diff --git a/cosmos_framework/model/generator/mot/attention.py b/cosmos_framework/model/generator/mot/attention.py
index 45123e37..89dfad34 100644
--- a/cosmos_framework/model/generator/mot/attention.py
+++ b/cosmos_framework/model/generator/mot/attention.py
@@ -353,8 +353,15 @@ def multi_control_two_way_attention(
       - ctrl_i output: from pass i only
       - noisy output:  w_1 * noisy_out_1 + ... + w_N * noisy_out_N  (weighted sum)
 
-    All SDPA calls are maskless → Flash Attention is always active.
-    N=1, w=1.0 → identical to ``two_way_attention``.
+    All SDPA calls are maskless. N=1, w=1.0 → identical to ``two_way_attention``.
+
+    Dense vs varlen:
+      Single-sample inference uses the dense attention API (no cumulative/max
+      seqlen args), matching ``two_way_attention``. That lets the chooser keep
+      cuDNN on sm_120 (workstation Blackwell), where Cosmos prefers cuDNN but
+      the current cuDNN integration rejects varlen and would otherwise fall back
+      to NATTEN kernels that lack sm_120 images. Training / multi-sample packs
+      keep the varlen path.
 
     Padding safety:
       Both ``get_causal_seq`` and ``get_full_only_seq`` can return padded rows.
@@ -374,22 +381,34 @@ def multi_control_two_way_attention(
     noisy_s, noisy_e = split_info.noisy_token_range
     weights = split_info.control_weights
 
-    # ── 1. Text self-attention (causal, unchanged) ───────────────────────────
+    # ── 1. Text self-attention (causal) ──────────────────────────────────────
     causal_q, causal_q_offsets = get_causal_seq(packed_query_states)
     causal_k, causal_k_offsets = get_causal_seq(packed_key_states)
     causal_v, _ = get_causal_seq(packed_value_states)
 
+    # Mirror two_way_attention: dense FMHA for single-sample inference so cuDNN
+    # remains eligible on sm_120; varlen only when training / multi-sample.
+    sample_offsets = packed_query_states["sample_offsets"]
+    num_samples = sample_offsets.shape[0] - 1
+    use_dense = num_samples == 1 and not torch.is_grad_enabled()
+
     use_dont_care_mask = causal_q_offsets is causal_k_offsets
+    if use_dense:
+        causal_varlen_kwargs = {}
+    else:
+        causal_varlen_kwargs = dict(
+            cumulative_seqlen_Q=causal_q_offsets,
+            cumulative_seqlen_KV=causal_k_offsets,
+            max_seqlen_Q=packed_query_states["max_causal_len"],
+            max_seqlen_KV=packed_query_states["max_causal_len"],
+        )
     causal_res = attention(
         causal_q.unsqueeze(0),
         causal_k.unsqueeze(0),
         causal_v.unsqueeze(0),
-        cumulative_seqlen_Q=causal_q_offsets,
-        cumulative_seqlen_KV=causal_k_offsets,
-        max_seqlen_Q=packed_query_states["max_causal_len"],
-        max_seqlen_KV=packed_query_states["max_causal_len"],
         is_causal=True,
         causal_type=CausalType.DontCare if use_dont_care_mask else CausalType.TopLeft,
+        **causal_varlen_kwargs,
     )
     causal_out = causal_res.squeeze(0).flatten(-2, -1)  # [N_text, Hq*D]
 
@@ -432,30 +451,37 @@ def _sdpa(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
         torch._check(k.shape[0] == v.shape[0])
         n_q, n_kv = q.shape[0], k.shape[0]
         # These lengths come from data-dependent unpadding, so they are unbacked
-        # symints under torch.compile. The selected attention backend (NATTEN)
-        # validates varlen inputs with `max_seqlen == 0` / `max_seqlen < 1`
-        # guards; without a positivity fact Dynamo cannot discharge `Eq(n, 0)`.
-        # Every control/noisy segment always has at least one token, so assert it.
+        # symints under torch.compile. Varlen backends validate with
+        # `max_seqlen == 0` / `max_seqlen < 1` guards; without a positivity fact
+        # Dynamo cannot discharge `Eq(n, 0)`. Every control/noisy segment always
+        # has at least one token, so assert it (needed for the varlen path).
         torch._check(n_q > 0)
         torch._check(n_kv > 0)
-        # Pass cumulative_seqlen_{Q,KV} + max_seqlen_{Q,KV} directly instead of
+        # Dense (inference, single sample): omit varlen args so cuDNN stays
+        # eligible on sm_120. Varlen (training / multi-sample): pass
+        # cumulative_seqlen_{Q,KV} + max_seqlen_{Q,KV} directly instead of
         # seqlens_{Q,KV}. The frontend derives cumulative offsets from seqlens via
         # `generate_varlen_parameters`, which calls `.max().item()` (a device-host
         # sync) and is explicitly disallowed inside a torch.compile region. Each
-        # pass here is a single (batch=1) packed sequence, so the cumulative
-        # offsets are simply [0, n]. Building them ourselves keeps the whole path
-        # inside the compiled graph.
-        zero = torch.zeros(1, dtype=torch.int32, device=q.device)
-        cu_seqlens_q = torch.cat([zero, torch.tensor([n_q], dtype=torch.int32, device=q.device)])
-        cu_seqlens_kv = torch.cat([zero, torch.tensor([n_kv], dtype=torch.int32, device=q.device)])
+        # pass here is a single packed sequence, so the cumulative offsets are
+        # simply [0, n]. Building them ourselves keeps the path compiled.
+        if use_dense:
+            varlen_kwargs = {}
+        else:
+            zero = torch.zeros(1, dtype=torch.int32, device=q.device)
+            cu_seqlens_q = torch.cat([zero, torch.tensor([n_q], dtype=torch.int32, device=q.device)])
+            cu_seqlens_kv = torch.cat([zero, torch.tensor([n_kv], dtype=torch.int32, device=q.device)])
+            varlen_kwargs = dict(
+                cumulative_seqlen_Q=cu_seqlens_q,
+                cumulative_seqlen_KV=cu_seqlens_kv,
+                max_seqlen_Q=n_q,
+                max_seqlen_KV=n_kv,
+            )
         res = attention(
             q.unsqueeze(0),  # [1, N_q,  Hq,  D]
             k.unsqueeze(0),  # [1, N_kv, Hkv, D]
             v.unsqueeze(0),  # [1, N_kv, Hkv, D]
-            cumulative_seqlen_Q=cu_seqlens_q,
-            cumulative_seqlen_KV=cu_seqlens_kv,
-            max_seqlen_Q=n_q,
-            max_seqlen_KV=n_kv,
+            **varlen_kwargs,
         )  # [1, N_q, Hq, D]
         return res.squeeze(0).flatten(-2, -1)  # [N_q, Hq*D]
 
diff --git a/cosmos_framework/model/generator/mot/attention_test.py b/cosmos_framework/model/generator/mot/attention_test.py
index 27da6dc8..9670b28f 100644
--- a/cosmos_framework/model/generator/mot/attention_test.py
+++ b/cosmos_framework/model/generator/mot/attention_test.py
@@ -400,6 +400,62 @@ def fake_two_way_attention(*args: object, **kwargs: object) -> object:
     assert kv_to_store is None
 
 
+@pytest.mark.L0
+@pytest.mark.CPU
+def test_multi_control_matches_two_way_dense_inference_convention(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Single-sample inference is dense; grad-enabled execution remains varlen."""
+
+    def make_pack(x: torch.Tensor):
+        return build_packed_sequence(
+            "two_way",
+            packed_sequence=x,
+            attn_modes=["causal", "full"],
+            split_lens=[2, 4],
+            sample_lens=[6],
+            packed_und_token_indexes=torch.tensor([0, 1], dtype=torch.long),
+            packed_gen_token_indexes=torch.tensor([2, 3, 4, 5], dtype=torch.long),
+            num_heads=1,
+            head_dim=8,
+            num_layers=1,
+        )
+
+    q_pack, split_info, _ = make_pack(torch.arange(48, dtype=torch.float32).reshape(6, 1, 8))
+    raw_k_pack, _, _ = make_pack(torch.zeros(6, 1, 8))
+    v_pack, _, _ = make_pack(torch.ones(6, 1, 8))
+    split_info.control_stream_token_ranges = [(0, 2)]
+    split_info.noisy_token_range = (2, 4)
+    split_info.control_weights = [1.0]
+
+    calls: list[dict[str, object]] = []
+
+    def fake_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, **kwargs: object) -> torch.Tensor:
+        calls.append(kwargs)
+        return q
+
+    monkeypatch.setattr(attention, "attention", fake_attention)
+
+    with torch.no_grad():
+        attention.multi_control_two_way_attention(
+            q_pack,
+            raw_k_pack,
+            v_pack,
+            split_info,
+        )
+
+    assert len(calls) == 3  # causal text, control, noisy
+    assert all("cumulative_seqlen_Q" not in call for call in calls)
+
+    calls.clear()
+    attention.multi_control_two_way_attention(
+        q_pack,
+        raw_k_pack,
+        v_pack,
+        split_info,
+    )
+    assert len(calls) == 3
+    assert all("cumulative_seqlen_Q" in call for call in calls)
+
+
 @pytest.mark.L0
 def test_dispatch_attention_rejects_incomplete_split_info() -> None:
     foreign_split_info = _foreign_split_info()
diff --git a/cosmos_framework/model/generator/mot/cosmos3_vfm_network.py b/cosmos_framework/model/generator/mot/cosmos3_vfm_network.py
index cdb2cbcf..25a8b1e8 100644
--- a/cosmos_framework/model/generator/mot/cosmos3_vfm_network.py
+++ b/cosmos_framework/model/generator/mot/cosmos3_vfm_network.py
@@ -1010,8 +1010,8 @@ def forward(
         # one per control.  For each pass i, KV = [text | ctrl_i | noisy].
         # The final noisy output is the weighted sum of the N pass outputs:
         #   noisy_out = w_1 * noisy_out_1 + ... + w_N * noisy_out_N
-        # All SDPA calls are maskless → Flash Attention always active.
-        # N=1, w=1.0 → identical to two_way_attention.
+        # Single-sample inference uses dense FMHA (cuDNN-eligible on sm_120);
+        # see multi_control_two_way_attention. N=1, w=1.0 → identical to two_way_attention.
         #
         # CP compatibility: control_stream_token_ranges are gen-relative global
         # offsets computed here, before CP sharding.  Ulysses CP restores the full