diff --git a/tests/kernels/moe/test_gpt_oss_triton_kernels.py b/tests/kernels/moe/test_gpt_oss_triton_kernels.py
index 384f43db479b..4900949ad780 100644
--- a/tests/kernels/moe/test_gpt_oss_triton_kernels.py
+++ b/tests/kernels/moe/test_gpt_oss_triton_kernels.py
@@ -22,7 +22,7 @@
 from triton_kernels.tensor_details import layout
 from triton_kernels.testing import assert_close
 
-from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
+from vllm.model_executor.layers.fused_moe.config import mxfp4_w4a16_moe_quant_config
 from vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe import (
     triton_kernel_moe_forward,
 )
@@ -298,12 +298,18 @@ def test_equiv(num_token, a_dtype, w_dtype, tp, workspace_init):
         pc2,
     ) = init_compute_data(M, K, N, E, a_dtype, w_dtype, num_warps=8)
 
-    quant_config = FusedMoEQuantConfig.make(
-        w1_bias=w1_bias_tri,
-        w2_bias=w2_bias_tri,
-        w1_scale=pc1,
-        w2_scale=pc2,
-    )
+    if a_dtype == "bf16" and w_dtype == "mx4":
+        quant_config = mxfp4_w4a16_moe_quant_config(
+            w1_scale=pc1,
+            w2_scale=pc2,
+            w1_bias=w1_bias_tri,
+            w2_bias=w2_bias_tri,
+        )
+    else:
+        raise NotImplementedError(
+            f"Quantization configuration for activation={a_dtype} and weight={w_dtype} "
+            f"has not been implemented."
+        )
 
     out_triton_monolithic = triton_kernel_moe_forward(
         hidden_states=x_tri,
diff --git a/tests/models/quantization/test_gpt_oss.py b/tests/models/quantization/test_gpt_oss.py
new file mode 100644
index 000000000000..e70ccaf88b0e
--- /dev/null
+++ b/tests/models/quantization/test_gpt_oss.py
@@ -0,0 +1,110 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+End-to-end accuracy test for GPT-OSS model quantization.
+
+Config:
+    Task:   gsm8k_platinum
+    Filter: flexible-extract
+    n-shot: 5
+    Metric: exact_match
+
+Run: pytest tests/models/quantization/test_gpt_oss.py
+"""
+
+import importlib
+import importlib.metadata
+from dataclasses import dataclass
+
+import huggingface_hub
+import lm_eval
+import pytest
+from packaging import version
+
+MODEL_ACCURACIES = {
+    # Full quantization: attention linears and MoE linears
+    "amd/gpt-oss-20b-WFP8-AFP8-KVFP8": 0.89,
+    # MoE linears only quantization
+    "amd/gpt-oss-20b-MoE-Quant-W-MXFP4-A-FP8-KV-FP8": 0.89,
+    # MoE linears only quantization
+    # "amd/gpt-oss-20b-MoE-Quant-W-MXFP4-A-MXFP4-KV-FP8": 0.90,
+}
+
+QUARK_MXFP4_AVAILABLE = importlib.util.find_spec("quark") is not None and version.parse(
+    importlib.metadata.version("amd-quark")
+) >= version.parse("0.9.0")
+
+
+def has_huggingface_access(repo):
+    try:
+        huggingface_hub.list_repo_refs(repo)
+        return True
+    except huggingface_hub.errors.RepositoryNotFoundError:
+        return False
+
+
+HF_HUB_AMD_ORG_ACCESS = all(
+    [has_huggingface_access(model_name) for model_name in MODEL_ACCURACIES]
+)
+
+
+@dataclass
+class ModelCase:
+    model_id: str
+    tp: int
+
+
+@dataclass
+class EvaluationConfig:
+    model_name: str
+
+    def get_model_args(self, tp_size: int):
+        return {
+            "pretrained": self.model_name,
+            "chat_template_args": {"reasoning_effort": "low"},
+            "enable_thinking": True,
+            "think_end_token": "200008",
+            "tensor_parallel_size": tp_size,
+            "dtype": "auto",
+            "gpu_memory_utilization": 0.95,
+            "trust_remote_code": False,
+            "enable_prefix_caching": False,
+            "enforce_eager": False,
+        }
+
+
+@pytest.mark.skipif(not QUARK_MXFP4_AVAILABLE, reason="amd-quark>=0.9 is not available")
+@pytest.mark.skipif(
+    not HF_HUB_AMD_ORG_ACCESS,
+    reason="Read access to huggingface.co/amd is required for this test.",
+)
+@pytest.mark.parametrize("tp_size", [1, 2, 4, 8])
+@pytest.mark.parametrize("model_name, expected_accuracy", MODEL_ACCURACIES.items())
+def test_gpt_oss_attention_quantization(
+    model_name: str, tp_size: int, expected_accuracy: float
+):
+    model_args = EvaluationConfig(model_name).get_model_args(tp_size)
+
+    extra_run_kwargs = {
+        "gen_kwargs": {"max_gen_toks": 8000},
+        "apply_chat_template": True,
+        "fewshot_as_multiturn": True,
+        "num_fewshot": 5,
+    }
+
+    lm_eval_out = lm_eval.simple_evaluate(
+        model="vllm",
+        model_args=model_args,
+        tasks="gsm8k_platinum",
+        batch_size="auto",
+        **extra_run_kwargs,
+    )
+    measured_accuracy = float(
+        lm_eval_out["results"]["gsm8k_platinum"]["exact_match,flexible-extract"]
+    )
+
+    rtol = 0.02
+    assert (
+        measured_accuracy - rtol < expected_accuracy
+        and measured_accuracy + rtol > expected_accuracy
+    ), f"Expected: {expected_accuracy} |  Measured: {measured_accuracy}"
diff --git a/tests/models/quantization/test_gpt_oss_attn_quantization.py b/tests/models/quantization/test_gpt_oss_attn_quantization.py
deleted file mode 100644
index 780165ea2ba7..000000000000
--- a/tests/models/quantization/test_gpt_oss_attn_quantization.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Test attention quantization of gpt-oss model.
-The qkv_proj and o_proj in self_attention can be either quantized or excluded.
-
-Run `pytest tests/models/quantization/test_gpt_oss_attn_quantization.py`.
-
-"""
-
-import importlib
-import importlib.metadata
-from dataclasses import dataclass
-
-import huggingface_hub
-import lm_eval
-import pytest
-from packaging import version
-
-MODEL_NAMES = ["amd/gpt-oss-20b-customized-attention-quantization"]
-
-QUARK_MXFP4_AVAILABLE = importlib.util.find_spec("quark") is not None and version.parse(
-    importlib.metadata.version("amd-quark")
-) >= version.parse("0.8.99")
-
-
-def has_huggingface_access(repo):
-    try:
-        huggingface_hub.list_repo_refs(repo)
-        return True
-    except huggingface_hub.errors.RepositoryNotFoundError:
-        return False
-
-
-HF_HUB_AMD_ORG_ACCESS = all(
-    [has_huggingface_access(model_name) for model_name in MODEL_NAMES]
-)
-
-
-@dataclass
-class ModelCase:
-    model_id: str
-    tp: int
-
-
-@dataclass
-class EvaluationConfig:
-    model_name: str
-
-    def get_model_args(self) -> str:
-        return (
-            f"pretrained={self.model_name},"
-            "tensor_parallel_size=4,dtype=auto,gpu_memory_utilization=0.9,trust_remote_code=False"
-        )
-
-
-EXPECTED_ACCURACIES = {"arc_challenge": 0.20}
-
-
-@pytest.mark.skipif(not QUARK_MXFP4_AVAILABLE, reason="amd-quark>=0.9 is not available")
-@pytest.mark.skipif(
-    not HF_HUB_AMD_ORG_ACCESS,
-    reason="Read access to huggingface.co/amd is required for this test.",
-)
-@pytest.mark.parametrize("model_name", MODEL_NAMES)
-@pytest.mark.parametrize("task_name, expected_accuracy", EXPECTED_ACCURACIES.items())
-def test_gpt_oss_attention_quantization(
-    model_name: str, task_name: str, expected_accuracy: float
-):
-    measured_accuracy = lm_eval.simple_evaluate(
-        model="vllm",
-        model_args=EvaluationConfig(model_name).get_model_args(),
-        tasks=task_name,
-        batch_size="auto",
-    )["results"][task_name]["acc,none"]
-
-    rtol = 0.05
-    assert (
-        measured_accuracy - rtol < expected_accuracy
-        and measured_accuracy + rtol > expected_accuracy
-    ), f"Expected: {expected_accuracy} |  Measured: {measured_accuracy}"
diff --git a/vllm/model_executor/layers/fused_moe/config.py b/vllm/model_executor/layers/fused_moe/config.py
index 828e9d0f3640..b9fee1dd4a45 100644
--- a/vllm/model_executor/layers/fused_moe/config.py
+++ b/vllm/model_executor/layers/fused_moe/config.py
@@ -386,6 +386,10 @@ def use_mxfp4_w4a4(self) -> bool:
     def use_nvfp4_w4a4(self) -> bool:
         return self.quant_dtype == "nvfp4"
 
+    @property
+    def use_mxfp4_w4a8(self) -> bool:
+        return self._a1.dtype == "fp8" and self._w1.dtype == "mxfp4"
+
     def config_name(self, dtype: torch.dtype) -> str | None:
         """
         Return a string used to construct the filename that contains the
@@ -532,6 +536,8 @@ def fp8_w8a8_moe_quant_config(
     w2_scale: torch.Tensor,
     a1_scale: torch.Tensor | None = None,
     a2_scale: torch.Tensor | None = None,
+    w1_bias: torch.Tensor | None = None,
+    w2_bias: torch.Tensor | None = None,
     per_act_token_quant: bool = False,
     per_out_ch_quant: bool = False,
     block_shape: list[int] | None = None,
@@ -549,6 +555,8 @@ def fp8_w8a8_moe_quant_config(
         g1_alphas=g1_alphas,
         w2_scale=w2_scale,
         g2_alphas=g2_alphas,
+        w1_bias=w1_bias,
+        w2_bias=w2_bias,
         a1_scale=a1_scale,
         a1_gscale=a1_gscale,
         a2_scale=a2_scale,
@@ -564,6 +572,8 @@ def int8_w8a8_moe_quant_config(
     w2_scale: torch.Tensor,
     a1_scale: torch.Tensor | None,
     a2_scale: torch.Tensor | None,
+    w1_bias: torch.Tensor | None = None,
+    w2_bias: torch.Tensor | None = None,
     per_act_token_quant: bool = False,
 ) -> FusedMoEQuantConfig:
     """
@@ -575,6 +585,8 @@ def int8_w8a8_moe_quant_config(
         w2_scale=w2_scale,
         a1_scale=a1_scale,
         a2_scale=a2_scale,
+        w1_bias=w1_bias,
+        w2_bias=w2_bias,
         per_act_token_quant=per_act_token_quant,
         per_out_ch_quant=False,
         block_shape=None,
@@ -654,6 +666,26 @@ def mxfp4_mxfp8_moe_quant_config(
     )
 
 
+def mxfp4_w4a8_moe_quant_config(
+    w1_scale: Union[torch.Tensor, "PrecisionConfig"],
+    w2_scale: Union[torch.Tensor, "PrecisionConfig"],
+    a1_scale: torch.Tensor | None = None,
+    a2_scale: torch.Tensor | None = None,
+    w1_bias: torch.Tensor | None = None,
+    w2_bias: torch.Tensor | None = None,
+    block_shape: list[int] | None = None,
+) -> FusedMoEQuantConfig:
+    """
+    Construct a quant config for fp8 activations and mxfp4 weights.
+    """
+    return FusedMoEQuantConfig(
+        _a1=FusedMoEQuantDesc("fp8", None, a1_scale, None, None, None),
+        _a2=FusedMoEQuantDesc("fp8", None, a2_scale, None, None, None),
+        _w1=FusedMoEQuantDesc("mxfp4", None, w1_scale, None, None, w1_bias),
+        _w2=FusedMoEQuantDesc("mxfp4", None, w2_scale, None, None, w2_bias),
+    )
+
+
 def ocp_mx_moe_quant_config(
     quant_dtype: str,
     w1_scale: Union[torch.Tensor, "PrecisionConfig"],
@@ -691,6 +723,8 @@ def nvfp4_moe_quant_config(
     a2_gscale: torch.Tensor,
     w1_scale: torch.Tensor,
     w2_scale: torch.Tensor,
+    w1_bias: torch.Tensor | None = None,
+    w2_bias: torch.Tensor | None = None,
 ) -> FusedMoEQuantConfig:
     """
     Construct a quant config for mxfp4 activations and nvp4 weights.
@@ -699,6 +733,8 @@ def nvfp4_moe_quant_config(
         "nvfp4",
         w1_scale=w1_scale,
         w2_scale=w2_scale,
+        w1_bias=w1_bias,
+        w2_bias=w2_bias,
         a1_gscale=a1_gscale,
         a2_gscale=a2_gscale,
         g1_alphas=g1_alphas,
diff --git a/vllm/model_executor/layers/fused_moe/fused_moe.py b/vllm/model_executor/layers/fused_moe/fused_moe.py
index e0907368bbc7..63aae43c3ddf 100644
--- a/vllm/model_executor/layers/fused_moe/fused_moe.py
+++ b/vllm/model_executor/layers/fused_moe/fused_moe.py
@@ -38,7 +38,6 @@
 )
 from vllm.model_executor.layers.quantization.utils.mxfp4_utils import dequant_mxfp4
 from vllm.model_executor.layers.quantization.utils.mxfp6_utils import dequant_mxfp6
-from vllm.model_executor.layers.quantization.utils.ocp_mx_utils import OCP_MX_Scheme
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey,
     kFp8Dynamic128Sym,
@@ -1583,6 +1582,11 @@ def _get_config_quant_dtype(
         return "mxfp6_e3m2"
     elif ocp_mx_scheme in {"w_mxfp4_a_mxfp6_e2m3", "w_mxfp6_e2m3_a_mxfp6_e2m3"}:
         return "mxfp6_e2m3"
+    elif ocp_mx_scheme in {"w_mxfp4", "w_mxfp6_e3m2", "w_mxfp6_e2m3"}:
+        return torch.bfloat16
+    elif ocp_mx_scheme in {"w_mxfp4_a_fp8", "w_mxfp6_e3m2_a_fp8", "w_mxfp6_e2m3_a_fp8"}:
+        return torch.float8_e4m3fn
+
     return None
 
 
@@ -1617,17 +1621,10 @@ def fused_experts_impl(
     if use_int4_w4a16:
         assert hidden_states.size(1) // 2 == w1.size(2), "Hidden size mismatch"
     elif ocp_mx_scheme is not None:
-        if ocp_mx_scheme in {
-            "w_mxfp4_a_mxfp4",
-            "w_mxfp4_a_mxfp6_e3m2",
-            "w_mxfp4_a_mxfp6_e2m3",
-        }:
+        if ocp_mx_scheme.startswith("w_mxfp4"):
             # 16bit activation and fp4x2 packed weight
             assert hidden_states.size(1) == w1.size(2) * 2, "hidden size mismatch"
-        elif ocp_mx_scheme in {
-            "w_mxfp6_e3m2_a_mxfp6_e3m2",
-            "w_mxfp6_e2m3_a_mxfp6_e2m3",
-        }:
+        elif ocp_mx_scheme.startswith("w_mxfp6"):
             assert hidden_states.size(1) == (w1.size(2) * 4) // 3, (
                 "hidden size mismatch"
             )
@@ -1717,17 +1714,13 @@ def fused_experts_impl(
         # TODO: On platforms for which `current_platform.supports_mx()` is True
         # and for which we have a native OCP mx fused MOE kernel,
         # this dequantization step should not be done.
-        if ocp_mx_scheme in {
-            OCP_MX_Scheme.w_mxfp4_a_mxfp4,
-            OCP_MX_Scheme.w_mxfp4_a_mxfp6_e3m2,
-            OCP_MX_Scheme.w_mxfp4_a_mxfp6_e2m3,
-        }:
+        if ocp_mx_scheme.startswith("w_mxfp4"):
             # Weight has to be dequantized for mxfp4 emulation.
             w1 = dequant_mxfp4(w1, w1_scale, hidden_states.dtype)
             w1_scale = None
             w2 = dequant_mxfp4(w2, w2_scale, hidden_states.dtype)
             w2_scale = None
-        elif ocp_mx_scheme == OCP_MX_Scheme.w_mxfp6_e3m2_a_mxfp6_e3m2:
+        elif ocp_mx_scheme.startswith("w_mxfp6_e3m2"):
             w1 = dequant_mxfp6(
                 w1, w1_scale, quant_dtype="fp6_e3m2", float_dtype=hidden_states.dtype
             )
@@ -1736,7 +1729,7 @@ def fused_experts_impl(
                 w2, w2_scale, quant_dtype="fp6_e3m2", float_dtype=hidden_states.dtype
             )
             w2_scale = None
-        elif ocp_mx_scheme == OCP_MX_Scheme.w_mxfp6_e2m3_a_mxfp6_e2m3:
+        elif ocp_mx_scheme.startswith("w_mxfp6_e2m3"):
             w1 = dequant_mxfp6(
                 w1, w1_scale, quant_dtype="fp6_e2m3", float_dtype=hidden_states.dtype
             )
@@ -1779,6 +1772,7 @@ def fused_experts_impl(
             quant_dtype=quant_dtype,
             per_act_token_quant=per_channel_quant,
             block_shape=block_shape,
+            ocp_mx_scheme=ocp_mx_scheme,
         )
 
         # SPARSITY_FACTOR is a heuristic margin ensuring tokens_in_chunk * top_k
@@ -1846,6 +1840,7 @@ def fused_experts_impl(
             quant_dtype=quant_dtype,
             per_act_token_quant=per_channel_quant,
             block_shape=block_shape,
+            ocp_mx_scheme=ocp_mx_scheme,
         )
 
         if expert_map is not None:
diff --git a/vllm/model_executor/layers/fused_moe/layer.py b/vllm/model_executor/layers/fused_moe/layer.py
index c3be1be8537a..f35ec87aac42 100644
--- a/vllm/model_executor/layers/fused_moe/layer.py
+++ b/vllm/model_executor/layers/fused_moe/layer.py
@@ -221,12 +221,14 @@ def get_compressed_expert_map(expert_map: torch.Tensor) -> str:
     )
 
 
+# TODO(rob): move this down to the kernel.
 def maybe_roundup_hidden_size(
     hidden_size: int,
     act_dtype: torch.dtype,
-    quant_config: QuantizationConfig | None,
     moe_parallel_config: FusedMoEParallelConfig,
     is_lora_enabled: bool,
+    model_type: str | None,
+    is_mxfp4_quant: bool,
 ) -> int:
     """
     Given layer hidden size and MoE configurations, round up hidden_size
@@ -235,11 +237,12 @@ def maybe_roundup_hidden_size(
     Args:
         hidden_size: Layer hidden-size
         act_dtype: Data type of the layer activations.
-        quant_config: Fused MoE quantization configuration.
         moe_parallel_config: Fused MoE parallelization strategy configuration.
         is_lora_enabled: True if the engine is enabled with LoRA. This
             is used in the case of mxfp4 quantization in selecting the
             MxFP4Backend.
+        model_type: for checking if gpt-oss
+        is_mxfp4_quant: whether the layer is quantized with mxfp4
 
     Return:
         Rounded up hidden_size if rounding up is required based on the configs.
@@ -254,7 +257,7 @@ def maybe_roundup_hidden_size(
     )
 
     # we are padding globally so EP buffer allocation works
-    if quant_config and quant_config.get_name() == "mxfp4":
+    if model_type == "gpt_oss" and is_mxfp4_quant:
         from vllm.model_executor.layers.quantization.mxfp4 import (
             Mxfp4Backend,
             get_mxfp4_backend,
@@ -398,15 +401,6 @@ def __init__(
         # Expert mapping used in self.load_weights
         self.expert_mapping = expert_mapping
 
-        # Round up hidden size if needed.
-        hidden_size = maybe_roundup_hidden_size(
-            hidden_size,
-            moe_in_dtype,
-            quant_config,
-            self.moe_parallel_config,
-            is_lora_enabled=self.vllm_config.lora_config is not None,
-        )
-
         # For smuggling this layer into the fused moe custom op
         compilation_config = vllm_config.compilation_config
         if prefix in compilation_config.static_forward_context:
@@ -508,7 +502,6 @@ def __init__(
             ), "Aiter Fused MoE kernel only supports expert_map with 0 and 1s."
 
         assert intermediate_size % self.tp_size == 0
-        self.hidden_size = hidden_size
         self.intermediate_size_per_partition = intermediate_size // self.tp_size
         self.reduce_results = reduce_results
         self.renormalize = renormalize
@@ -548,6 +541,24 @@ def __init__(
         )
         self.routing_method_type: RoutingMethodType = self.router.routing_method_type
 
+        # Round up hidden size before creating moe_config.
+        # This way moe_config is created with the correct hidden_size from the start.
+        hidden_size = maybe_roundup_hidden_size(
+            hidden_size=hidden_size,
+            act_dtype=moe_in_dtype,
+            moe_parallel_config=self.moe_parallel_config,
+            is_lora_enabled=vllm_config.lora_config is not None,
+            model_type=(
+                self.vllm_config.model_config.hf_config.model_type
+                if self.vllm_config.model_config is not None
+                else None
+            ),
+            is_mxfp4_quant=(
+                quant_config is not None and quant_config.is_mxfp4_quant(prefix, self)
+            ),
+        )
+        self.hidden_size = hidden_size
+
         self.moe_config: FusedMoEConfig = FusedMoEConfig(
             num_experts=self.global_num_experts,
             experts_per_token=top_k,
diff --git a/vllm/model_executor/layers/fused_moe/utils.py b/vllm/model_executor/layers/fused_moe/utils.py
index 75873a92abdb..7d5ca876bdcc 100644
--- a/vllm/model_executor/layers/fused_moe/utils.py
+++ b/vllm/model_executor/layers/fused_moe/utils.py
@@ -23,6 +23,9 @@
 from vllm.model_executor.layers.quantization.utils.mxfp8_utils import (
     mxfp8_e4m3_quantize,
 )
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    per_tensor_dequantize,
+)
 from vllm.triton_utils import tl, triton
 from vllm.utils.math_utils import cdiv
 from vllm.utils.torch_utils import is_torch_equal_or_newer
@@ -241,7 +244,27 @@ def moe_kernel_quantize_input(
     per_act_token_quant: bool,
     block_shape: list[int] | None = None,
     is_fp4_scale_swizzled: bool = True,
+    ocp_mx_scheme: str | None = None,
 ) -> tuple[torch.Tensor, torch.Tensor | None]:
+    # Handle OCP MX scheme that requires QDQ (quantize-dequantize) for emulation
+    if ocp_mx_scheme is not None:
+        if ocp_mx_scheme in {"w_mxfp4", "w_mxfp4_a_mxfp4"}:
+            pass  # No QDQ needed for these schemes
+        elif ocp_mx_scheme.endswith("a_fp8"):
+            # Perform QDQ (quantize and dequantize) on activation for emulation
+            # purpose, because there is no native kernel for weight in ocp_mx_scheme
+            # and activation in FP8. The implementation is based on existing
+            # non-emulation ops.
+            qA, qA_scale = ops.scaled_fp8_quant(
+                A, A_scale, use_per_token_if_dynamic=False
+            )
+            A = per_tensor_dequantize(qA, qA_scale).to(A.dtype)
+            # After QDQ, we don't need further quantization
+            return A, None
+        # else: For other schemes (e.g., *_a_mxfp6_e3m2, *_a_mxfp6_e2m3),
+        # weights are already dequantized, and we proceed with normal
+        # activation quantization below.
+
     if quant_dtype == torch.float8_e4m3fn:
         return _fp8_quantize(A, A_scale, per_act_token_quant, block_shape)
     elif quant_dtype == torch.int8:
diff --git a/vllm/model_executor/layers/quantization/base_config.py b/vllm/model_executor/layers/quantization/base_config.py
index c8a8424eb5c8..a10264865073 100644
--- a/vllm/model_executor/layers/quantization/base_config.py
+++ b/vllm/model_executor/layers/quantization/base_config.py
@@ -168,3 +168,19 @@ def maybe_update_config(self, model_name: str):  # noqa: B027
         Interface to update values after config initialization.
         """
         pass
+
+    def is_mxfp4_quant(self, prefix: str, layer: torch.nn.Module) -> bool:
+        """
+        Determine if mxfp4 quantization will be used for this config.
+
+        This allows hidden_size rounding to happen before moe_config creation
+        without needing to instantiate quant_method first.
+
+        Args:
+            prefix: The layer prefix/name in the model
+            layer: The layer module
+
+        Returns:
+            True if this config uses MXFP4 quantization, False otherwise
+        """
+        return False
diff --git a/vllm/model_executor/layers/quantization/mxfp4.py b/vllm/model_executor/layers/quantization/mxfp4.py
index b9dec4530565..d1c9cb6bb2cf 100644
--- a/vllm/model_executor/layers/quantization/mxfp4.py
+++ b/vllm/model_executor/layers/quantization/mxfp4.py
@@ -229,10 +229,15 @@ def get_quant_method(
             )
         return None
 
+    def is_mxfp4_quant(self, prefix: str, layer: torch.nn.Module) -> bool:
+        """MXFP4 config always uses MXFP4 quantization."""
+        return True
+
 
 class Mxfp4MoEMethod(FusedMoEMethodBase):
     def __init__(self, moe: FusedMoEConfig):
         super().__init__(moe)
+        self.weight_dtype = "mxfp4"
         self.mxfp4_backend = get_mxfp4_backend(moe.is_lora_enabled)
 
         self.marlin_input_dtype = None
diff --git a/vllm/model_executor/layers/quantization/quark/quark.py b/vllm/model_executor/layers/quantization/quark/quark.py
index dd6db7193235..2e75a3de564b 100644
--- a/vllm/model_executor/layers/quantization/quark/quark.py
+++ b/vllm/model_executor/layers/quantization/quark/quark.py
@@ -320,38 +320,45 @@ def _is_static_tensor_w8a8(
         # Only symmetric weight quantization supported.
         return is_int8_dtype and is_tensor and is_weight_symmetric and is_static
 
-    def _is_ocp_mx(
-        self,
-        weight_quant: dict[str, Any] | None,
-        input_quant: dict[str, Any] | None,
+    def _is_w_ocp_mx_a_x(
+        self, weight_quant: dict[str, Any] | None, input_quant: dict[str, Any] | None
     ) -> bool:
-        # Confirm weights and input quantized.
-        if weight_quant is None or input_quant is None:
+        """
+        This check returns True only if it is an OCP-MX weight quantization.
+        The activation can be any data type (e.g., FP16/BF16, FP8, or OCP-MX format).
+        The rationale for checking only the weight type is that
+        the model loading concept and process primarily concerns the weights themselves.
+        """
+        # Confirm weights quantized.
+        if weight_quant is None:
             logger.debug(
-                "Quark model is not in OCP MX format: "
-                "weight_quant or input_quant not set"
+                "Quark model's weight quantization is incompatible with OCP_MX format: "
+                "weight_quant is not set."
             )
             return False
 
         # Input and weight qscheme needs to be per group.
-        if (
-            weight_quant.get("qscheme") != "per_group"
-            or input_quant.get("qscheme") != "per_group"
-        ):
-            logger.debug("Quark model is not in OCP MX format: not per_group")
+        if weight_quant.get("qscheme") != "per_group":
+            logger.debug(
+                "Quark model's weight quantization is incompatible with OCP MX format: "
+                "weight is not per_group."
+            )
             return False
 
         # Input and weight group size needs to be 32.
-        if weight_quant.get("group_size") != 32 or input_quant.get("group_size") != 32:
-            logger.debug("Quark model is not in OCP MX format: not group_size=32")
+        if weight_quant.get("group_size") != 32:
+            logger.debug(
+                "Quark model's weight quantization is incompatible with OCP MX format: "
+                "group_size of weight is not 32."
+            )
             return False
 
         # Activations and weight scales need to be in e8m0 format.
-        if (
-            weight_quant.get("scale_format") != "e8m0"
-            or input_quant.get("scale_format") != "e8m0"
-        ):
-            logger.debug("Quark model is not in OCP MX format: not scale_format e8m0")
+        if weight_quant.get("scale_format") != "e8m0":
+            logger.debug(
+                "Quark model's weight quantization is incompatible with OCP MX format: "
+                "scale_format of weight is not e8m0."
+            )
             return False
 
         # Input and weight dtypes need to be any of fp4,
@@ -360,14 +367,31 @@ def _is_ocp_mx(
             "fp4",
             "fp6_e3m2",
             "fp6_e2m3",
-        } or input_quant.get("dtype") not in {"fp4", "fp6_e3m2", "fp6_e2m3"}:
+        }:
             logger.debug(
-                "Quark model is not in OCP MX format: dtype not fp4, fp6_e3m2, fp6_e2m3"
+                "Quark model's weight quantization is incompatible with OCP MX format: "
+                "dtype is not in {fp4, fp6_e3m2, fp6_e2m3}."
             )
             return False
 
         return True
 
+    def is_mxfp4_quant(self, prefix: str, layer: torch.nn.Module) -> bool:
+        """
+        For Quark, determine if it's OCP MXFP4 by checking config directly.
+        This allows hidden_size rounding to happen before moe_config creation.
+        """
+        layer_quant_config = self._find_matched_config(prefix, layer)
+        weight_config = layer_quant_config.get("weight")
+        input_config = layer_quant_config.get("input_tensors")
+
+        return (
+            self._is_w_ocp_mx_a_x(weight_config, input_config)
+            and weight_config is not None
+            and weight_config.get("dtype") == "fp4"
+            and getattr(torch, "float4_e2m1fn_x2", None) is not None
+        )
+
     def _find_matched_config(
         self, layer_name: str, module: torch.nn.Module
     ) -> dict[str, Any]:
@@ -441,7 +465,7 @@ def _get_scheme_from_config(self, config: dict[str, Any]) -> "QuarkScheme":
                 is_static_input_scheme=True,
                 input_symmetric=input_config.get("symmetric"),
             )
-        elif self._is_ocp_mx(weight_config, input_config):
+        elif self._is_w_ocp_mx_a_x(weight_config, input_config):
             return QuarkOCP_MX(weight_config, input_config)
 
         raise NotImplementedError(
diff --git a/vllm/model_executor/layers/quantization/quark/quark_moe.py b/vllm/model_executor/layers/quantization/quark/quark_moe.py
index fc836c56be1d..190890130e33 100644
--- a/vllm/model_executor/layers/quantization/quark/quark_moe.py
+++ b/vllm/model_executor/layers/quantization/quark/quark_moe.py
@@ -8,6 +8,7 @@
 import vllm.envs as envs
 from vllm import _custom_ops as ops
 from vllm._aiter_ops import rocm_aiter_ops
+from vllm.config import get_current_vllm_config
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import (
     FusedMoE,
@@ -18,9 +19,15 @@
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEQuantConfig,
     fp8_w8a8_moe_quant_config,
+    mxfp4_w4a8_moe_quant_config,
+    mxfp4_w4a16_moe_quant_config,
     ocp_mx_moe_quant_config,
 )
 from vllm.model_executor.layers.fused_moe.fused_marlin_moe import fused_marlin_moe
+from vllm.model_executor.layers.quantization.mxfp4 import (
+    Mxfp4Backend,
+    get_mxfp4_backend,
+)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
     prepare_fp8_moe_layer_for_marlin,
 )
@@ -37,6 +44,7 @@
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
+from vllm.utils.math_utils import round_up
 
 logger = init_logger(__name__)
 
@@ -46,6 +54,7 @@
 class QuarkMoEMethod(FusedMoEMethodBase):
     def __init__(self, moe: FusedMoEConfig):
         super().__init__(moe)
+        self.has_bias = self.moe.has_bias
 
     @staticmethod
     def get_moe_method(
@@ -67,7 +76,7 @@ def get_moe_method(
             return QuarkW4A8Fp8MoEMethod(weight_config, input_config, module.moe_config)
         elif quant_config._is_fp8_w8a8(weight_config, input_config):
             return QuarkW8A8Fp8MoEMethod(weight_config, input_config, module.moe_config)
-        elif quant_config._is_ocp_mx(weight_config, input_config):
+        elif quant_config._is_w_ocp_mx_a_x(weight_config, input_config):
             return QuarkOCP_MX_MoEMethod(weight_config, input_config, module.moe_config)
         else:
             raise RuntimeError("Unsupported FusedMoe scheme")
@@ -86,6 +95,10 @@ def __init__(
 
         self.weight_qscheme = self.weight_quant.get("qscheme")
         self.input_qscheme = self.input_quant.get("qscheme")
+        self.weight_dtype = self.weight_quant.get("dtype", "").replace(
+            "fp8_e4m3", "fp8"
+        )
+        self.input_dtype = self.input_quant.get("dtype", "").replace("fp8_e4m3", "fp8")
         per_tensor = (
             self.weight_qscheme == "per_tensor" and self.input_qscheme == "per_tensor"
         )
@@ -121,6 +134,10 @@ def __init__(
 
         self.rocm_aiter_moe_enabled = rocm_aiter_ops.is_fused_moe_enabled()
 
+        self.model_type = getattr(
+            get_current_vllm_config().model_config.hf_config, "model_type", None
+        )
+
     def create_weights(
         self,
         layer: torch.nn.Module,
@@ -166,9 +183,16 @@ def create_weights(
         if self.weight_qscheme == "per_tensor":
             # Allocate 2 scales for w1 and w3 respectively.
             # They are combined to a single scale after weight loading.
-            w13_weight_scale = torch.nn.Parameter(
-                torch.ones(num_experts, 2, dtype=torch.float32), requires_grad=False
-            )
+            if self.model_type != "gpt_oss":
+                w13_weight_scale = torch.nn.Parameter(
+                    torch.ones(num_experts, 2, dtype=torch.float32), requires_grad=False
+                )
+            else:
+                # For gpt_oss, the w1(gate) & w3(up) are fused as one.
+                # Therefore, only one weight scale for each expert.
+                w13_weight_scale = torch.nn.Parameter(
+                    torch.ones(num_experts, 1, dtype=torch.float32), requires_grad=False
+                )
             layer.register_parameter("w13_weight_scale", w13_weight_scale)
             w2_weight_scale = torch.nn.Parameter(
                 torch.ones(num_experts, dtype=torch.float32), requires_grad=False
@@ -220,6 +244,27 @@ def create_weights(
             layer.w13_input_scale = None
             layer.w2_input_scale = None
 
+        if self.has_bias:
+            w13_bias = torch.nn.Parameter(
+                torch.zeros(
+                    num_experts,
+                    2 * intermediate_size_per_partition,
+                    dtype=torch.float32,
+                ),
+                requires_grad=False,
+            )
+            layer.register_parameter("w13_bias", w13_bias)
+            set_weight_attrs(w13_bias, extra_weight_attrs)
+
+            w2_bias = torch.nn.Parameter(
+                torch.zeros(num_experts, hidden_size, dtype=torch.float32),
+                requires_grad=False,
+            )
+            layer.register_parameter("w2_bias", w2_bias)
+            set_weight_attrs(w2_bias, extra_weight_attrs)
+        else:
+            layer.w13_bias, layer.w2_bias = None, None
+
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         # Fp8 moe kernels require a single activation scale.
         # We take the max of all the scales in case they differ.
@@ -278,21 +323,40 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
             assert layer.w13_weight_scale is not None
             shard_size = layer.intermediate_size_per_partition
             max_w13_scales = layer.w13_weight_scale.max(dim=1).values
-            for expert_id in range(layer.local_num_experts):
-                start = 0
-                for shard_id in range(2):
+
+            # For gpt_oss, w1 and w3 are fused into a single combined
+            # gate_up_proj tensor with size 2*intermediate_size_per_partition
+            # and only one scale per expert.
+            # Process the entire weight tensor as one shard.
+            if self.model_type == "gpt_oss":
+                for expert_id in range(layer.local_num_experts):
+                    # Process all 2*intermediate_size_per_partition rows at once
                     dq_weight = per_tensor_dequantize(
-                        layer.w13_weight[expert_id][start : start + shard_size, :],
-                        layer.w13_weight_scale[expert_id][shard_id],
+                        layer.w13_weight[expert_id],
+                        layer.w13_weight_scale[expert_id][0],
                     )
-                    layer.w13_weight[expert_id][start : start + shard_size, :], _ = (
-                        ops.scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
+                    layer.w13_weight[expert_id], _ = ops.scaled_fp8_quant(
+                        dq_weight, max_w13_scales[expert_id]
                     )
-                    start += shard_size
+            else:
+                # For non-gpt_oss, process w1 and w3 shards separately
+                for expert_id in range(layer.local_num_experts):
+                    start = 0
+                    for shard_id in range(2):
+                        dq_weight = per_tensor_dequantize(
+                            layer.w13_weight[expert_id][start : start + shard_size, :],
+                            layer.w13_weight_scale[expert_id][shard_id],
+                        )
+                        (
+                            layer.w13_weight[expert_id][start : start + shard_size, :],
+                            _,
+                        ) = ops.scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
+                        start += shard_size
 
             layer.w13_weight_scale = torch.nn.Parameter(
                 max_w13_scales, requires_grad=False
             )
+
         # quark's scale is 1 dim.
         elif self.weight_qscheme == "per_channel":
             if self.act_quant_group_shape == GroupShape.PER_TOKEN:
@@ -343,6 +407,8 @@ def get_fused_moe_quant_config(
             w2_scale=layer.w2_weight_scale,
             a1_scale=layer.w13_input_scale,
             a2_scale=layer.w2_input_scale,
+            w1_bias=layer.w13_bias,
+            w2_bias=layer.w2_bias,
             per_act_token_quant=self.input_qscheme == "per_channel",
             per_out_ch_quant=self.weight_qscheme == "per_channel",
         )
@@ -563,7 +629,7 @@ class QuarkOCP_MX_MoEMethod(QuarkMoEMethod):
     def __init__(
         self,
         weight_config: dict[str, Any],
-        input_config: dict[str, Any],
+        input_config: dict[str, Any] | None,
         moe: FusedMoEConfig,
     ):
         super().__init__(moe)
@@ -571,35 +637,79 @@ def __init__(
         self.input_quant = input_config
 
         weight_qscheme = self.weight_quant.get("qscheme")
-        input_qscheme = self.input_quant.get("qscheme")
-        if not (weight_qscheme == "per_group" and input_qscheme == "per_group"):
+        if not weight_qscheme == "per_group":
             raise ValueError(
                 "For MX(FP4) Fused MoE layers, only per-group scales "
-                "for weights and activations are supported. Found "
-                f"{weight_qscheme}, {input_qscheme}"
+                f"for weights are supported. Found {weight_qscheme}."
             )  # noqa E501
 
-        self.static_input_scales = not self.input_quant.get("is_dynamic")
-
         self.weight_dtype = self.weight_quant["dtype"].replace("fp", "mxfp")
-        self.input_dtype = self.input_quant["dtype"].replace("fp", "mxfp")
+        if self.input_quant is not None:
+            input_quant = self.input_quant["dtype"]
+            if input_quant in ["fp4", "fp6_e3m2", "fp6_e2m3"]:
+                self.input_dtype = input_quant.replace("fp", "mxfp")
+            elif input_quant == "fp8_e4m3":
+                self.input_dtype = input_quant.replace("fp8_e4m3", "fp8")
+            else:
+                raise NotImplementedError(
+                    f"Current input dtype {input_quant} is not compatible \
+                        with OCP MX (weight) MoE quantization. Please open an issue"
+                )
+        else:
+            self.input_dtype = None
+
         self.fp4_dtype = getattr(torch, "float4_e2m1fn_x2", None)
 
         self.ocp_mx_scheme = OCP_MX_Scheme.from_quant_dtype(
             self.input_dtype, self.weight_dtype
         )
 
-        if self.static_input_scales:
+        if self.ocp_mx_scheme is None:
+            raise ValueError(
+                f"Unsupported OCP MX dtype combination for MoE: "
+                f"input_dtype={self.input_dtype}, weight_dtype={self.weight_dtype}. "
+                f"Please check that the combination is supported in OCP_MX_Scheme."
+            )
+
+        self.mxfp4_backend: Mxfp4Backend | None = None
+        if self.ocp_mx_scheme == "w_mxfp4":
+            self.mxfp4_backend = get_mxfp4_backend(moe.is_lora_enabled)
+
+        if self.input_quant is not None:
+            self.static_input_scales = not self.input_quant.get("is_dynamic")
+        else:
+            self.static_input_scales = False
+
+        if any(
+            self.ocp_mx_scheme.endswith(a_scheme)
+            for a_scheme in ["a_mxfp4", "a_mxfp6_e3m2", "a_mxfp6_e2m3"]
+        ):
+            if self.static_input_scales:
+                raise NotImplementedError(
+                    "QuarkOCP_MX_MoEMethod with static input scales is currently "
+                    f"not implemented for OCP MX scheme {self.ocp_mx_scheme}. "
+                    "Please open an issue."
+                )
+        elif self.ocp_mx_scheme.endswith("a_fp8") and not self.static_input_scales:
             raise NotImplementedError(
-                "QuarkOCP_MX_MoEMethod with static input scales is currently "
-                "not implemented. Please open an issue."
+                "QuarkOCP_MX_MoEMethod with dynamic input scales is currently "
+                f"not implemented for OCP MX scheme {self.ocp_mx_scheme}. "
+                "Please open an issue."
             )
 
         self.use_rocm_aiter_moe = rocm_aiter_ops.is_fused_moe_enabled()
 
-        self.emulate = not current_platform.supports_mx() or not (
-            self.use_rocm_aiter_moe and self.ocp_mx_scheme == "w_mxfp4_a_mxfp4"
+        self.model_type = getattr(
+            get_current_vllm_config().model_config.hf_config, "model_type", None
         )
+
+        self._emulate = (
+            not current_platform.supports_mx()
+            or not self.ocp_mx_scheme.startswith("w_mxfp4")
+        ) and (self.mxfp4_backend is None or not self.use_rocm_aiter_moe)
+
+        self.emulate = True if self.model_type == "gpt_oss" else self._emulate
+
         if self.emulate:
             logger.warning_once(
                 f"The current mode (supports_mx={current_platform.supports_mx()}, "
@@ -640,12 +750,23 @@ def create_weights(
         )
 
         params_dtype = torch.uint8
+        if self.model_type == "gpt_oss":
+            if current_platform.is_rocm():
+                intermediate_size_per_partition_after_pad = round_up(
+                    intermediate_size_per_partition, 256
+                )
+            else:
+                intermediate_size_per_partition_after_pad = round_up(
+                    intermediate_size_per_partition, 64
+                )
+        else:
+            intermediate_size_per_partition_after_pad = intermediate_size_per_partition
 
         # WEIGHTS
         w13_weight = torch.nn.Parameter(
             torch.empty(
                 num_experts,
-                2 * intermediate_size_per_partition,
+                2 * intermediate_size_per_partition_after_pad,
                 self.get_packed_dim(hidden_size, self.weight_dtype),
                 dtype=params_dtype,
             ),
@@ -659,7 +780,9 @@ def create_weights(
             torch.empty(
                 num_experts,
                 hidden_size,
-                self.get_packed_dim(intermediate_size_per_partition, self.weight_dtype),
+                self.get_packed_dim(
+                    intermediate_size_per_partition_after_pad, self.weight_dtype
+                ),
                 dtype=params_dtype,
             ),
             requires_grad=False,
@@ -672,7 +795,7 @@ def create_weights(
         w13_weight_scale = torch.nn.Parameter(
             torch.ones(
                 num_experts,
-                2 * intermediate_size_per_partition,
+                2 * intermediate_size_per_partition_after_pad,
                 hidden_size // OCP_MX_BLOCK_SIZE,
                 dtype=params_dtype,
             ),
@@ -682,7 +805,7 @@ def create_weights(
             torch.ones(
                 num_experts,
                 hidden_size,
-                intermediate_size_per_partition // OCP_MX_BLOCK_SIZE,
+                intermediate_size_per_partition_after_pad // OCP_MX_BLOCK_SIZE,
                 dtype=params_dtype,
             ),
             requires_grad=False,
@@ -693,8 +816,96 @@ def create_weights(
         layer.register_parameter("w13_weight_scale", w13_weight_scale)
         layer.register_parameter("w2_weight_scale", w2_weight_scale)
 
+        if self.has_bias:
+            w13_bias = torch.nn.Parameter(
+                torch.zeros(
+                    num_experts,
+                    2 * intermediate_size_per_partition_after_pad,
+                    dtype=torch.float32,
+                ),
+                requires_grad=False,
+            )
+            layer.register_parameter("w13_bias", w13_bias)
+            set_weight_attrs(w13_bias, extra_weight_attrs)
+
+            w2_bias = torch.nn.Parameter(
+                torch.zeros(num_experts, hidden_size, dtype=torch.float32),
+                requires_grad=False,
+            )
+            layer.register_parameter("w2_bias", w2_bias)
+            set_weight_attrs(w2_bias, extra_weight_attrs)
+        else:
+            layer.w13_bias, layer.w2_bias = None, None
+
+        # INPUT_SCALES
+        if self.static_input_scales:
+            w13_input_scale = torch.nn.Parameter(
+                torch.ones(num_experts, dtype=torch.float32), requires_grad=False
+            )
+            layer.register_parameter("w13_input_scale", w13_input_scale)
+            set_weight_attrs(w13_input_scale, extra_weight_attrs)
+
+            w2_input_scale = torch.nn.Parameter(
+                torch.ones(num_experts, dtype=torch.float32), requires_grad=False
+            )
+            layer.register_parameter("w2_input_scale", w2_input_scale)
+            set_weight_attrs(w2_input_scale, extra_weight_attrs)
+        else:
+            layer.w13_input_scale = None
+            layer.w2_input_scale = None
+
     def process_weights_after_loading(self, layer):
+        if self.static_input_scales:
+            # firstly, process activations if fp8 static input
+            if layer.w13_input_scale is None or layer.w2_input_scale is None:
+                raise ValueError(
+                    "QuantConfig has static quantization, but found "
+                    "activation scales are None."
+                )
+            if not all_close_1d(layer.w13_input_scale) or not all_close_1d(
+                layer.w2_input_scale
+            ):
+                logger.warning_once(
+                    "Found input_scales that are not equal for "
+                    "fp8 MoE layer. Using the maximum across experts "
+                    "for each layer. "
+                )
+            layer.w13_input_scale = torch.nn.Parameter(
+                layer.w13_input_scale.max(), requires_grad=False
+            )
+            layer.w2_input_scale = torch.nn.Parameter(
+                layer.w2_input_scale.max(), requires_grad=False
+            )
+
+            if current_platform.is_fp8_fnuz():
+                # Normalize the weights and scales
+                _, _, w13_input_scale = normalize_e4m3fn_to_e4m3fnuz(
+                    torch.empty_like(layer.w13_weight, dtype=torch.float8_e4m3fnuz),
+                    torch.empty_like(
+                        layer.w13_weight_scale, dtype=layer.w13_weight_scale.dtype
+                    ),
+                    layer.w13_input_scale,
+                )
+                _, _, w2_input_scale = normalize_e4m3fn_to_e4m3fnuz(
+                    torch.empty_like(layer.w2_weight, dtype=torch.float8_e4m3fnuz),
+                    torch.empty_like(
+                        layer.w2_weight_scale, dtype=layer.w13_weight_scale.dtype
+                    ),
+                    layer.w2_input_scale,
+                )
+                # Reset the parameter
+                if w13_input_scale is not None:
+                    layer.w13_input_scale = torch.nn.Parameter(
+                        w13_input_scale, requires_grad=False
+                    )
+                if w2_input_scale is not None:
+                    layer.w2_input_scale = torch.nn.Parameter(
+                        w2_input_scale, requires_grad=False
+                    )
+
+        # secondly, process mxfp weights
         if self.emulate:
+            torch.cuda.empty_cache()
             return
 
         from aiter.utility.fp4_utils import e8m0_shuffle
@@ -725,15 +936,40 @@ def process_weights_after_loading(self, layer):
     def get_fused_moe_quant_config(
         self, layer: torch.nn.Module
     ) -> FusedMoEQuantConfig | None:
-        return ocp_mx_moe_quant_config(
-            quant_dtype=self.input_dtype,
-            weight_dtype=self.weight_dtype,
-            w1_scale=layer.w13_weight_scale,
-            w2_scale=layer.w2_weight_scale,
-            a1_scale=None,
-            a2_scale=None,
-            block_shape=None,
-        )
+        if self.ocp_mx_scheme == "w_mxfp4":
+            return mxfp4_w4a16_moe_quant_config(
+                w1_scale=layer.w13_weight_scale,
+                w2_scale=layer.w2_weight_scale,
+                w1_bias=layer.w13_bias,
+                w2_bias=layer.w2_bias,
+            )
+        elif self.ocp_mx_scheme == "w_mxfp4_a_fp8":
+            return mxfp4_w4a8_moe_quant_config(
+                w1_scale=layer.w13_weight_scale,
+                w2_scale=layer.w2_weight_scale,
+                a1_scale=layer.w13_input_scale,
+                a2_scale=layer.w2_input_scale,
+                w1_bias=layer.w13_bias,
+                w2_bias=layer.w2_bias,
+                block_shape=None,
+            )
+        elif self.ocp_mx_scheme in ["w_mxfp6_e3m2_a_fp8", "w_mxfp6_e2m3_a_fp8"]:
+            raise NotImplementedError(
+                "Currently there is no corresponding fused moe quant config configured "
+                f"in vLLM for OCP MX scheme {self.ocp_mx_scheme}. Please open an issue."
+            )
+        else:
+            return ocp_mx_moe_quant_config(
+                quant_dtype=self.input_dtype,
+                weight_dtype=self.weight_dtype,
+                w1_scale=layer.w13_weight_scale,
+                w2_scale=layer.w2_weight_scale,
+                w1_bias=layer.w13_bias,
+                w2_bias=layer.w2_bias,
+                a1_scale=None,
+                a2_scale=None,
+                block_shape=None,
+            )
 
     def apply(
         self,
@@ -743,24 +979,34 @@ def apply(
         topk_ids: torch.Tensor,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         if not self.emulate:
-            from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
-                rocm_aiter_fused_experts,
-            )
+            if (
+                self.model_type == "gpt_oss"
+                and self.mxfp4_backend == Mxfp4Backend.TRITON
+            ):
+                raise NotImplementedError(
+                    "Triton kernel implemented fused MoE for GPT_OSS model "
+                    "in Quark(MoE) format is not integrated or provided yet."
+                )
 
-            out = rocm_aiter_fused_experts(
-                x,
-                layer.w13_weight,
-                layer.w2_weight,
-                topk_weights=topk_weights,
-                topk_ids=topk_ids,
-                activation=layer.activation,
-                quant_config=self.moe_quant_config,
-                expert_map=layer.expert_map,
-            )
+            else:
+                from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+                    rocm_aiter_fused_experts,
+                )
+
+                return rocm_aiter_fused_experts(
+                    x,
+                    layer.w13_weight,
+                    layer.w2_weight,
+                    topk_weights=topk_weights,
+                    topk_ids=topk_ids,
+                    activation=layer.activation,
+                    quant_config=self.moe_quant_config,
+                    expert_map=layer.expert_map,
+                )
         else:
             from vllm.model_executor.layers.fused_moe import fused_experts
 
-            out = fused_experts(
+            return fused_experts(
                 x,
                 layer.w13_weight,
                 layer.w2_weight,
@@ -773,5 +1019,3 @@ def apply(
                 expert_map=layer.expert_map,
                 quant_config=self.moe_quant_config,
             )
-
-        return out
diff --git a/vllm/model_executor/layers/quantization/utils/ocp_mx_utils.py b/vllm/model_executor/layers/quantization/utils/ocp_mx_utils.py
index 7752324f41fe..a9157cbfb08b 100644
--- a/vllm/model_executor/layers/quantization/utils/ocp_mx_utils.py
+++ b/vllm/model_executor/layers/quantization/utils/ocp_mx_utils.py
@@ -20,26 +20,44 @@
 
 
 class OCP_MX_Scheme(str, Enum):
+    w_mxfp4 = "w_mxfp4"
     w_mxfp4_a_mxfp4 = "w_mxfp4_a_mxfp4"
     w_mxfp4_a_mxfp6_e3m2 = "w_mxfp4_a_mxfp6_e3m2"
     w_mxfp4_a_mxfp6_e2m3 = "w_mxfp4_a_mxfp6_e2m3"
+    w_mxfp4_a_fp8 = "w_mxfp4_a_fp8"
+    w_mxfp6_e3m2 = "w_mxfp6_e3m2"
     w_mxfp6_e3m2_a_mxfp6_e3m2 = "w_mxfp6_e3m2_a_mxfp6_e3m2"
+    w_mxfp6_e3m2_a_fp8 = "w_mxfp6_e3m2_a_fp8"
+    w_mxfp6_e2m3 = "w_mxfp6_e2m3"
     w_mxfp6_e2m3_a_mxfp6_e2m3 = "w_mxfp6_e2m3_a_mxfp6_e2m3"
+    w_mxfp6_e2m3_a_fp8 = "w_mxfp6_e2m3_a_fp8"
 
     @classmethod
     def from_quant_dtype(cls, input_dtype: str | None, weight_dtype: str | None):
-        if input_dtype not in OCP_MX_DTYPES or weight_dtype not in OCP_MX_DTYPES:
+        if input_dtype not in OCP_MX_DTYPES and weight_dtype not in OCP_MX_DTYPES:
             return None
+        elif input_dtype is None and weight_dtype == "mxfp4":
+            return cls.w_mxfp4
+        elif input_dtype is None and weight_dtype == "mxfp6_e3m2":
+            return cls.w_mxfp6_e3m2
+        elif input_dtype is None and weight_dtype == "mxfp6_e2m3":
+            return cls.w_mxfp6_e2m3
         elif input_dtype == "mxfp4" and weight_dtype == "mxfp4":
             return cls.w_mxfp4_a_mxfp4
         elif input_dtype == "mxfp6_e3m2" and weight_dtype == "mxfp4":
             return cls.w_mxfp4_a_mxfp6_e3m2
         elif input_dtype == "mxfp6_e2m3" and weight_dtype == "mxfp4":
             return cls.w_mxfp4_a_mxfp6_e2m3
+        elif input_dtype == "fp8" and weight_dtype == "mxfp4":
+            return cls.w_mxfp4_a_fp8
         elif input_dtype == "mxfp6_e3m2" and weight_dtype == "mxfp6_e3m2":
             return cls.w_mxfp6_e3m2_a_mxfp6_e3m2
+        elif input_dtype == "fp8" and weight_dtype == "mxfp6_e3m2":
+            return cls.w_mxfp6_e3m2_a_fp8
         elif input_dtype == "mxfp6_e2m3" and weight_dtype == "mxfp6_e2m3":
             return cls.w_mxfp6_e2m3_a_mxfp6_e2m3
+        elif input_dtype == "fp8" and weight_dtype == "mxfp6_e2m3":
+            return cls.w_mxfp6_e2m3_a_fp8
         else:
             logger.warning(
                 "input_dtype='%s' and"
diff --git a/vllm/model_executor/models/gpt_oss.py b/vllm/model_executor/models/gpt_oss.py
index f62771c3697a..28c37c64b8ab 100644
--- a/vllm/model_executor/models/gpt_oss.py
+++ b/vllm/model_executor/models/gpt_oss.py
@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from collections.abc import Iterable
+import typing
+from collections.abc import Callable, Iterable
 
 import torch
 import torch.distributed as dist
@@ -25,13 +26,17 @@
 from vllm.model_executor.layers.linear import QKVParallelLinear, RowParallelLinear
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.quantization.utils.ocp_mx_utils import OCP_MX_BLOCK_SIZE
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.utils import rocm_unquantized_gemm
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,
 )
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
 from vllm.model_executor.models.utils import sequence_parallel_chunk
 from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
@@ -98,6 +103,7 @@ def __init__(
             head_size=self.head_dim,
             total_num_heads=self.num_attention_heads,
             total_num_kv_heads=self.num_key_value_heads,
+            bias=True,
             quant_config=quant_config,
             prefix=f"{prefix}.qkv_proj",
         )
@@ -105,6 +111,7 @@ def __init__(
         self.o_proj = RowParallelLinear(
             input_size=self.num_attention_heads * self.head_dim,
             output_size=self.hidden_size,
+            bias=True,
             quant_config=quant_config,
             prefix=f"{prefix}.o_proj",
         )
@@ -306,6 +313,19 @@ def forward(
             return x, aux_hidden_states
         return x
 
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, weight scales, activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        # NOTE: this is only used for quark.
+        return FusedMoE.make_expert_params_mapping(
+            self,
+            ckpt_gate_proj_name="w1",
+            ckpt_down_proj_name="w2",
+            ckpt_up_proj_name="w3",
+            num_experts=self.config.num_local_experts,
+            num_redundant_experts=0,
+        )
+
     def _load_weights_mxfp4(
         self,
         ep_rank_end: int,
@@ -318,7 +338,6 @@ def _load_weights_mxfp4(
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
 
-        mxfp4_block = 32
         use_ep = self.parallel_config.enable_expert_parallel
         num_experts = self.config.num_local_experts
 
@@ -333,9 +352,11 @@ def _load_weights_mxfp4(
         )
 
         intermediate_size = self.config.intermediate_size
-        intermediate_size_block = intermediate_size // mxfp4_block
+        intermediate_size_block = intermediate_size // OCP_MX_BLOCK_SIZE
         per_rank_intermediate_size_block = cdiv(intermediate_size_block, tp_size)
-        per_rank_intermediate_size = per_rank_intermediate_size_block * mxfp4_block
+        per_rank_intermediate_size = (
+            per_rank_intermediate_size_block * OCP_MX_BLOCK_SIZE
+        )
 
         # Calculate common slicing bounds for current rank
         tp_rank_start = tp_rank * per_rank_intermediate_size
@@ -370,7 +391,9 @@ def _load_weights_mxfp4(
                     narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
                 else:
                     narrow_weight = weight[
-                        ..., tp_rank_start // mxfp4_block : tp_rank_end // mxfp4_block
+                        ...,
+                        tp_rank_start // OCP_MX_BLOCK_SIZE : tp_rank_end
+                        // OCP_MX_BLOCK_SIZE,
                     ]
 
                 param = params_dict[name]
@@ -495,6 +518,449 @@ def _load_weights_mxfp4(
             loaded_params.add(name)
         return loaded_params
 
+    def _load_weights_quark(
+        self,
+        ep_rank_end: int,
+        ep_rank_start: int,
+        heads_per_rank: int,
+        head_start: int,
+        weights: Iterable[tuple[str, torch.Tensor]],
+        stacked_params_mapping: list[tuple[str, ...]],
+    ) -> set[str]:
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        use_ep = self.parallel_config.enable_expert_parallel
+        num_experts = self.config.num_local_experts
+
+        if use_ep:
+            tp_rank = get_tensor_model_parallel_rank()
+            tp_size = get_tensor_model_parallel_world_size()
+        else:
+            tp_size, tp_rank = FusedMoEParallelConfig.flatten_tp_across_dp_and_pcp(
+                tp_size=get_tensor_model_parallel_world_size(),
+                dp_size=get_dp_group().world_size,
+                dp_rank=get_dp_group().rank_in_group,
+                pcp_size=get_pcp_group().world_size,
+                pcp_rank=get_pcp_group().rank_in_group,
+            )
+
+        def _get_moe_weight_dtype(layer_id: int = 0) -> str | None:
+            """Helper function to get MoE quantization weight dtype.
+
+            Args:
+                layer_id: Layer index to check (default 0, as all layers should
+                        have the same quantization method)
+
+            Returns:
+                Weight dtype string (e.g., "mxfp4", "fp8") or None if not available
+            """
+            if hasattr(self.layers[layer_id].mlp.experts.quant_method, "weight_dtype"):
+                return self.layers[layer_id].mlp.experts.quant_method.weight_dtype
+            return None
+
+        intermediate_size = self.config.intermediate_size
+
+        moe_weight_dtype = _get_moe_weight_dtype(layer_id=0)
+
+        if moe_weight_dtype == "mxfp4":
+            # MXFP4 requires OCP_MX_BLOCK_SIZE alignment
+            intermediate_size_block = intermediate_size // OCP_MX_BLOCK_SIZE
+            per_rank_intermediate_size_block = cdiv(intermediate_size_block, tp_size)
+            per_rank_intermediate_size = (
+                per_rank_intermediate_size_block * OCP_MX_BLOCK_SIZE
+            )
+        else:
+            # FP8 and other formats don't need alignment
+            per_rank_intermediate_size = cdiv(intermediate_size, tp_size)
+
+        tp_rank_start = tp_rank * per_rank_intermediate_size
+        tp_rank_end = min((tp_rank + 1) * per_rank_intermediate_size, intermediate_size)
+        expert_params_mapping = self.get_expert_mapping()
+        for name, loaded_weight in weights:
+            if is_pp_missing_parameter(name, self):
+                continue
+
+            layer_id, expert_id, fused_name = None, None, None
+            moe_quant_method = None
+            if "experts" in name:
+                parts = name.split(".")
+                ids = [s for s in parts if s.isdigit()]
+
+                # for amd-quark format that each expert is seperated
+                # need to extract the parameter name with experts fused.
+                # example model: amd/gpt-oss-20b-MoE-Quant-W-MXFP4-A-FP8-KV-FP8
+                if len(ids) == 2:
+                    layer_id, expert_id = int(ids[0]), int(ids[-1])
+                    parts.pop(len(parts) - 1 - parts[::-1].index(str(expert_id)))
+                    fused_name = ".".join(parts)
+
+                # for openai mxfp4 format that all experts are combined
+                # no need to extract the parameter name with experts fused.
+                # models: openai/gpt-oss-20b, openai/gpt-oss-120b
+                elif len(ids) == 1:
+                    layer_id, expert_id = int(ids[0]), None
+                    fused_name = name
+
+                else:
+                    raise NameError(
+                        f"Layer {name} contains more than 2 numeric indices. This is "
+                        "an unexpected condition. Please open an issue if encountered."
+                    )
+
+                moe_quant_method = _get_moe_weight_dtype(layer_id=layer_id)
+
+            def kv_cache_scale_loader(
+                quant_config: QuantizationConfig,
+                name: str,
+                params_dict: dict[str, typing.Any],
+                weight: torch.Tensor,
+                default_weight_loader: Callable[..., None],
+                loaded_params: set[str],
+            ) -> tuple[bool, set[str]]:
+                """
+                Load KV cache output scales.
+                Returns:
+                    Tuple of (bool, set):
+                    - bool: True if KV-cache scale was loaded into loaded_params
+                    - set: Updated set of loaded_params if True else the original set
+                """
+                # load explicit cached KV output scale from quant_config
+                if quant_config is not None and (
+                    scale_name := quant_config.get_cache_scale(name)
+                ):
+                    param = params_dict[scale_name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    if weight.numel() != 1:
+                        raise ValueError(
+                            f"KV cache scale '{scale_name}' is expected to be a "
+                            f"scalar, but got a tensor of shape {weight.shape}."
+                        )
+                    # Ensure weight is a scalar before passing to loader.
+                    weight_loader(param, weight.flatten()[0])
+                    loaded_params.add(scale_name)
+                    return True, loaded_params
+
+                return False, loaded_params
+
+            load_kv_cache_scale_completed, loaded_params = kv_cache_scale_loader(
+                self.quant_config,
+                name,
+                params_dict,
+                loaded_weight,
+                default_weight_loader,
+                loaded_params,
+            )
+            if load_kv_cache_scale_completed:
+                continue
+
+            if (
+                all(key in name for key in ["input_scale", "mlp.experts"])
+                and expert_id is not None
+            ):
+                assert loaded_weight.numel() == 1
+                expert_data = params_dict[fused_name].data[expert_id]
+                expert_data.copy_(loaded_weight)
+                loaded_params.add(fused_name)
+                continue
+
+            # Unified handler for mxfp4 weights and scales
+            elif moe_quant_method == "mxfp4" and any(
+                name.endswith(suffix)
+                for suffix in [
+                    ".w13_weight_scale",
+                    ".w2_weight_scale",
+                    ".w13_weight",
+                    ".w2_weight",
+                ]
+            ):
+                is_w13 = ".w13_" in name
+                is_scale = "_scale" in name
+
+                # Reshape weight for mxfp4 if needed (not for scales)
+                if not is_scale and expert_id is None:
+                    if is_w13:
+                        if loaded_weight.dim() < 3:
+                            raise ValueError(
+                                f"Expected w13_weight to have at least 3 "
+                                f"dimensions, got shape "
+                                f"{loaded_weight.shape}"
+                            )
+                        if loaded_weight.shape[0] != num_experts:
+                            raise ValueError(
+                                f"Expected w13_weight first dimension to be "
+                                f"{num_experts}, got "
+                                f"{loaded_weight.shape[0]}"
+                            )
+                        loaded_weight = loaded_weight.view(
+                            num_experts, 2 * intermediate_size, -1
+                        ).contiguous()
+                    else:
+                        if loaded_weight.dim() < 3:
+                            raise ValueError(
+                                f"Expected w2_weight to have at least 3 "
+                                f"dimensions, got shape "
+                                f"{loaded_weight.shape}"
+                            )
+                        if loaded_weight.shape[0] != num_experts:
+                            raise ValueError(
+                                f"Expected w2_weight first dimension to be "
+                                f"{num_experts}, got "
+                                f"{loaded_weight.shape[0]}"
+                            )
+                        loaded_weight = loaded_weight.view(
+                            num_experts, -1, intermediate_size // 2
+                        ).contiguous()
+
+                if use_ep:
+                    sliced_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    if is_w13:
+                        if expert_id is None:
+                            sliced_weight = loaded_weight[
+                                :, 2 * tp_rank_start : 2 * tp_rank_end, ...
+                            ]
+                        else:
+                            sliced_weight = loaded_weight[
+                                2 * tp_rank_start : 2 * tp_rank_end, ...
+                            ]
+                    else:
+                        if is_scale:
+                            sliced_weight = loaded_weight[
+                                ...,
+                                tp_rank_start // OCP_MX_BLOCK_SIZE : tp_rank_end
+                                // OCP_MX_BLOCK_SIZE,
+                            ]
+                        else:
+                            sliced_weight = loaded_weight[
+                                ..., tp_rank_start // 2 : tp_rank_end // 2
+                            ]
+
+                # NOTE(rob): because gpt-oss ckpt has "unique" structure with
+                # fused gate_up_proj fused on disk, we cannot use the existing
+                # weight loaders without added complexity, so just do the
+                # direct load here.
+                param = params_dict[fused_name]
+                expert_data = param.data[expert_id]
+                dim1 = sliced_weight.shape[0]
+                dim2 = sliced_weight.shape[1]
+                expert_data.data[:dim1, :dim2].copy_(sliced_weight)
+                loaded_params.add(fused_name)
+                continue
+
+            elif name.endswith(".w13_weight") and moe_quant_method == "fp8":
+                if use_ep:
+                    narrow_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    if expert_id is None:
+                        narrow_weight = loaded_weight[
+                            :, 2 * tp_rank_start : 2 * tp_rank_end, :
+                        ]
+                    else:
+                        narrow_weight = loaded_weight[
+                            2 * tp_rank_start : 2 * tp_rank_end, :
+                        ]
+
+                assert fused_name is not None
+                param = params_dict[fused_name]
+
+                if expert_id is None:
+                    param.data.copy_(narrow_weight)
+                else:
+                    param.data[expert_id].copy_(narrow_weight)
+
+                loaded_params.add(fused_name)
+                continue
+
+            elif name.endswith(".w13_weight_scale") and moe_quant_method == "fp8":
+                assert fused_name is not None
+                param = params_dict[fused_name]
+
+                # Check if this is per-channel or per-tensor scale
+                if loaded_weight.numel() > 1 and loaded_weight.dim() == 1:
+                    if use_ep:
+                        narrow_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
+                    else:
+                        narrow_weight = loaded_weight[
+                            2 * tp_rank_start : 2 * tp_rank_end
+                        ]
+                else:
+                    narrow_weight = loaded_weight
+
+                if expert_id is None:
+                    param.data.copy_(narrow_weight)
+                else:
+                    param.data[expert_id].copy_(narrow_weight)
+
+                loaded_params.add(fused_name)
+                continue
+
+            elif name.endswith(".w13_input_scale") and moe_quant_method == "fp8":
+                assert fused_name is not None
+                param = params_dict[fused_name]
+
+                if expert_id is None:
+                    param.data.copy_(loaded_weight)
+                else:
+                    param.data[expert_id].copy_(loaded_weight)
+
+                loaded_params.add(fused_name)
+                continue
+
+            elif name.endswith(".w2_weight") and moe_quant_method == "fp8":
+                if use_ep:
+                    narrow_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    if expert_id is None:
+                        narrow_weight = loaded_weight[..., tp_rank_start:tp_rank_end]
+                    else:
+                        narrow_weight = loaded_weight[..., tp_rank_start:tp_rank_end]
+
+                assert fused_name is not None
+                param = params_dict[fused_name]
+
+                if expert_id is None:
+                    param.data.copy_(narrow_weight)
+                else:
+                    param.data[expert_id].copy_(narrow_weight)
+
+                loaded_params.add(fused_name)
+                continue
+
+            elif name.endswith(".w2_weight_scale") and moe_quant_method == "fp8":
+                assert fused_name is not None
+                param = params_dict[fused_name]
+
+                if use_ep:
+                    narrow_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    narrow_weight = loaded_weight
+
+                if expert_id is None:
+                    param.data.copy_(narrow_weight)
+                else:
+                    param.data[expert_id].copy_(narrow_weight)
+
+                loaded_params.add(fused_name)
+                continue
+
+            # Unified handler for bias loading (w13_bias and w2_bias)
+            elif name.endswith(".w13_bias") or name.endswith(".w2_bias"):
+                is_w13_bias = name.endswith(".w13_bias")
+
+                if use_ep:
+                    sliced_weight = loaded_weight[ep_rank_start:ep_rank_end, ...]
+                else:
+                    if is_w13_bias:
+                        if expert_id is None:
+                            sliced_weight = loaded_weight[
+                                :, 2 * tp_rank_start : 2 * tp_rank_end
+                            ]
+                        else:
+                            sliced_weight = loaded_weight[
+                                2 * tp_rank_start : 2 * tp_rank_end
+                            ]
+                    else:
+                        sliced_weight = loaded_weight
+                        if tp_rank != 0:
+                            sliced_weight = sliced_weight.zero_()
+
+                # NOTE(rob): because gpt-oss ckpt has "unique" structure with
+                # fused gate_up_proj fused on disk, we cannot use the existing
+                # weight loaders without added complexity, so just do the
+                # direct load here.
+                assert fused_name is not None
+                param = params_dict[fused_name]
+                expert_data = param.data[expert_id]
+                dim1 = sliced_weight.shape[0]
+                expert_data.data[:dim1].copy_(sliced_weight)
+                loaded_params.add(fused_name)
+                continue
+
+            elif "sinks" in name:
+                # Handle attention sinks (distributed across ranks)
+                param = params_dict[name]
+                narrow_weight = loaded_weight.narrow(0, head_start, heads_per_rank)
+                param.data.copy_(narrow_weight)
+                loaded_params.add(name)
+                continue
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                # Skip non-stacked layers and experts (experts handled below).
+                if weight_name not in name:
+                    continue
+                # We have mlp.experts[0].gate_proj in the checkpoint.
+                # Since we handle the experts below in expert_params_mapping,
+                # we need to skip here BEFORE we update the name, otherwise
+                # name will be updated to mlp.experts[0].gate_up_proj, which
+                # will then be updated below in expert_params_mapping
+                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+                if ("mlp.experts." in name) and name not in params_dict:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                if name.endswith("scale"):
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+
+                weight_loader(param, loaded_weight, shard_id)
+                loaded_params.add(name)
+                break
+            else:
+                for mapping in expert_params_mapping:
+                    # Anyway, this is an expert weight and should not be
+                    # attempted to load as other weights later
+                    param_name, weight_name, mapping_expert_id, shard_id = mapping
+                    weight_name = (
+                        weight_name[:-1] if weight_name.endswith(".") else weight_name
+                    )
+
+                    if weight_name not in name:
+                        continue
+
+                    param = params_dict[fused_name]
+                    # We should ask the weight loader to return success or not
+                    # here since otherwise we may skip experts with other
+                    # available replicas.
+                    weight_loader = typing.cast(
+                        Callable[..., bool], param.weight_loader
+                    )
+                    # Use checkpoint's expert_id for quark format (when expert_id
+                    # is extracted from weight name), otherwise use mapping's expert_id
+                    actual_expert_id = (
+                        expert_id if expert_id is not None else mapping_expert_id
+                    )
+                    success = weight_loader(
+                        param,
+                        loaded_weight,
+                        fused_name,
+                        shard_id=shard_id,
+                        expert_id=actual_expert_id,
+                        return_success=True,
+                    )
+                    if success:
+                        name = fused_name
+                        loaded_params.add(name)
+                        break
+                else:
+                    if name not in params_dict:
+                        continue
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+
+                loaded_params.add(name)
+        return loaded_params
+
     def _load_weights_other(
         self,
         ep_rank_end: int,
@@ -635,6 +1101,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             if hasattr(self.config, "quantization_config")
             else None
         )
+
         if quant_method == "mxfp4":
             return self._load_weights_mxfp4(
                 ep_rank_end,
@@ -644,6 +1111,15 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
                 weights,
                 stacked_params_mapping,
             )
+        elif quant_method == "quark":
+            return self._load_weights_quark(
+                ep_rank_end,
+                ep_rank_start,
+                heads_per_rank,
+                head_start,
+                weights,
+                stacked_params_mapping,
+            )
         else:
             return self._load_weights_other(
                 ep_rank_end,
@@ -676,6 +1152,15 @@ class GptOssForCausalLM(nn.Module, SupportsPP, SupportsEagle3, SupportsLoRA):
             # MoE Bias
             ".gate_up_proj_bias": ".w13_bias",
             ".down_proj_bias": ".w2_bias",
+            # For quark format
+            ".gate_up_proj.weight": ".w13_weight",
+            ".gate_up_proj.weight_scale": ".w13_weight_scale",
+            ".gate_up_proj.bias": ".w13_bias",
+            ".gate_up_proj.input_scale": ".w13_input_scale",
+            ".down_proj.weight": ".w2_weight",
+            ".down_proj.weight_scale": ".w2_weight_scale",
+            ".down_proj.bias": ".w2_bias",
+            ".down_proj.input_scale": ".w2_input_scale",
         },
     )
 
@@ -725,18 +1210,6 @@ def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
         logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
-    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
-        # Params for weights, weight scales, activation scales
-        # (param_name, weight_name, expert_id, shard_id)
-        return FusedMoE.make_expert_params_mapping(
-            self,
-            ckpt_gate_proj_name="gate_proj",
-            ckpt_down_proj_name="down_proj",
-            ckpt_up_proj_name="up_proj",
-            num_experts=self.config.num_local_experts,
-            num_redundant_experts=0,
-        )
-
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(
             self,