diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py
index ee431c9148b8..da69bdbbd09b 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py
@@ -12,7 +12,6 @@
 )
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     W8A8BlockFp8LinearOp,
-    check_aiter_fp8_linear_support,
     create_fp8_input_scale,
     create_fp8_scale_parameter,
     create_fp8_weight_parameter,
@@ -20,6 +19,7 @@
     process_fp8_weight_block_strategy,
     process_fp8_weight_channel_strategy,
     process_fp8_weight_tensor_strategy,
+    use_aiter_fp8_linear,
     validate_fp8_block_shape,
 )
 from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
@@ -61,7 +61,7 @@ def __init__(self, weight_quant: QuantizationArgs, is_static_input_scheme: bool)
             )
 
         self.cutlass_block_fp8_supported = cutlass_block_fp8_supported()
-        self.use_aiter_and_is_supported = check_aiter_fp8_linear_support()
+        self.use_aiter_and_is_supported = use_aiter_fp8_linear()
 
         if self.weight_block_size is not None:
             assert not self.is_static_input_scheme
diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py
index e5681cb85625..c59da2963663 100644
--- a/vllm/model_executor/layers/quantization/fp8.py
+++ b/vllm/model_executor/layers/quantization/fp8.py
@@ -56,7 +56,6 @@
 )
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     W8A8BlockFp8LinearOp,
-    check_aiter_fp8_linear_support,
     create_fp8_input_scale,
     create_fp8_scale_parameter,
     create_fp8_weight_parameter,
@@ -65,6 +64,7 @@
     process_fp8_weight_block_strategy,
     process_fp8_weight_tensor_strategy,
     requant_weight_ue8m0_inplace,
+    use_aiter_fp8_linear,
     validate_fp8_block_shape,
 )
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
@@ -362,7 +362,7 @@ def __init__(self, quant_config: Fp8Config):
         if vllm_is_batch_invariant():
             self.use_marlin = False
 
-        self.use_aiter_and_is_supported = check_aiter_fp8_linear_support()
+        self.use_aiter_and_is_supported = use_aiter_fp8_linear()
 
         self.weight_block_size = self.quant_config.weight_block_size
         self.block_quant = self.weight_block_size is not None
diff --git a/vllm/model_executor/layers/quantization/utils/fp8_utils.py b/vllm/model_executor/layers/quantization/utils/fp8_utils.py
index f25148abb619..a2b0d1368c17 100644
--- a/vllm/model_executor/layers/quantization/utils/fp8_utils.py
+++ b/vllm/model_executor/layers/quantization/utils/fp8_utils.py
@@ -104,12 +104,38 @@ def rocm_aiter_gemm_w8a8_blockscale_fake(
     if (
         envs.VLLM_ROCM_USE_AITER
         and envs.VLLM_ROCM_USE_AITER_LINEAR
-        and current_platform.is_fp8_fnuz()
+        and current_platform.supports_aiter_w8a8_block_fp8_linear()
     ):
         import aiter as rocm_aiter
-        from aiter import get_hip_quant
+        from aiter import per_group_quant_hip
+
+        def rocm_aiter_per1x128_quant_impl(
+            x: torch.Tensor,
+            scale: torch.Tensor | None = None,
+            quant_dtype: torch.dtype = torch.float8_e4m3fn,
+        ) -> tuple[torch.Tensor, torch.Tensor]:
+            return per_group_quant_hip(x, scale, quant_dtype, group_size=128)
+
+        def rocm_aiter_per1x128_quant_fake(
+            x: torch.Tensor,
+            scale: torch.Tensor | None = None,
+            quant_dtype: torch.dtype = torch.float8_e4m3fn,
+        ) -> tuple[torch.Tensor, torch.Tensor]:
+            group_size = 128
+            y = torch.empty(x.shape, dtype=quant_dtype, device=x.device)
+            scale = torch.empty(
+                (*x.shape[:-1], x.shape[-1] // group_size),
+                dtype=torch.float32,
+                device=x.device,
+            )
+            return y, scale
 
-        aiter_per1x128_quant = get_hip_quant(rocm_aiter.QuantType.per_1x128)
+        direct_register_custom_op(
+            op_name="rocm_aiter_per1x128_quant",
+            op_func=rocm_aiter_per1x128_quant_impl,
+            mutates_args=[],
+            fake_impl=rocm_aiter_per1x128_quant_fake,
+        )
 
 
 # TODO we should be able to change the type of block_size to GroupShape
@@ -352,7 +378,7 @@ def _run_aiter(
         weight_scale: torch.Tensor,
     ) -> torch.Tensor:
         assert self.act_quant_group_shape == GroupShape(1, 128)
-        q_input, input_scale = aiter_per1x128_quant(
+        q_input, input_scale = torch.ops.vllm.rocm_aiter_per1x128_quant(
             input_2d.contiguous(), quant_dtype=rocm_aiter.dtypes.fp8
         )
         return torch.ops.vllm.rocm_aiter_gemm_w8a8_blockscale(
@@ -938,14 +964,13 @@ def requant_weight_ue8m0_inplace(
         s_old.copy_(s_requant)
 
 
-def check_aiter_fp8_linear_support() -> bool:
-    """AITER is only supported on ROCm and only for FP8_FNUZ
-    and at the moment are MI300 series"""
+def use_aiter_fp8_linear() -> bool:
+    """Check whether the ROCm AITER FP8 linear op is supported and activated"""
     return (
         current_platform.is_rocm()
         and envs.VLLM_ROCM_USE_AITER
         and envs.VLLM_ROCM_USE_AITER_LINEAR
-        and current_platform.is_fp8_fnuz()
+        and current_platform.supports_aiter_w8a8_block_fp8_linear()
     )
 
 
diff --git a/vllm/platforms/rocm.py b/vllm/platforms/rocm.py
index d3535c9781c4..f857957643bc 100644
--- a/vllm/platforms/rocm.py
+++ b/vllm/platforms/rocm.py
@@ -434,19 +434,22 @@ def get_device_communicator_cls(cls) -> str:
         )
 
     @classmethod
-    def supports_mx(cls) -> bool:
+    def arch_is_in(cls, arch_list: list[str]) -> bool:
+        # only device 0 is checked, this assumes MI300 platforms are homogeneous
         gcn_arch = torch.cuda.get_device_properties(0).gcnArchName
-        return any(gfx in gcn_arch for gfx in ["gfx95"])
+        return any(gfx in gcn_arch for gfx in arch_list)
+
+    @classmethod
+    def supports_mx(cls) -> bool:
+        return cls.arch_is_in(["gfx95"])
 
     @classmethod
     def supports_fp8(cls) -> bool:
-        gcn_arch = torch.cuda.get_device_properties(0).gcnArchName
-        return any(gfx in gcn_arch for gfx in ["gfx94", "gfx95", "gfx12"])
+        return cls.arch_is_in(["gfx94", "gfx95", "gfx12"])
 
     @classmethod
     def is_fp8_fnuz(cls) -> bool:
-        # only device 0 is checked, this assumes MI300 platforms are homogeneous
-        return "gfx94" in torch.cuda.get_device_properties(0).gcnArchName
+        return cls.arch_is_in(["gfx94"])
 
     @classmethod
     def fp8_dtype(cls) -> torch.dtype:
@@ -457,10 +460,13 @@ def fp8_dtype(cls) -> torch.dtype:
 
     @classmethod
     def use_custom_allreduce(cls) -> bool:
-        # We only enable custom allreduce for MI300 series
-        gcn_arch = torch.cuda.get_device_properties(0).gcnArchName
-        supported_archs = ["gfx94", "gfx95"]
-        return any(gfx in gcn_arch for gfx in supported_archs)
+        # Enable for MI300 and MI350 series
+        return cls.arch_is_in(["gfx94", "gfx95"])
+
+    @classmethod
+    def supports_aiter_w8a8_block_fp8_linear(cls) -> bool:
+        # Enable for MI300 and MI350 series
+        return cls.arch_is_in(["gfx94", "gfx95"])
 
     @classmethod
     def opaque_attention_op(cls) -> bool: