diff --git a/benchmarks/routines/attention.py b/benchmarks/routines/attention.py
index 96b7a8f58b..320cfbe020 100644
--- a/benchmarks/routines/attention.py
+++ b/benchmarks/routines/attention.py
@@ -396,7 +396,7 @@ def testBatchDecodeWithPagedKVCacheWrapper(args):
     # Now initialize the page tables
     block_tables = torch.tensor(
         [
-            [k + i * num_pages_per_seq for k in range(num_pages_per_seq)]
+            [k + i * num_pages_per_seq for k in torch.randperm(num_pages_per_seq)]
             for i in range(batch_size)
         ],
         dtype=torch.int,
@@ -421,11 +421,7 @@ def testBatchDecodeWithPagedKVCacheWrapper(args):
     for i in range(len(kv_indptr) - 1):
         start_idx = kv_indptr[i]
         end_idx = kv_indptr[i + 1]
-        kv_indices[start_idx:end_idx] = torch.arange(
-            i * num_pages_per_seq,
-            i * num_pages_per_seq + (end_idx - start_idx),
-            device=device,
-        )
+        kv_indices[start_idx:end_idx] = block_tables[i, : end_idx - start_idx]
 
     kv_last_page_len = (
         torch.where(
@@ -837,7 +833,7 @@ def testBatchPrefillWithPagedKVCacheWrapper(args):
     # Now initialize the page tables
     block_tables = torch.tensor(
         [
-            [k + i * num_pages_per_seq for k in range(num_pages_per_seq)]
+            [k + i * num_pages_per_seq for k in torch.randperm(num_pages_per_seq)]
             for i in range(batch_size)
         ],
         dtype=torch.int,
@@ -887,11 +883,7 @@ def testBatchPrefillWithPagedKVCacheWrapper(args):
     for i in range(len(kv_indptr) - 1):
         start_idx = kv_indptr[i]
         end_idx = kv_indptr[i + 1]
-        kv_indices[start_idx:end_idx] = torch.arange(
-            i * num_pages_per_seq,
-            i * num_pages_per_seq + (end_idx - start_idx),
-            device=device,
-        )
+        kv_indices[start_idx:end_idx] = block_tables[i, : end_idx - start_idx]
     kv_last_page_len = (
         torch.where(
             actual_seq_lens_kv_device.flatten() % page_size == 0,
@@ -1711,7 +1703,7 @@ def testBatchMLAPagedAttentionWrapper(args):
     # Now initialize the page tables
     block_tables = torch.tensor(
         [
-            [k + i * num_pages_per_seq for k in range(num_pages_per_seq)]
+            [k + i * num_pages_per_seq for k in torch.randperm(num_pages_per_seq)]
             for i in range(batch_size)
         ],
         dtype=torch.int,
@@ -1758,11 +1750,7 @@ def testBatchMLAPagedAttentionWrapper(args):
     for i in range(len(kv_indptr) - 1):
         start_idx = kv_indptr[i]
         end_idx = kv_indptr[i + 1]
-        kv_indices[start_idx:end_idx] = torch.arange(
-            i * num_pages_per_seq,
-            i * num_pages_per_seq + (end_idx - start_idx),
-            device=device,
-        )
+        kv_indices[start_idx:end_idx] = block_tables[i, : end_idx - start_idx]
 
     sm_scale = 1.0 / ((128 + 64) ** 0.5)  # For DeepSeek-R1
     workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8, device=device)