diff --git a/tests/runner/test_kv_cache_manager.py b/tests/runner/test_kv_cache_manager.py
index 72df8501a..33beec81a 100644
--- a/tests/runner/test_kv_cache_manager.py
+++ b/tests/runner/test_kv_cache_manager.py
@@ -3,6 +3,7 @@
 import jax
 import jax.numpy as jnp
 import numpy as np
+import pytest
 import torch
 from vllm.attention import Attention
 from vllm.attention.backends.abstract import AttentionType
@@ -200,14 +201,15 @@ def test_insert_request_with_kv_cache(self):
             np.testing.assert_array_equal(updated_block_content,
                                           expected_padded_slice)
 
-    def test_get_kv_cache_spec_with_compilation_cfg(self):
+    @pytest.mark.parametrize("num_kv_heads", [16, 32])
+    @pytest.mark.parametrize("head_size", [64, 100, 200])
+    def test_get_kv_cache_spec_with_compilation_cfg(self, num_kv_heads,
+                                                    head_size):
         # tests we create kv cache spec from compilation config
         # create a static forward context with
         # 10 full attention layers +
         # 10 sliding window attention layers
         # 1 layer with shared kv cache.
-        num_kv_heads = 16
-        head_size = 128
         attn_type = AttentionType.DECODER
         sliding_window = 10
         static_forward_context = {}
diff --git a/tpu_inference/utils.py b/tpu_inference/utils.py
index 18a0a8b24..08cb23ae5 100644
--- a/tpu_inference/utils.py
+++ b/tpu_inference/utils.py
@@ -150,6 +150,10 @@ def hbm_usage_gb(devices: Any) -> List[Tuple[float, float]]:
 
 def get_padded_head_dim(head_dim: int) -> int:
     """Pads head_dim up to the nearest multiple of 128 for kernel performance."""
+    # When head_dim == 64, we use kernel specificly optimized for it which does
+    # not require any padding.
+    if head_dim == 64:
+        return 64
     return (head_dim + 127) // 128 * 128