diff --git a/vllm/v1/attention/backends/pallas.py b/vllm/v1/attention/backends/pallas.py
index 32ef5dc2e36..b7fc1ffeb65 100644
--- a/vllm/v1/attention/backends/pallas.py
+++ b/vllm/v1/attention/backends/pallas.py
@@ -326,7 +326,54 @@ def kv_cache_update_op_non_xla(kv: torch.Tensor, slot_mapping: torch.Tensor,
     return kv_cache
 
 
+# We can move this function to a common utils file if it's also useful for other
+# hardware.
+def dtype_bits(dtype: torch.dtype):
+    if dtype.is_floating_point:
+        try:
+            return torch.finfo(dtype).bits
+        except TypeError:
+            pass
+    elif dtype.is_complex:
+        if dtype is torch.complex32:
+            return 32
+        elif dtype is torch.complex64:
+            return 64
+        elif dtype is torch.complex128:
+            return 128
+    else:
+        try:
+            return torch.iinfo(dtype).bits
+        # torch.iinfo cannot support int4, int2, bits8...
+        except TypeError:
+            pass
+    str_dtype = str(dtype)
+    # support torch.int4, torch.int5, torch.uint5...
+    if str_dtype.startswith("torch.int") or str_dtype.startswith("torch.uint"):
+        return int(str_dtype[-1])
+    raise TypeError(f"Getting the bit width of {dtype} is not supported")
+
+
+def get_dtype_packing(dtype):
+    bits = dtype_bits(dtype)
+    if 32 % bits != 0:
+        raise ValueError(
+            f"The bit width must be divisible by 32, but got bits={bits}, "
+            "dtype={dtype}")
+    return 32 // bits
+
+
 def get_page_size_bytes(block_size: int, num_kv_heads: int, head_size: int,
                         kv_cache_dtype: torch.dtype) -> int:
     """Returns the size in bytes of one page of the KV cache."""
-    return block_size * num_kv_heads * head_size * kv_cache_dtype.itemsize
+    padded_head_size = cdiv(head_size,
+                            TPU_HEAD_SIZE_ALIGNMENT) * TPU_HEAD_SIZE_ALIGNMENT
+    num_combined_kv_heads = num_kv_heads * 2
+
+    # NOTE: for the implicit padding in XLA
+    packing = get_dtype_packing(kv_cache_dtype)
+    num_combined_kv_heads = cdiv(num_combined_kv_heads, packing) * packing
+
+    kv_cache_dtype_bits = dtype_bits(kv_cache_dtype)
+    return (block_size * num_combined_kv_heads * padded_head_size *
+            kv_cache_dtype_bits // 8)
diff --git a/vllm/v1/worker/tpu_model_runner.py b/vllm/v1/worker/tpu_model_runner.py
index 83a80bd865b..a1d66d1fa40 100644
--- a/vllm/v1/worker/tpu_model_runner.py
+++ b/vllm/v1/worker/tpu_model_runner.py
@@ -1863,8 +1863,9 @@ def _get_num_slices_per_kv_cache_update_block(page_size_bytes: int) -> int:
     out of scalar registers. Thus this function will limit the number of
     slices to 64.
     """
-    # Conservative VMEM usage limit: 32 MiB
-    vmem_limit = 32 * 1024 * 1024
+    # The default vmem_limit_bytes of a pallas kernel is 32MB. Here we
+    # calculate num_slices_per_block based on 16MB in case any register spills.
+    vmem_limit = 16 * 1024 * 1024
     num_slices_per_block = vmem_limit // page_size_bytes
     assert num_slices_per_block > 0, "Number of slices should be positive"
     num_slices_per_block = prev_power_of_2(num_slices_per_block)