diff --git a/include/flashinfer/attention/scheduler.cuh b/include/flashinfer/attention/scheduler.cuh
index 7087101a5..44e8c0b40 100644
--- a/include/flashinfer/attention/scheduler.cuh
+++ b/include/flashinfer/attention/scheduler.cuh
@@ -1098,7 +1098,7 @@ inline cudaError_t MLAPlan(void* float_buffer, size_t float_workspace_size_in_by
       total_kv_lens += effective_kv_len;
     }
   }
-  int kv_len_limit = ceil_div(ceil_div(total_kv_lens, num_clusters), 512L) * 512L;
+  int kv_len_limit = ceil_div(std::max(ceil_div(total_kv_lens, num_clusters), 1L), 512L) * 512L;
 
   // step 1. load-balancing scheduling algorithm
   MinHeap cluster_cost_heap(num_clusters);
diff --git a/tests/test_deepseek_mla.py b/tests/test_deepseek_mla.py
index 9448828c2..e93aa1798 100644
--- a/tests/test_deepseek_mla.py
+++ b/tests/test_deepseek_mla.py
@@ -189,7 +189,7 @@ def generate_kv_from_cache(ckv, kpe, kv_len, batch_size, num_heads):
 @pytest.mark.parametrize("num_heads", [16, 32, 64])
 @pytest.mark.parametrize("causal", [False, True])
 @pytest.mark.parametrize("page_size", [1])
-@pytest.mark.parametrize("backend", ["fa2", "fa3"])
+@pytest.mark.parametrize("backend", ["fa3"])
 @pytest.mark.parametrize("dtype", [torch.half])
 def test_batch_mla_varlen_page_attention(
     batch_size,
@@ -311,15 +311,16 @@ def test_batch_mla_varlen_page_attention(
         #     torch.testing.assert_close(lse_i, lse_ref, rtol=1e-3, atol=1e-3)
 
 
-@pytest.mark.parametrize("batch_size", [1, 2, 3, 4, 5, 6, 7])
+@pytest.mark.parametrize("batch_size", [1, 2, 3, 4, 5, 6, 7, 157])
 @pytest.mark.parametrize("kv_len", [0, 17, 33, 96, 97, 114, 514, 1024])
 @pytest.mark.parametrize(
     "qo_len", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
 )
 @pytest.mark.parametrize("num_heads", [16])
 @pytest.mark.parametrize("causal", [False, True])
-@pytest.mark.parametrize("page_size", [1])
+@pytest.mark.parametrize("page_size", [1, 16])
 @pytest.mark.parametrize("backend", ["fa2", "fa3"])
+@pytest.mark.parametrize("use_cuda_graph", [True, False])
 @pytest.mark.parametrize("dtype", [torch.half])
 def test_batch_mla_page_attention(
     batch_size,
@@ -329,6 +330,7 @@ def test_batch_mla_page_attention(
     causal,
     page_size,
     backend,
+    use_cuda_graph,
     dtype,
 ):
     if not mla_is_fa3_supported(torch.device("cuda")):
@@ -362,12 +364,51 @@ def test_batch_mla_page_attention(
     sm_scale = 1.0 / ((128 + 64) ** 0.5)  # use head dimension before matrix absorption
     workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8).to(0)
     wrapper = flashinfer.mla.BatchMLAPagedAttentionWrapper(
-        workspace_buffer, backend=backend
+        workspace_buffer,
+        backend=backend,
+        use_cuda_graph=True,
+        qo_indptr=torch.empty(batch_size + 1, dtype=torch.int32, device="cuda"),
+        kv_indptr=torch.empty(batch_size + 1, dtype=torch.int32, device="cuda"),
+        kv_indices=torch.empty(1048576, dtype=torch.int32, device="cuda"),
+        kv_len_arr=torch.empty(batch_size, dtype=torch.int32, device="cuda"),
     )
     q_indptr = torch.arange(0, batch_size + 1).to(0).int() * qo_len
     kv_indptr = torch.arange(0, batch_size + 1).to(0).int() * pages_num
     kv_indices = torch.arange(0, batch_size * pages_num).to(0).int()
     kv_lens = torch.full((batch_size,), kv_len, dtype=torch.int32).to(0)
+
+    if use_cuda_graph:
+        kv_indptr_warmup = torch.zeros(batch_size + 1).to(0).int()
+        kv_indices_warmup = torch.arange(0, batch_size).to(0).int()
+        kv_lens_warmup = torch.full((batch_size,), 0, dtype=torch.int32).to(0)
+        wrapper.plan(
+            q_indptr,
+            kv_indptr_warmup,
+            kv_indices_warmup,
+            kv_lens_warmup,
+            num_heads,
+            head_dim_ckv,
+            head_dim_kpe,
+            page_size,
+            causal,
+            sm_scale,
+            q_nope.dtype,
+            ckv.dtype,
+        )
+
+        # warmup
+        s = torch.cuda.Stream()
+        s.wait_stream(torch.cuda.current_stream())
+        with torch.cuda.stream(s):
+            for _ in range(3):
+                o, lse = wrapper.run(q_nope, q_pe, ckv, kpe, return_lse=True)
+        torch.cuda.current_stream().wait_stream(s)
+
+        # capture
+        g = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(g):
+            o, lse = wrapper.run(q_nope, q_pe, ckv, kpe, return_lse=True)
+
     wrapper.plan(
         q_indptr,
         kv_indptr,
@@ -382,7 +423,12 @@ def test_batch_mla_page_attention(
         q_nope.dtype,
         ckv.dtype,
     )
-    o, lse = wrapper.run(q_nope, q_pe, ckv, kpe, return_lse=True)
+    if use_cuda_graph:
+        o.fill_(0)
+        lse.fill_(0)
+        g.replay()
+    else:
+        o, lse = wrapper.run(q_nope, q_pe, ckv, kpe, return_lse=True)
 
     k, v = generate_kv_from_cache(ckv, kpe, kv_len, batch_size, num_heads)
 
@@ -408,3 +454,4 @@ def test_batch_mla_page_attention(
     test_batch_mla_varlen_page_attention(
         155, 1024, 8, 128, 128, 16, False, 1, "fa3", torch.half
     )
+    test_batch_mla_page_attention(1, 1024, 128, 128, False, 1, "fa2", True, torch.half)