diff --git a/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_talker.py b/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_talker.py
index 35d988895b3..4468bba86f9 100644
--- a/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_talker.py
+++ b/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_talker.py
@@ -1,4 +1,5 @@
 from collections.abc import Iterable
+from dataclasses import replace
 from functools import cached_property
 
 import torch
@@ -18,6 +19,11 @@
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.sample.sampler import Sampler
 
+from vllm_omni.quantization.component_config import (
+    PRE_QUANTIZED_METHODS,
+    ComponentQuantizationConfig,
+)
+
 
 class Qwen2_5OmniTalkerForConditionalGeneration(
     nn.Module,
@@ -41,6 +47,11 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config: Qwen2_5OmniTalkerConfig = vllm_config.model_config.hf_config
         quant_config = vllm_config.quant_config
+        if isinstance(quant_config, ComponentQuantizationConfig):
+            quant_config = quant_config.resolve("talker")
+        elif quant_config is not None and quant_config.get_name() not in PRE_QUANTIZED_METHODS:
+            quant_config = None
+            vllm_config = replace(vllm_config, quant_config=None)
         self.vllm_config = vllm_config
         self.prefix = prefix
         self.quant_config = quant_config
diff --git a/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py b/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
index 931049d8d44..f28d798b15a 100644
--- a/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
+++ b/vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
@@ -1,6 +1,7 @@
 """Thin Omni wrapper: reuse upstream Qwen2.5-Omni thinker (v0.14) with minimal overrides."""
 
 from collections.abc import Iterable, Mapping
+from dataclasses import replace
 from typing import Any
 
 import torch
@@ -65,7 +66,8 @@
 from vllm.sequence import IntermediateTensors
 
 from vllm_omni.quantization.component_config import (
-    resolve_encoder_quant_config,
+    PRE_QUANTIZED_METHODS,
+    ComponentQuantizationConfig,
 )
 
 try:
@@ -372,10 +374,26 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.quant_config = quant_config
 
         # Pre-quantized checkpoints (modelopt NVFP4/FP8/MXFP8) only quantize
-        # the Thinker LM. Vision encoder weights remain in BF16 with no FP8
-        # scale tensors; passing quant_config causes FP8 kernels to run on
-        # BF16 weights, producing garbage embeddings. Keep None for encoders.
-        visual_quant_config = resolve_encoder_quant_config(quant_config)
+        # the Thinker LM (language model). Vision and audio encoder weights
+        # remain in BF16 and have no corresponding scale tensors in the
+        # checkpoint. Dynamic quantization methods (e.g. --quantization fp8)
+        # should also only target the language model.
+        visual_prefix = maybe_prefix(prefix, "visual")
+        language_prefix = maybe_prefix(prefix, "language_model")
+        if isinstance(quant_config, ComponentQuantizationConfig):
+            visual_quant_config = quant_config.resolve(visual_prefix)
+        elif quant_config is not None:
+            if quant_config.get_name() in PRE_QUANTIZED_METHODS:
+                visual_quant_config = None
+            else:
+                quant_config = ComponentQuantizationConfig(
+                    component_configs={language_prefix: quant_config},
+                    default_config=None,
+                )
+                vllm_config = replace(vllm_config, quant_config=quant_config)
+                visual_quant_config = None
+        else:
+            visual_quant_config = None
 
         with self._mark_tower_model(vllm_config, "audio"):
             if multimodal_config.get_limit_per_prompt("audio"):