diff --git a/include/flashinfer/trtllm/fmha/kernelParams.h b/include/flashinfer/trtllm/fmha/kernelParams.h
index c184ad9e10..bc45832968 100644
--- a/include/flashinfer/trtllm/fmha/kernelParams.h
+++ b/include/flashinfer/trtllm/fmha/kernelParams.h
@@ -331,6 +331,12 @@ struct KernelParams {
       strideHeads = options.vStrideHeads;
       strideBatch = options.vStrideBatch;
     }
+
+    // Ragged layout has no batch stride; reset negative overflow to 0 for TMA descriptor.
+    if (!isPagedKv(options.mQkvLayout) && !isContiguousKv(options.mQkvLayout) && strideBatch < 0) {
+      strideBatch = 0;
+    }
+
     // The 3 strides (the other ones are 1 and 0).
     return std::make_tuple(strideKeysVals, strideHeads, strideBatch);
   }
diff --git a/tests/attention/test_trtllm_ragged_kv_stride.py b/tests/attention/test_trtllm_ragged_kv_stride.py
new file mode 100644
index 0000000000..69c11359a7
--- /dev/null
+++ b/tests/attention/test_trtllm_ragged_kv_stride.py
@@ -0,0 +1,118 @@
+import pytest
+import torch
+
+import flashinfer
+from flashinfer.utils import get_compute_capability
+
+
+@pytest.mark.cuda
+def test_trtllm_ragged_kv_large_stride_overflow():
+    """
+    Test that ragged KV with large numel (>2^31) doesn't cause TMA descriptor error.
+
+    Constructs a scenario where key.numel() = 131072 * 128 * 192 > 2^31, which
+    triggers int32 overflow in kStrideBatch. Before the fix, this caused negative
+    stride and TMA descriptor error. After the fix, negative strideBatch is clamped
+    to 0 for ragged layouts.
+    """
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA is not available")
+
+    if not hasattr(flashinfer.prefill, "trtllm_ragged_attention_deepseek"):
+        pytest.skip("trtllm_ragged_attention_deepseek is not available in this build")
+
+    device = torch.device("cuda")
+    compute_capability = get_compute_capability(device)
+    if compute_capability[0] != 10:
+        pytest.skip(
+            f"TRTLLM-gen ragged attention requires SM100 and SM103 GPUs, got sm{compute_capability[0]}{compute_capability[1]}"
+        )
+
+    torch.manual_seed(42)
+
+    # Configuration that triggers numel > 2^31
+    batch_size = 16
+    max_kv_len = 8192
+    num_kv_heads = 128
+    head_dim_qk = 192
+    head_dim_vo = 128
+
+    # Construct ragged Q
+    seq_lens_q = torch.randint(
+        low=50, high=150, size=(batch_size,), device=device, dtype=torch.int32
+    )
+    cum_seq_lens_q = torch.cat(
+        [
+            torch.zeros(1, device=device, dtype=torch.int32),
+            torch.cumsum(seq_lens_q, dim=0, dtype=torch.int32),
+        ],
+        dim=0,
+    )
+    total_q = int(cum_seq_lens_q[-1].item())
+    max_q_len = int(seq_lens_q.max().item())
+
+    q = torch.randn(
+        total_q,
+        num_kv_heads,
+        head_dim_qk,
+        device=device,
+        dtype=torch.bfloat16,
+    )
+
+    # Construct ragged KV: total_kv = 16 * 8192 = 131072
+    # key.numel() = 131072 * 128 * 192 = 3,221,225,472 (0xC0000000) > 2^31
+    seq_lens_kv = torch.full(
+        (batch_size,), max_kv_len, device=device, dtype=torch.int32
+    )
+    cum_seq_lens_kv = torch.arange(
+        0,
+        (batch_size + 1) * max_kv_len,
+        max_kv_len,
+        device=device,
+        dtype=torch.int32,
+    )
+    total_kv = int(cum_seq_lens_kv[-1].item())
+
+    k = torch.randn(
+        total_kv,
+        num_kv_heads,
+        head_dim_qk,
+        device=device,
+        dtype=torch.bfloat16,
+    )
+    v = torch.randn(
+        total_kv,
+        num_kv_heads,
+        head_dim_vo,
+        device=device,
+        dtype=torch.bfloat16,
+    )
+
+    workspace_buffer = torch.zeros(128 * 1024 * 1024, dtype=torch.uint8, device=device)
+    scale = float(1.0 / (head_dim_qk**0.5))
+
+    # Should not raise "buildNdTmaDescriptor: invalid argument" error
+    output = flashinfer.prefill.trtllm_ragged_attention_deepseek(
+        query=q,
+        key=k,
+        value=v,
+        workspace_buffer=workspace_buffer,
+        seq_lens=seq_lens_kv,
+        max_q_len=max_q_len,
+        max_kv_len=max_kv_len,
+        bmm1_scale=scale,
+        bmm2_scale=1.0,
+        o_sf_scale=1.0,
+        batch_size=batch_size,
+        window_left=-1,
+        cum_seq_lens_q=cum_seq_lens_q,
+        cum_seq_lens_kv=cum_seq_lens_kv,
+        enable_pdl=False,
+        is_causal=True,
+        return_lse=False,
+    )
+
+    # Basic shape check
+    assert output.shape[0] == total_q
+    assert output.shape[1] == num_kv_heads
+    assert output.shape[2] == head_dim_vo
diff --git a/tests/comm/test_trtllm_mnnvl_allreduce_custom_comm.py b/tests/comm/test_trtllm_mnnvl_allreduce_custom_comm.py
index 60933cf89b..772ceead0b 100644
--- a/tests/comm/test_trtllm_mnnvl_allreduce_custom_comm.py
+++ b/tests/comm/test_trtllm_mnnvl_allreduce_custom_comm.py
@@ -250,7 +250,7 @@ def test_mnnvl_allreduce_custom_communicator(
 
     available_gpus = torch.cuda.device_count()
     if world_size > available_gpus:
-        raise ValueError(
+        pytest.skip(
             f"world_size {world_size} is greater than available_gpus {available_gpus}"
         )
     print(f"Running test for world_size={world_size}")