diff --git a/docker/Dockerfile.rock_base b/docker/Dockerfile.rock_base
index 40100a1bbf..57bf6ae6fe 100644
--- a/docker/Dockerfile.rock_base
+++ b/docker/Dockerfile.rock_base
@@ -8,7 +8,7 @@ ARG TRITON_VERSION=3.8.0+git669b31ac
 ARG TRITON_REPO="https://github.com/ROCm/triton.git"
 ARG FA_BRANCH="0e60e394"
 ARG FA_REPO="https://github.com/Dao-AILab/flash-attention.git"
-ARG AITER_BRANCH="v0.1.24.post1"
+ARG AITER_BRANCH="v0.1.25"
 ARG AITER_REPO="https://github.com/ROCm/aiter.git"
 ARG MORI_BRANCH="v1.2.3.post1"
 ARG MORI_REPO="https://github.com/ROCm/mori.git"
diff --git a/docker/Dockerfile.rocm_base b/docker/Dockerfile.rocm_base
index bd8099dd15..33dd0f6b10 100644
--- a/docker/Dockerfile.rocm_base
+++ b/docker/Dockerfile.rocm_base
@@ -9,7 +9,7 @@ ARG PYTORCH_AUDIO_BRANCH="v2.11.0"
 ARG PYTORCH_AUDIO_REPO="https://github.com/pytorch/audio.git"
 ARG FA_BRANCH="0e60e394"
 ARG FA_REPO="https://github.com/Dao-AILab/flash-attention.git"
-ARG AITER_BRANCH="v0.1.24.post1"
+ARG AITER_BRANCH="v0.1.25"
 ARG AITER_REPO="https://github.com/ROCm/aiter.git"
 ARG MORI_BRANCH="v1.2.3.post1"
 ARG MORI_REPO="https://github.com/ROCm/mori.git"
diff --git a/tests/models/test_dsv41_mono_numerics.py b/tests/models/test_dsv41_mono_numerics.py
index f7cd877caf..83308d46b0 100644
--- a/tests/models/test_dsv41_mono_numerics.py
+++ b/tests/models/test_dsv41_mono_numerics.py
@@ -468,7 +468,10 @@ def _check_layers(rank: int, tp: int, device) -> None:
         # relative errors about 3x what the kernels show (0.3-0.5%)
         assert _cos(o[1], res2) > 0.9999 and _rel(o[1], res2) < 0.01, tag
         assert _cos(normed, xf) > 0.999 and _rel(normed, xf) < 0.012, tag
-        assert _cos(o[0], same_in) > 0.9999 and _rel(o[0], same_in) < 0.015, tag
+        # TODO: back to cos > 0.9999, rel < 0.015 once
+        # https://github.com/ROCm/aiter/issues/6548 is fixed: aiter's fused-FP8
+        # stage-1 MoE kernels use another MXFP8 scale rule (1.53% at TP4)
+        assert _cos(o[0], same_in) > 0.9998 and _rel(o[0], same_in) < 0.02, tag
         # per token, where both FFN inputs pick the same experts
         same = (top6(xf) == top6(normed)).all(1)
         assert same.float().mean() >= 0.5, tag
@@ -479,7 +482,8 @@ def _check_layers(rank: int, tp: int, device) -> None:
         f = runner.ffn(mw, part, res, post, comb, attn_pre)
         torch.testing.assert_close(f[1], res2, rtol=2**-8, atol=1e-6)
         torch.testing.assert_close(normed, xf, rtol=2**-7, atol=1e-6)
-        assert _cos(f[0], out) > 0.9999 and _rel(f[0], out) < 0.015, tag
+        # TODO: back to 0.9999 / 0.015 with the one above (ROCm/aiter#6548)
+        assert _cos(f[0], out) > 0.9998 and _rel(f[0], out) < 0.02, tag
         torch.accelerator.synchronize()
 
         whole, ffn = runner._outs(M, res0), runner._outs(M, res)