diff --git a/tests/layers/vllm/test_attention.py b/tests/layers/vllm/test_attention.py
index df1f0395f..002f2641a 100644
--- a/tests/layers/vllm/test_attention.py
+++ b/tests/layers/vllm/test_attention.py
@@ -30,9 +30,7 @@
 # Number of attention heads (Key/Value) - for Grouped-Query Attention
 NUM_KV_HEADS = 4
 # Dimension of each attention head
-HEAD_DIM = 64
-# Padded head dimension
-PADDED_HEAD_DIM = 64
+HEAD_DIM = 128
 # Total number of blocks in the KV cache
 NUM_BLOCKS = 32
 # Number of tokens per block
diff --git a/tpu_inference/runner/kv_cache.py b/tpu_inference/runner/kv_cache.py
index 236e86c5d..096be0164 100644
--- a/tpu_inference/runner/kv_cache.py
+++ b/tpu_inference/runner/kv_cache.py
@@ -8,6 +8,7 @@
 from torchax.ops.mappings import t2j_dtype
 
 import tpu_inference.kernels.ragged_paged_attention.v3.kernel as rpa
+import tpu_inference.kernels.ragged_paged_attention.v3.kernel_hd64 as rpa_hd64
 from tpu_inference.logger import init_logger
 
 logger = init_logger(__name__)
@@ -22,10 +23,18 @@ def get_kv_cache_shape_with_mesh(mesh: Mesh, total_num_pages: int,
 
     model_cnt = mesh.shape["model"]
     assert actual_num_kv_heads % model_cnt == 0
+    # NOTE(chengjiyao): Currently, the attention kernel is tailored to the
+    # specific model, rather than being determined by the head_dim. If new
+    # models are introduced with a head_dim of 64, this will require additional
+    # model-specific adjustments.
+    get_kv_cache_shape_fn = (
+        rpa_hd64.get_kv_cache_shape if actual_head_dim == 64 \
+            else rpa.get_kv_cache_shape
+    )
     shape = list(
-        rpa.get_kv_cache_shape(total_num_pages, page_size,
-                               actual_num_kv_heads // model_cnt,
-                               actual_head_dim, kv_dtype))
+        get_kv_cache_shape_fn(total_num_pages, page_size,
+                              actual_num_kv_heads // model_cnt,
+                              actual_head_dim, kv_dtype))
     shape[2] *= model_cnt
     return tuple(shape)