diff --git a/tests/quantization/test_quark.py b/tests/quantization/test_quark.py
index ce918a324887..ae09ac58e675 100644
--- a/tests/quantization/test_quark.py
+++ b/tests/quantization/test_quark.py
@@ -5,6 +5,7 @@
 """
 
 import pytest
+import torch
 
 from vllm.model_executor.layers.quantization.quark.quark import (  # noqa: E501
     QuarkLinearMethod, QuarkW8A8Fp8, QuarkW8A8Int8)
@@ -63,3 +64,28 @@ def check_model(model):
 
         output = llm.generate_greedy("Hello my name is", max_tokens=20)
         assert output
+
+
+def test_quark_fp8_parity(vllm_runner):
+    quark_model_id = "amd-quark/llama-tiny-fp8-quark-quant-method"
+    fp8_model_id = "amd-quark/llama-tiny-fp8-quant-method"
+
+    llm_kwargs = {
+        "tensor_parallel_size": 1,
+        "enforce_eager": True,
+        "gpu_memory_utilization": 0.1
+    }
+    with (vllm_runner(quark_model_id, **llm_kwargs) as
+          quark_handle, vllm_runner(fp8_model_id, **llm_kwargs) as fp8_handle):
+        quark_model = (quark_handle.model.llm_engine.model_executor.
+                       driver_worker.model_runner.model)
+        quark_state_dict = quark_model.state_dict()
+
+        fp8_model = (fp8_handle.model.llm_engine.model_executor.driver_worker.
+                     model_runner.model)
+        fp8_state_dict = fp8_model.state_dict()
+
+    assert fp8_state_dict.keys() == quark_state_dict.keys()
+
+    for key in fp8_state_dict:
+        assert torch.equal(fp8_state_dict[key], quark_state_dict[key])
diff --git a/vllm/model_executor/layers/quantization/quark/schemes/quark_w8a8_fp8.py b/vllm/model_executor/layers/quantization/quark/schemes/quark_w8a8_fp8.py
index afd4bb722dad..f8eb3611592e 100644
--- a/vllm/model_executor/layers/quantization/quark/schemes/quark_w8a8_fp8.py
+++ b/vllm/model_executor/layers/quantization/quark/schemes/quark_w8a8_fp8.py
@@ -34,21 +34,24 @@ def process_weights_after_loading(self, layer) -> None:
         # tensor scales (thus N scales being passed to the kernel),
         # requantize so we can always run per tensor
         if self.qscheme == "per_tensor":
-            max_w_scale, weight = requantize_with_max_scale(
-                weight=layer.weight,
-                weight_scale=layer.weight_scale,
-                logical_widths=layer.logical_widths,
-            )
-
-            if current_platform.is_fp8_fnuz():
+            if current_platform.is_rocm():
                 input_scale = getattr(layer, 'input_scale', None)
                 weight, max_w_scale, input_scale = normalize_e4m3fn_to_e4m3fnuz(
-                    weight=weight,
-                    weight_scale=max_w_scale,
+                    weight=layer.weight,
+                    weight_scale=layer.weight_scale,
                     input_scale=input_scale)
                 if input_scale is not None:
                     layer.input_scale = Parameter(input_scale,
                                                   requires_grad=False)
+            else:
+                max_w_scale = layer.weight_scale
+                weight = layer.weight
+
+            max_w_scale, weight = requantize_with_max_scale(
+                weight=weight,
+                weight_scale=max_w_scale,
+                logical_widths=layer.logical_widths,
+            )
 
             layer.weight = Parameter(weight.t(), requires_grad=False)
             layer.weight_scale = Parameter(max_w_scale, requires_grad=False)