len(interleaved) == n:
+                        return dataset.select(interleaved)
+        return dataset.select(interleaved)
+
     def _resolve_external_data(
         self, dataset: "Dataset", speed_config: config_type | str
     ) -> "Dataset":