diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml
index 4ee6b499b539..b739851cb905 100644
--- a/.buildkite/test-pipeline.yaml
+++ b/.buildkite/test-pipeline.yaml
@@ -424,6 +424,9 @@ steps:
   - vllm/model_executor/layers/quantization
   - tests/quantization
   commands:
+  # temporary install here since we need nightly, will move to requirements/test.in
+  # after torchao 0.12 release
+  - pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
   - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization
 
 - label: LM Eval Small Models # 53min
diff --git a/tests/quantization/test_torchao.py b/tests/quantization/test_torchao.py
index c966dc9b8152..54ec59585450 100644
--- a/tests/quantization/test_torchao.py
+++ b/tests/quantization/test_torchao.py
@@ -13,7 +13,7 @@ TORCHAO_AVAILABLE = importlib.util.find_spec("torchao") is not None
 
 @pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available")
 def test_pre_quantized_model(vllm_runner):
-    with vllm_runner("drisspg/float8_dynamic_act_float8_weight-opt-125m",
+    with vllm_runner("drisspg/fp8-opt-125m",
                      quantization="torchao",
                      dtype="bfloat16",
                      enforce_eager=True) as llm:
@@ -30,10 +30,10 @@ def test_pre_quantized_model(vllm_runner):
         "cuda:0",
         # {"": "cuda"},
     ])
-def test_opt_125m_int4wo_model_loading_with_params(vllm_runner,
+def test_opt_125m_int8wo_model_loading_with_params(vllm_runner,
                                                    pt_load_map_location):
     torch._dynamo.reset()
-    model_name = "jerryzh168/opt-125m-int4wo"
+    model_name = "jerryzh168/opt-125m-int8wo-partial-quant"
     with vllm_runner(model_name=model_name,
                      quantization="torchao",
                      dtype="bfloat16",
diff --git a/vllm/model_executor/layers/quantization/torchao.py b/vllm/model_executor/layers/quantization/torchao.py
index af362f7a7d2d..a7d9332032a2 100644
--- a/vllm/model_executor/layers/quantization/torchao.py
+++ b/vllm/model_executor/layers/quantization/torchao.py
@@ -6,6 +6,7 @@ import torch
 import torch.nn.functional as F
 from torch.nn.parameter import Parameter
 
+from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod)
 from vllm.model_executor.layers.quantization import QuantizationMethods
@@ -13,12 +14,28 @@ from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.utils import set_weight_attrs
 
+logger = init_logger(__name__)
+
 
 class TorchAOConfig(QuantizationConfig):
     """Config class for torchao."""
 
     def __init__(self, torchao_config) -> None:
         self.torchao_config = torchao_config
+        """
+        # TorchAO quantization relies on tensor subclasses. In order,
+        # to enable proper caching this needs standalone compile
+        if is_torch_equal_or_newer("2.8.0"):
+            os.environ["VLLM_TEST_STANDALONE_COMPILE"] = "1"
+            logger.info(
+                "Using TorchAO: Setting VLLM_TEST_STANDALONE_COMPILE=1")
+
+        # TODO: remove after the torch dependency is updated to 2.8
+        if is_torch_equal_or_newer(
+                "2.7.0") and not is_torch_equal_or_newer("2.8.0"):
+            os.environ["VLLM_DISABLE_COMPILE_CACHE"] = "1"
+            logger.info("Using TorchAO: Setting VLLM_DISABLE_COMPILE_CACHE=1")
+        """
 
     def __repr__(self) -> str:
         return f"TorchAOConfig({self.torchao_config})"
@@ -61,10 +78,10 @@ class TorchAOConfig(QuantizationConfig):
         if not isinstance(layer, LinearBase):
             return None
 
-        from torchao.quantization import AOPerModuleConfig
+        from torchao.quantization import ModuleFqnToConfig
 
         module_fqn = prefix
-        if isinstance(self.torchao_config, AOPerModuleConfig):
+        if isinstance(self.torchao_config, ModuleFqnToConfig):
             module_fqn_to_config = self.torchao_config.module_fqn_to_config
             c = module_fqn_to_config.get(
                 module_fqn) or module_fqn_to_config.get("_default", None)