diff --git a/.buildkite/intel_jobs/quantization_intel.yaml b/.buildkite/intel_jobs/quantization_intel.yaml
index a51c6ebfc715..7f9fd884d35f 100644
--- a/.buildkite/intel_jobs/quantization_intel.yaml
+++ b/.buildkite/intel_jobs/quantization_intel.yaml
@@ -26,7 +26,6 @@ steps:
     bash .buildkite/scripts/hardware_ci/run-intel-test.sh
     'VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s
     tests/quantization/test_per_token_kv_cache.py
-    --deselect="tests/quantization/test_per_token_kv_cache.py::test_triton_unified_attention_per_token_head_scale[int4-16-128-num_heads0-seq_lens1]"
     && VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s tests/quantization/test_auto_awq.py
     && VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s tests/quantization/test_fp8_per_channel.py
     && VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s
diff --git a/tests/quantization/test_per_token_kv_cache.py b/tests/quantization/test_per_token_kv_cache.py
index 2647e8b5a8e2..46bb9a29b22a 100644
--- a/tests/quantization/test_per_token_kv_cache.py
+++ b/tests/quantization/test_per_token_kv_cache.py
@@ -578,9 +578,10 @@ def test_triton_unified_attention_per_token_head_scale(
     value_cache_bf16 = torch.randn_like(key_cache_bf16)
 
     if is_int4:
-        # Asymmetric quantization reference (matches the Triton kernel).
-        kf = key_cache_bf16.float()
-        vf = value_cache_bf16.float()
+        # The INT4 cache lives in the RHT domain: rotate K/V before the
+        # asymmetric quantization, like reshape_and_cache_int4 does.
+        kf = single_rht(key_cache_bf16.float()).to(torch.bfloat16).float()
+        vf = single_rht(value_cache_bf16.float()).to(torch.bfloat16).float()
         k_min = kf.amin(dim=-1)
         k_max = kf.amax(dim=-1)
         v_min = vf.amin(dim=-1)
@@ -597,11 +598,22 @@ def test_triton_unified_attention_per_token_head_scale(
             (vf / v_scale_cache[..., None] + v_zp[..., None]).round().clamp(0, 15)
         )
 
-        # Dequantized reference: x_hat = (q - zp) * scale
-        key_cache_deq = (key_cache_q_full - k_zp[..., None]) * k_scale_cache[..., None]
-        value_cache_deq = (value_cache_q_full - v_zp[..., None]) * v_scale_cache[
-            ..., None
-        ]
+        # Dequantized reference: x_hat = (q - zp) * scale, then IRHT/d back
+        # to the original domain the reference attention runs in.
+        key_cache_deq = (
+            single_rht(
+                (key_cache_q_full - k_zp[..., None]) * k_scale_cache[..., None],
+                inverse=True,
+            )
+            / head_size
+        )
+        value_cache_deq = (
+            single_rht(
+                (value_cache_q_full - v_zp[..., None]) * v_scale_cache[..., None],
+                inverse=True,
+            )
+            / head_size
+        )
 
         # Pack two uint4 values into one byte
         def _pack_int4(data_float):
@@ -725,12 +737,4 @@ def _pack_int4(data_float):
         softmax_segm_expsum=softmax_segm_expsum,
     )
 
-    # Coarser quantization → wider tolerance.
-    if is_int4:
-        # Hopper's attention reduction order can move a few BF16 elements by
-        # just over 1.0 after INT4 quantization.
-        atol = 1.1 if current_platform.is_device_capability_family(90) else 0.5
-        rtol = 0.5
-    else:
-        atol, rtol = 5e-2, 5e-2
-    torch.testing.assert_close(output_q, output_ref, atol=atol, rtol=rtol)
+    torch.testing.assert_close(output_q, output_ref, atol=5e-2, rtol=5e-2)