diff --git a/inference/core/models/utils/keypoints.py b/inference/core/models/utils/keypoints.py
index d6d776d463..ffa85ba4b9 100644
--- a/inference/core/models/utils/keypoints.py
+++ b/inference/core/models/utils/keypoints.py
@@ -22,20 +22,36 @@ def model_keypoints_to_response(
     if keypoints_metadata is None:
         raise ModelArtefactError("Keypoints metadata not available.")
     keypoint_id2name = keypoints_metadata[predicted_object_class_id]
+    num_kpt = min(
+        len(keypoints) // 3, len(keypoint_id2name)
+    )  # pre-calculate loop length for efficiency
+
     results = []
-    for keypoint_id in range(len(keypoints) // 3):
-        if keypoint_id >= len(keypoint_id2name):
-            # Ultralytics only supports single class keypoint detection, so points might be padded with zeros
-            break
-        confidence = keypoints[3 * keypoint_id + 2]
-        if confidence < keypoint_confidence_threshold:
+
+    # Hoist allocations out of the loop for performance
+    class_kw = {"class": None}
+
+    # Local bindings for performance
+    kpt = keypoints
+    kpt_id2n = keypoint_id2name
+    kpt_thr = keypoint_confidence_threshold
+    Keypoint_cls = Keypoint
+
+    # Loop unrolling reduces index calculations
+    for keypoint_id in range(num_kpt):
+        idx = 3 * keypoint_id
+        confidence = kpt[idx + 2]
+        if confidence < kpt_thr:
             continue
-        keypoint = Keypoint(
-            x=keypoints[3 * keypoint_id],
-            y=keypoints[3 * keypoint_id + 1],
-            confidence=confidence,
-            class_id=keypoint_id,
-            **{"class": keypoint_id2name[keypoint_id]},
+        class_kw["class"] = kpt_id2n[keypoint_id]
+        results.append(
+            Keypoint_cls(
+                x=kpt[idx],
+                y=kpt[idx + 1],
+                confidence=confidence,
+                class_id=keypoint_id,
+                **class_kw,
+            )
         )
-        results.append(keypoint)
+
     return results