diff --git a/tests/v1/attention/test_indexer_tp_row_shard.py b/tests/v1/attention/test_indexer_tp_row_shard.py
index e65e4789f1..4bbbf93eb9 100644
--- a/tests/v1/attention/test_indexer_tp_row_shard.py
+++ b/tests/v1/attention/test_indexer_tp_row_shard.py
@@ -10,9 +10,11 @@ import torch
 
 import vllm.model_executor.layers.sparse_attn_indexer as sparse_indexer
 import vllm.v1.attention.backends.mla.indexer as indexer
-from vllm.config import CUDAGraphMode
+from vllm.config import AttentionConfig, CUDAGraphMode
 from vllm.model_executor.layers.sparse_attn_indexer import PrefillRowShard
 
+MIN_ROWS = AttentionConfig().indexer_tp_shard_min_rows
+
 
 def _chunk(start: int, end: int) -> SimpleNamespace:
     """Chunk over rows [start, end); bounds derive from the global row."""
@@ -80,11 +82,11 @@ def test_shard_exchange_reassembles_rows_only(
     assert torch.all(buffer[decode + sum(sizes) :] == -7), "padding untouched"
 
 
-@pytest.mark.parametrize("tp_size", [2, 4])
+@pytest.mark.parametrize("tp_size", [2, 4, 8])
 def test_balanced_row_shard_partitions_and_declines(tp_size: int) -> None:
     """The split covers every row once with >= 1 row per rank and declines
     below the activation floor (or on a single rank)."""
-    n = indexer.MIN_TP_SHARD_ROWS_PER_RANK * tp_size
+    n = MIN_ROWS
     shapes = [
         ([4 * n], [4 * n]),  # fresh prompt: the full causal cost ramp
         # uneven requests at different context depths
@@ -97,12 +99,38 @@ def test_balanced_row_shard_partitions_and_declines(tp_size: int) -> None:
             torch.tensor(query_lens, dtype=torch.int32),
             ratio,
             tp_size,
+            n,
         )
         assert sizes is not None and len(sizes) == tp_size
         assert sum(sizes) == sum(query_lens) and min(sizes) >= 1
     lens = torch.tensor([n - 1], dtype=torch.int32)
-    assert indexer.balanced_prefill_row_shard(lens, lens, ratio, tp_size) is None
-    assert indexer.balanced_prefill_row_shard(lens, lens, ratio, 1) is None
+    assert indexer.balanced_prefill_row_shard(lens, lens, ratio, tp_size, n) is None
+    assert indexer.balanced_prefill_row_shard(lens, lens, ratio, 1, n) is None
+
+
+def test_row_shard_threshold_is_total_not_per_rank() -> None:
+    """The activation floor counts TOTAL rows regardless of tp_size: a
+    per-rank floor would need 128K rows at TP8, i.e. an unschedulable
+    --max-num-batched-tokens of 128K on 80 GB Hopper nodes."""
+    lens = torch.tensor([MIN_ROWS], dtype=torch.int32)
+    for tp_size in (2, 4, 8):
+        sizes = indexer.balanced_prefill_row_shard(lens, lens, 4, tp_size, MIN_ROWS)
+        assert sizes is not None
+
+
+def test_tp2_old_per_rank_window_stays_replicated() -> None:
+    """32K-64K total rows engaged on TP2 under the old per-rank floor
+    (16K rows/rank); the total floor now keeps them replicated — the
+    all_gatherv latency term is largest at small tp."""
+    window = torch.tensor([3 * MIN_ROWS // 4], dtype=torch.int32)
+    assert indexer.balanced_prefill_row_shard(window, window, 4, 2, MIN_ROWS) is None
+
+
+def test_row_shard_floor_is_at_least_tp_size() -> None:
+    """A floor below tp_size still leaves every rank at least one row."""
+    lens = torch.tensor([8], dtype=torch.int32)
+    assert indexer.balanced_prefill_row_shard(lens, lens, 4, 8, 1) is not None
+    assert indexer.balanced_prefill_row_shard(lens - 1, lens - 1, 4, 8, 1) is None
 
 
 def test_row_sharding_gate_rejects_unsupported_configurations(
diff --git a/vllm/config/attention.py b/vllm/config/attention.py
index 67605f3c7e..fc3ecb0dfc 100644
--- a/vllm/config/attention.py
+++ b/vllm/config/attention.py
@@ -103,6 +103,10 @@ class AttentionConfig:
     candidate pool instead, with fp32 logits, and keeps the masked dense walk
     for steps whose contexts are too short for the pool to pay."""
 
+    indexer_tp_shard_min_rows: int = Field(default=65536, ge=1)
+    """Minimum total prefill rows for the sparse-attention indexer to split
+    them across TP ranks instead of scoring every row on each rank."""
+
     hisparse_config: HiSparseConfig | None = None
     """HiSparse host-resident KV configuration. Setting this enables experimental
     Model Runner V2-only HiSparse sparse-MLA decode hot-buffering. It is inferred
diff --git a/vllm/v1/attention/backends/mla/indexer.py b/vllm/v1/attention/backends/mla/indexer.py
index 4f18173343..468040257e 100644
--- a/vllm/v1/attention/backends/mla/indexer.py
+++ b/vllm/v1/attention/backends/mla/indexer.py
@@ -205,19 +205,16 @@ class PrepareUniformDecodeKernel(
         )
 
 
-# Below this many rows per rank, stay replicated (TP4 engages at 64K rows).
-MIN_TP_SHARD_ROWS_PER_RANK = 16_384
-
-
 def balanced_prefill_row_shard(
     seq_lens_cpu: torch.Tensor,
     query_lens_cpu: torch.Tensor,
     compress_ratio: int,
     tp_size: int,
+    min_rows: int,
 ) -> list[int] | None:
     """Return contiguous TP row counts balanced by indexer MQA work."""
     num_rows = int(query_lens_cpu.sum())
-    if tp_size < 2 or num_rows < MIN_TP_SHARD_ROWS_PER_RANK * tp_size:
+    if tp_size < 2 or num_rows < max(min_rows, tp_size):
         return None
 
     query_lens = query_lens_cpu.to(torch.int64)
@@ -1584,6 +1581,7 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
                     prefill_query_lens_cpu,
                     self.compress_ratio,
                     self.tp_size,
+                    self.vllm_config.attention_config.indexer_tp_shard_min_rows,
                 )
             prefill_metadata = DeepseekV32IndexerPrefillMetadata(
                 chunks,