diff --git a/tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.py b/tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.py
index 7e5d24ae7a5f..95a21311ce44 100644
--- a/tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.py
+++ b/tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.py
@@ -996,9 +996,14 @@ def forward(self, x, position_ids):
             .expand(3, position_ids.shape[1], -1, 1)
             .to(x.device)
         )
-        position_ids_expanded = position_ids[:, :, None, :].float()  # shape (3, bs, 1, positions)
-
-        freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(2, 3)
+        position_ids_expanded = position_ids[:, :, None, :]  # shape (3, bs, 1, positions)
+
+        # The diffusers/transformers reference writes this outer product as
+        # `inv_freq @ position_ids`. That is a K=1 GEMM, and a GEMM may run in
+        # TF32, which cannot represent positions above 2048 and skews the
+        # rotary phase of late tokens. The broadcast multiply is the same
+        # arithmetic with no GEMM dispatch, so no precision mode applies.
+        freqs = (inv_freq_expanded * position_ids_expanded).transpose(2, 3)
         freqs = self.apply_interleaved_mrope(freqs, self.mrope_section)
         emb = torch.cat((freqs, freqs), dim=-1)
         cos = emb.cos() * self.attention_scaling
diff --git a/tests/unittest/_torch/visual_gen/test_cosmos3_transformer.py b/tests/unittest/_torch/visual_gen/test_cosmos3_transformer.py
index 9a8105b14d57..c167094dc84b 100644
--- a/tests/unittest/_torch/visual_gen/test_cosmos3_transformer.py
+++ b/tests/unittest/_torch/visual_gen/test_cosmos3_transformer.py
@@ -28,6 +28,7 @@
 from tensorrt_llm._torch.visual_gen.models.cosmos3.transformer_cosmos3 import (
     PRETRAINED_CONFIG_COMPAT_DEFAULTS,
     Cosmos3VFMTransformer,
+    Qwen3VLTextRotaryEmbedding,
     apply_pretrained_config_compat_defaults,
 )
 from tensorrt_llm._torch.visual_gen.pipeline_loader import PipelineComponent, PipelineLoader
@@ -854,3 +855,80 @@ def test_constructs_without_audio_or_action_towers(self):
         assert not hasattr(model, "audio_modality_embed")
         assert model.base_fps == 16
         assert len(model.gen_layers) == 2
+
+
+class TestRotaryTablePrecision:
+    """The mRoPE angle table must stay fp32-accurate when fp32 GEMMs run in TF32.
+
+    NGC PyTorch images set TORCH_ALLOW_TF32_CUBLAS_OVERRIDE=1. Positions above
+    2048 do not fit TF32's 10-bit mantissa, so a table built as a K=1 matmul is
+    off by radians for late tokens wherever cuBLAS picks a TF32 kernel for that
+    shape (Blackwell). The table is an outer product and must not go through a
+    GEMM. Checkpoint-free; Nano head_dim and rope axes."""
+
+    HEAD_DIM = 128
+    ROPE_AXES = [24, 20, 20]
+    # Text tokens (up to 4096) + margin + fps-scaled vision positions land well
+    # above 2048. cuBLAS picks a TF32 kernel for the K=1 GEMM only at some
+    # problem sizes, so sweep the sizes a Cosmos3 request actually produces.
+    SEQUENCE_LENGTHS = [4096, 6240, 8192, 10336, 16384]
+    # Text ids are int64; fps-modulated vision, audio and action ids are fp32.
+    POSITION_DTYPES = [torch.int64, torch.float32]
+
+    @pytest.fixture(autouse=True)
+    def _require_tf32_capable_cuda(self):
+        if not torch.cuda.is_available():
+            pytest.skip("CUDA not available")
+        # TF32 exists from Ampere on. Below that, allow_tf32=True changes
+        # nothing and the case would pass without covering the regression.
+        if torch.cuda.get_device_capability() < (8, 0):
+            pytest.skip("TF32-capable GPU (SM80+) required")
+
+    def _rotary(self) -> Qwen3VLTextRotaryEmbedding:
+        pretrained = SimpleNamespace(
+            rope_theta=1_000_000.0,
+            head_dim=self.HEAD_DIM,
+            max_position_embeddings=262_144,
+            rope_axes_dim=self.ROPE_AXES,
+            rope_scaling=None,
+        )
+        return Qwen3VLTextRotaryEmbedding(SimpleNamespace(pretrained_config=pretrained))
+
+    @staticmethod
+    def _reference_cos_sin(rotary: Qwen3VLTextRotaryEmbedding, position_ids: torch.Tensor):
+        """Same math as the module, in float64 and without any matmul."""
+        inv = rotary.inv_freq.double()[None, None, :, None]  # [1, 1, D/2, 1]
+        pos = position_ids.double()[:, :, None, :]  # [3, B, 1, N]
+        freqs = (inv * pos).transpose(2, 3)  # [3, B, N, D/2]
+        freqs = rotary.apply_interleaved_mrope(freqs.clone(), rotary.mrope_section)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        return emb.cos(), emb.sin()
+
+    @pytest.mark.parametrize("allow_tf32", [False, True])
+    @pytest.mark.parametrize("pos_dtype", POSITION_DTYPES, ids=str)
+    @pytest.mark.parametrize("seq_len", SEQUENCE_LENGTHS)
+    def test_rotary_table_matches_fp64_under_tf32(
+        self, allow_tf32: bool, pos_dtype: torch.dtype, seq_len: int
+    ):
+        saved = torch.backends.cuda.matmul.allow_tf32
+        torch.backends.cuda.matmul.allow_tf32 = allow_tf32
+        try:
+            rotary = self._rotary().to(DEVICE)
+            ramp = torch.arange(seq_len, dtype=pos_dtype, device=DEVICE)
+            position_ids = ramp.expand(3, -1)[:, None, :]
+            probe = torch.empty(0, dtype=torch.float32, device=DEVICE)
+
+            cos, sin = rotary(probe, position_ids)
+            ref_cos, ref_sin = self._reference_cos_sin(rotary, position_ids)
+
+            # The returned dtype follows the probe, not the position ids.
+            assert cos.dtype == probe.dtype
+            assert sin.dtype == probe.dtype
+
+            # fp32 evaluates angles of ~4e4 rad with ~6e-8 relative precision, so
+            # a few 1e-3 absolute is the honest fp32 floor; TF32 breakage is O(1).
+            tol = 5e-3
+            assert (cos.double() - ref_cos).abs().max().item() < tol
+            assert (sin.double() - ref_sin).abs().max().item() < tol
+        finally:
+            torch.backends.cuda.matmul.allow_tf32 = saved