diff --git a/test/integration/test_integration.py b/test/integration/test_integration.py
index 4461355fb3..74eab6cad6 100644
--- a/test/integration/test_integration.py
+++ b/test/integration/test_integration.py
@@ -20,11 +20,10 @@
     DynamicallyPerAxisQuantizedLinear,
 )
 from torchao.quantization.quant_api import (
-    apply_dynamic_quant,
-    apply_weight_only_int8_quant,
-    change_linear_weights_to_int8_dqtensors,
-    change_linear_weights_to_int8_woqtensors,
-    change_linear_weights_to_int4_woqtensors,
+    int4wo,
+    int8wo,
+    int8da_int8w,
+    quantize,
     _replace_with_custom_fn_if_matches_filter,
 )
 from torchao.quantization.quant_primitives import (
@@ -73,7 +72,11 @@
 from parameterized import parameterized
 import itertools
 import logging
-from torchao.utils import TORCH_VERSION_AFTER_2_3, TORCH_VERSION_AFTER_2_4
+from torchao.utils import (
+    TORCH_VERSION_AFTER_2_3,
+    TORCH_VERSION_AFTER_2_4,
+    unwrap_tensor_subclass,
+)
 
 logger = logging.getLogger("INFO")
 
@@ -82,9 +85,9 @@
 
 # TODO: use this to reduce the number of tests
 TENSOR_SUBCLASS_APIS = [
-    change_linear_weights_to_int8_dqtensors,
-    change_linear_weights_to_int8_woqtensors,
-    change_linear_weights_to_int4_woqtensors,
+    int4wo,
+    int8wo,
+    int8da_int8w,
 ]
 
 COMMON_DEVICES = ["cpu", "cuda"]
@@ -736,7 +739,8 @@ def _test_lin_weight_subclass_api_impl(
             nn.Linear(k, n, device=test_device), nn.ReLU(), nn.Linear(n, n, device=test_device)
         ).to(test_dtype)
         ref_f = mod(x)
-        api(mod)
+        quantize(mod, api())
+        unwrap_tensor_subclass(mod)
 
         test = mod(x)
         self.assertGreater(
@@ -756,13 +760,13 @@ def _test_lin_weight_subclass_api_impl(
     @unittest.skipIf(TORCH_VERSION_AFTER_2_4, "skip because there is some bug in inductor codegen")
     def test_int8_dynamic_quant_subclass_api(self, device, dtype):
         self._test_lin_weight_subclass_api_impl(
-            change_linear_weights_to_int8_dqtensors, device, 35, test_dtype=dtype
+            int8da_int8w, device, 35, test_dtype=dtype
         )
 
     @parameterized.expand(COMMON_DEVICE_DTYPE)
     def test_int8_weight_only_quant_subclass_api(self, device, dtype):
         self._test_lin_weight_subclass_api_impl(
-            change_linear_weights_to_int8_woqtensors, device, 40, test_dtype=dtype
+            int8wo, device, 40, test_dtype=dtype
         )
 
     @parameterized.expand(COMMON_DEVICE_DTYPE)
@@ -772,7 +776,7 @@ def test_int4_weight_only_quant_subclass_api(self, device, dtype):
             self.skipTest(f"Fails for {dtype}")
         for test_shape in ([(16, 1024, 16)] + ([(1, 1024, 256)] if device=='cuda' else [])):
             self._test_lin_weight_subclass_api_impl(
-                change_linear_weights_to_int4_woqtensors,
+                int4wo,
                 device,
                 15,
                 test_shape=test_shape,
@@ -789,7 +793,7 @@ def test_int4_weight_only_quant_subclass_api_grouped(self, device, dtype):
                 for inner_k_tiles in [4, 2]:
                     kwargs = {"groupsize": groupsize, "inner_k_tiles": inner_k_tiles}
                     self._test_lin_weight_subclass_api_impl(
-                        lambda mod: change_linear_weights_to_int4_woqtensors(mod, **kwargs),
+                        lambda: int4wo(**kwargs),
                         device,
                         15,
                         test_shape=test_shape,
@@ -804,7 +808,7 @@ def test_dynamic_quant(self):
         m = nn.Sequential(nn.Linear(K, N))
 
         y_ref = m(x)
-        apply_dynamic_quant(m)
+        quantize(m, int8da_int8w())
         y_test = m(x)
 
         sqnr = compute_error(y_ref, y_test)
@@ -818,7 +822,7 @@ def test_weight_only_quant(self):
             x = torch.randn(*x_shape)
             m = nn.Sequential(nn.Linear(4, 5))
             y_ref = m(x)
-            apply_weight_only_int8_quant(m)
+            quantize(m, int8wo())
             y_wo = m(x)
             sqnr = compute_error(y_ref, y_wo)
             self.assertGreater(sqnr, 44.0)
@@ -841,7 +845,8 @@ def test_weight_only_quant_force_mixed_mm(self, device, dtype):
                 x = torch.randn(*x_shape).to(device).to(dtype)
                 m = nn.Sequential(nn.Linear(4, 5)).to(device).to(dtype)
                 y_ref = m(x)
-                apply_weight_only_int8_quant(m)
+                m = quantize(m, int8wo())
+                m = unwrap_tensor_subclass(m)
                 m(x)
                 m_c = torch.compile(m, mode="max-autotune")
                 y_wo, (code,) = run_and_get_code(m_c, x)
@@ -868,7 +873,8 @@ def test_weight_only_quant_use_mixed_mm(self, device, dtype):
                 x = torch.randn(*x_shape).to(device).to(dtype)
                 m = nn.Sequential(nn.Linear(4, 5)).to(device).to(dtype)
                 y_ref = m(x)
-                apply_weight_only_int8_quant(m)
+                m = quantize(m, int8wo())
+                m = unwrap_tensor_subclass(m)
                 m_c = torch.compile(m, mode="max-autotune")
                 y_wo, (code,) = run_and_get_code(m_c, x)
                 sqnr = compute_error(y_ref, y_wo)
@@ -908,7 +914,9 @@ def forward(self, x):
         ref_f = model(x)
 
         # save quantized state_dict
-        api(model)
+        quantize(model, api())
+        unwrap_tensor_subclass(model)
+
         torch.save(model.state_dict(), "test.pth")
         # get quantized reference
         model_qc = torch.compile(model, mode="max-autotune")
@@ -919,11 +927,13 @@ def forward(self, x):
         # load model structure
         with torch.device('meta'):
             model = test_model().to(dtype=test_dtype)
-        api(model)
+        quantize(model, api())
+        unwrap_tensor_subclass(model)
 
         # load quantized state_dict
         state_dict = torch.load("test.pth", mmap=True)
         os.remove("test.pth")
+
         model.load_state_dict(state_dict, assign=True)
         model = model.to(device=test_device, dtype=test_dtype).eval()
 
@@ -936,23 +946,26 @@ def forward(self, x):
 
     @parameterized.expand(COMMON_DEVICE_DTYPE)
     @torch.no_grad()
+    @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "Skip if torch version is prior to 2.4 since the current API relies on a parametrization fix")
     def test_save_load_dqtensors(self, device, dtype):
         if device == "cpu":
             self.skipTest(f"indcutor failed for cpu right now")
-        self._test_handle_save_load_meta_impl(change_linear_weights_to_int8_dqtensors, device, test_dtype=dtype)
+        self._test_handle_save_load_meta_impl(int8da_int8w, device, test_dtype=dtype)
 
     @parameterized.expand(COMMON_DEVICE_DTYPE)
     @torch.no_grad()
+    @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "Skip if torch version is prior to 2.4 since the current API relies on a parametrization fix")
     def test_save_load_int8woqtensors(self, device, dtype):
-        self._test_handle_save_load_meta_impl(change_linear_weights_to_int8_woqtensors, device, test_dtype=dtype)
+        self._test_handle_save_load_meta_impl(int8wo, device, test_dtype=dtype)
 
     @parameterized.expand(COMMON_DEVICE_DTYPE)
     @unittest.skipIf(not TORCH_VERSION_AFTER_2_3, "int4 requires torch nightly.")
     @torch.no_grad()
+    @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "Skip if torch version is prior to 2.4 since the current API relies on a parametrization fix")
     def test_save_load_int4woqtensors(self, device, dtype):
         if dtype != torch.bfloat16:
             self.skipTest(f"Fails for {dtype}")
-        self._test_handle_save_load_meta_impl(change_linear_weights_to_int4_woqtensors, device, 20, test_dtype=dtype)
+        self._test_handle_save_load_meta_impl(int4wo, device, 20, test_dtype=dtype)
 
 
 class TorchCompileUnitTest(unittest.TestCase):
@@ -1271,8 +1284,7 @@ def forward(self, x):
         model = test_model().to(dtype=test_dtype, device=test_device).eval()
         ref_f = model(x)
 
-        kwargs = {"dtype": test_dtype}
-        api(model, **kwargs)
+        quantize(model, api())
 
         # running model
         model(x)
@@ -1317,8 +1329,8 @@ def forward(self, x):
         model = test_model().to(dtype=test_dtype, device=test_device).eval()
         ref_f = model(x)
 
-        kwargs = {"dtype": test_dtype}
-        api(model, **kwargs)
+        model = quantize(model, api())
+        model = unwrap_tensor_subclass(model)
 
         # running model
         ref = model(x)
diff --git a/test/quantization/test_quant_api.py b/test/quantization/test_quant_api.py
index e0ead9ad12..f16a2c4d2a 100644
--- a/test/quantization/test_quant_api.py
+++ b/test/quantization/test_quant_api.py
@@ -35,15 +35,13 @@
 )
 from torchao.quantization.quant_api import (
     _replace_with_custom_fn_if_matches_filter,
-    apply_dynamic_quant,
-    apply_weight_only_int8_quant,
     Quantizer,
     TwoStepQuantizer,
     quantize,
-    get_apply_8da4w_quant,
-    get_apply_int4wo_quant,
-    get_apply_int8wo_quant,
-    get_apply_int8dyn_quant,
+    int8da_int4w,
+    int4wo,
+    int8wo,
+    int8da_int8w,
 )
 from torchao.utils import (
     TORCH_VERSION_AFTER_2_3,
@@ -53,6 +51,7 @@
 from torchao._models.llama.tokenizer import get_tokenizer
 from torchao._models.llama.model import Transformer, prepare_inputs_for_model
 import copy
+import tempfile
 
 
 def dynamic_quant(model, example_inputs):
@@ -62,20 +61,6 @@ def dynamic_quant(model, example_inputs):
     m = convert_pt2e(m)
     return m
 
-def _apply_dynamic_quant(model):
-    """
-    Applies dynamic symmetric per-token activation and per-channel weight
-    quantization to all linear layers in the given model using
-    module swaps.
-    """
-    _replace_with_custom_fn_if_matches_filter(
-        model,
-        lambda linear_mod: dynamic_quant(linear_mod, (torch.randn(1, linear_mod.in_features),)),
-        lambda mod, fqn: isinstance(mod, torch.nn.Linear),
-    )
-    return model
-
-
 def capture_and_prepare(model, example_inputs):
     m = torch.export.export(model, example_inputs)
     quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config(is_dynamic=True))
@@ -104,7 +89,7 @@ def convert(self, model: torch.nn.Module) -> torch.nn.Module:
 
 class TorchCompileDynamicQuantizer(Quantizer):
     def quantize(self, model: torch.nn.Module) -> torch.nn.Module:
-        apply_dynamic_quant(model)
+        quantize(model, int8da_int8w())
         return model
 
 class ToyLinearModel(torch.nn.Module):
@@ -127,11 +112,13 @@ def _ref_change_linear_weights_to_int8_dqtensors(model, filter_fn=None, **kwargs
     The deprecated implementation for int8 dynamic quant API, used as a reference for
     numerics and performance
     """
-    from torchao.quantization.quant_api import _in_features_greater_than_16
     from torchao.quantization.quant_api import _is_linear
     from torchao.quantization.quant_api import _get_subclass_inserter
     from torchao.quantization.subclass import Int8DynamicallyQuantizedLinearWeight
 
+    def _in_features_greater_than_16(mod, *args):
+        return hasattr(mod, "in_features") and mod.in_features > 16
+
     if filter_fn is None:
         filter_fn = lambda *args: _is_linear(*args) and _in_features_greater_than_16(
             *args
@@ -167,7 +154,7 @@ class TestQuantFlow(unittest.TestCase):
     def test_dynamic_quant_gpu_singleline(self):
         m = ToyLinearModel().eval()
         example_inputs = m.example_inputs()
-        m = _apply_dynamic_quant(m)
+        m = quantize(m, int8da_int8w())
         quantized = m(*example_inputs)
         # AssertionError: Expecting input to have dtype torch.float32, but got dtype: torch.float64
         # While executing %choose_qparams_tensor_1 : [num_users=2] = call_function[target=torch.ops.quantized_decomposed.choose_qparams.tensor](args = (%arg0_3, -128, 127, 0.000244140625, torch.int8), kwargs = {})
@@ -205,16 +192,21 @@ def test_dynamic_quant_gpu_unified_api_eager_mode_impl(self):
     @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
     def test_int8_wo_quant_save_load(self):
         m = ToyLinearModel().eval().cpu()
-        apply_weight_only_int8_quant(m)
+        m = quantize(m, int8wo())
+
+        from torchao.utils import unwrap_tensor_subclass
+        unwrap_tensor_subclass(m)
         example_inputs = m.example_inputs()
         ref = m(*example_inputs)
-        _TMP_FN = "_test.pt"
-        torch.save(m.state_dict(), _TMP_FN)
+        with tempfile.NamedTemporaryFile() as f:
+            torch.save(m.state_dict(), f)
+            f.seek(0)
+            state_dict = torch.load(f)
 
-        state_dict = torch.load(_TMP_FN)
-        os.remove(_TMP_FN)
         m2 = ToyLinearModel().eval()
-        apply_weight_only_int8_quant(m2)
+        m2 = quantize(m2, int8wo())
+        unwrap_tensor_subclass(m2)
+
         m2.load_state_dict(state_dict)
         m2 = m2.to(device="cuda")
         example_inputs = map(lambda x: x.cuda(), example_inputs)
@@ -508,7 +500,7 @@ def test_quantized_tensor_subclass_8da4w(self):
         m = ToyLinearModel().eval()
         m_copy = copy.deepcopy(m)
         example_inputs = m.example_inputs()
-        m = quantize(m, get_apply_8da4w_quant(groupsize=groupsize))
+        m = quantize(m, int8da_int4w(groupsize=groupsize))
 
         assert isinstance(m.linear1.weight, LinearActQuantizedTensor)
         assert isinstance(m.linear2.weight, LinearActQuantizedTensor)
@@ -537,7 +529,7 @@ def test_quantized_tensor_subclass_int4(self):
         example_inputs = m.example_inputs(dtype=torch.bfloat16, device="cuda")
 
         groupsize = 32
-        m = quantize(m, get_apply_int4wo_quant(groupsize=groupsize))
+        m = quantize(m, int4wo(groupsize=groupsize))
         assert isinstance(m.linear1.weight, AffineQuantizedTensor)
         assert isinstance(m.linear2.weight, AffineQuantizedTensor)
 
@@ -557,7 +549,7 @@ def test_quantized_tensor_subclass_int8_wo(self):
         m_copy = copy.deepcopy(m)
         example_inputs = tuple(map(lambda x: x.to(torch.bfloat16), m.example_inputs()))
 
-        m = quantize(m, get_apply_int8wo_quant())
+        m = quantize(m, int8wo())
 
         assert isinstance(m.linear1.weight, AffineQuantizedTensor)
         assert isinstance(m.linear2.weight, AffineQuantizedTensor)
@@ -580,7 +572,7 @@ def test_quantized_tensor_subclass_int8_dyn_quant(self):
         m_copy = copy.deepcopy(m)
         # setting batch_size to 20 to be compatible with the kernel
         example_inputs = m.example_inputs(batch_size=20, dtype=torch.bfloat16, device="cuda")
-        m = quantize(m, get_apply_int8dyn_quant())
+        m = quantize(m, int8da_int8w())
 
         assert isinstance(m.linear1.weight, LinearActQuantizedTensor)
         assert isinstance(m.linear2.weight, LinearActQuantizedTensor)
diff --git a/torchao/dtypes/aqt.py b/torchao/dtypes/aqt.py
index ae05720b05..02737fadb6 100644
--- a/torchao/dtypes/aqt.py
+++ b/torchao/dtypes/aqt.py
@@ -394,8 +394,6 @@ def __new__(
         kwargs["layout"] = (
             kwargs.get("layout") if kwargs.get("layout", False) else layout_tensor.layout
         )
-        if dtype is None:
-            dtype = scale.dtype
         kwargs["dtype"] = dtype
         if strides is not None:
             kwargs["strides"] = strides
diff --git a/torchao/quantization/README.md b/torchao/quantization/README.md
index 04efddd0ca..a86f9493d9 100644
--- a/torchao/quantization/README.md
+++ b/torchao/quantization/README.md
@@ -56,33 +56,139 @@ with open("quantization-cache.pkl", "wb") as f:
 with open("quantization-cache.pkl", "rb") as f:
     torchao.quantization.AUTOQUANT_CACHE.update(pickle.load(f))
 ```
+## Affine Quantization
+Affine quantization refers to the type of quantization that maps from floating point numbers to quantized numbers (typically integer) with an affine transformation, i.e.: `quantized_val = float_val / scale + zero_point` where `scale` and `zero_point` are quantization parameters for some granularity and based on some data.
 
+### Quantization Primitives
+We used to have different quantize and dequantize operators for quantization with different granularities. But in the end these can all be expressed with a `block_size` argument with different settings, so we unified existing quant primitives to `choose_qparams_affine`, `quantize_affine` and `dequantize_affine` that can represent symmetric/asymmetric per tensor/channel/token/channel_group quantization, this can be used to implement the unified quantized tensor subclass.
 
-## A8W8 Dynamic Quantization
+### Quantized Tensor Subclass
+We also have a unified quantized tensor subclass that implements how to get a quantized tensor from floating point tensor and what does it mean to call linear ops on an instance of the tensor, e.g. `F.linear` and `aten.addmm`, with this we could dispatch to different operators (e.g. `int4mm` op) based on device (cpu, cuda) and quantization settings (`int4`, `int8`) and also packing formats (e.g. format optimized for cpu int4 mm kernel)
+
+### Quantization Flow Example
+Let's use int4 weight only quantization that's targeting tinygemm int4 weight only quantized matmul
+as an example:
+```python
+import torch
+from torchao.quantization.quant_primitives import MappingType, ZeroPointDomain
+from torchao.dtypes import to_aq
+from torch._inductor.runtime.runtime_utils import do_bench_gpu
+import copy
+from torchao.quantization.quant_api import (
+    quantize,
+    int4wo,
+)
+
+class ToyLinearModel(torch.nn.Module):
+    def __init__(self, m=64, n=32, k=64):
+        super().__init__()
+        self.linear1 = torch.nn.Linear(m, n, bias=False)
+        self.linear2 = torch.nn.Linear(n, k, bias=False)
+
+    def example_inputs(self, batch_size=1, dtype=torch.float32, device="cpu"):
+        return (torch.randn(batch_size, self.linear1.in_features, dtype=dtype, device=device),)
+
+    def forward(self, x):
+        x = self.linear1(x)
+        x = self.linear2(x)
+        return x
+
+dtype = torch.bfloat16
+m = ToyLinearModel(1024, 1024, 1024).eval().to(dtype).to("cuda")
+m_bf16 = copy.deepcopy(m)
+example_inputs = m.example_inputs(dtype=dtype, device="cuda")
+
+m_bf16 = torch.compile(m_bf16, mode='max-autotune')
+# apply int4 weight only quant (compatible with tinygemm int4 weight only quant mm kernel in torchao)
+groupsize = 32
+m = quantize(m, int4wo(groupsize=groupsize))
+
+torch._inductor.config.force_fuse_int_mm_with_mul = True
+torch._inductor.config.use_mixed_mm = True
+
+# temporary workaround for tensor subclass + torch.compile
+from torchao.quantization.utils import unwrap_tensor_subclass
+m = unwrap_tensor_subclass(m)
+# compile the model to improve performance
+m = torch.compile(m, mode='max-autotune')
+
+# benchmark to see the speedup
+from torchao.utils import benchmark_model
+
+num_runs = 100
+torch._dynamo.reset()
+bf16_time = benchmark_model(m_bf16, num_runs, example_inputs[0])
+print(f"bf16 mean time: {bf16_time}")
+int4_time = benchmark_model(m, num_runs, example_inputs[0])
+print(f"int4 weight only quantized mean time: {int4_time}")
+print(f"speedup: {bf16_time / int4_time}")
+
+# output (1xA100 GPU machine)
+bf16 mean time: 71.457685546875
+int4 weight only quantized mean time: 31.4580908203125
+speedup: 2.2715200981216173
+```
+
+What we do underlying the APIs are roughly the following:
+```
+from torchao.dtypes.aqt import to_aq
+def int8wo_quant(weight):
+    return to_aq(weight, MappingType.SYMMETRIC, (1, weight.shape[1]), torch.int8, eps=torch.finfo(torch.float32).eps, zero_point_dtype=torch.int64)
+
+for n, m in model.named_modules():
+    if isinstance(m, torch.nn.Linear):
+        # optional filtering for module name, shape etc.
+        m.weight = nn.Parameter(int8wo_quant(m.weight))
+        
+        # note: quantization for activation need to be applied after the weight quantization
+        # quantization activation (needed by dynamic quantization)
+        input_quant_func = int8wo_quant  # specify how input activation is quantized
+        m.weight = nn.Parameter(to_linear_act_quantized(m.weight, input_quant_func))
+```
+The model/tensor subclass should also be compatible with AOTI and torch.export, currently we can support
+`torch.export.export` and `torch.aot_compile` with the following workaround:
+```
+from torchao.quantization.utils import unwrap_tensor_subclass
+m_unwrapped = unwrap_tensor_subclass(m)
+
+
+# export
+m = torch.export.export(m_unwrapped, example_inputs).module()
+
+# aot_compile
+torch._export.aot_compile(m_unwrapped, example_inputs)
+```
+
+### Other Available Quantization Techniques
+#### A8W8 Dynamic Quantization
 
 ```python
 # Fuse the int8*int8 -> int32 matmul and subsequent mul op avoiding materialization of the int32 intermediary tensor
 torch._inductor.config.force_fuse_int_mm_with_mul = True
 from torchao.quantization import quant_api
-# convert linear modules to quantized tensor subclasses
-quant_api.change_linear_weights_to_int8_dqtensors(model)
+
+from torchao.quantization.quant_api import quantize
+from torchao.quantization.quant_api import get_apply_int8dyn_quant
+quantize(model, get_apply_int8dyn_quant())
 ```
 
-## A16W8 WeightOnly Quantization
+#### A16W8 WeightOnly Quantization
 
 ```python
-from torchao.quantization import quant_api
-quant_api.change_linear_weights_to_int8_woqtensors(model)
+from torchao.quantization.quant_api import quantize
+from torchao.quantization.quant_api import get_apply_int8wo_quant
+quantize(model, get_apply_int8wo_quant())
 ```
 
 This technique works best when the torch._inductor.config.use_mixed_mm option is enabled. This avoids dequantizing the weight tensor before the matmul, instead fusing the dequantization into the matmul, thereby avoiding materialization of a large floating point weight tensor.
 
 
-## A16W4 WeightOnly Quantization
+#### A16W4 WeightOnly Quantization
 
 ```python
-from torchao.quantization import quant_api
-quant_api.change_linear_weights_to_int4_woqtensors(model)
+from torchao.quantization.quant_api import quantize
+from torchao.quantization.quant_api import get_apply_int4wo_quant
+quantize(model, get_apply_int4wo_quant(groupsize=32))
 ```
 
 Note: The quantization error incurred by applying int4 quantization to your model can be fairly significant, so using external techniques like GPTQ may be necessary to obtain a usable model.
@@ -137,17 +243,17 @@ model = quantizer.quantize(model, inputs).cuda()
 
 ```
 
-## A8W8 Dynamic Quantization
+## (To be deprecated) A8W8 Dynamic Quantization
 
 ```Python
 from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer
-quantizer = Int8DynActInt4WeightQuantizer(groupsize=32)
+quantizer = Int8DynActInt4WeightQuantizer(groupsize=128)
 model = quantizer.quantize(model)
 ```
 
 This is used in [ExecuTorch](https://github.com/pytorch/executorch) to quantize llama model right now.
 
-## A8W8 Dynamic Quantization with Smoothquant
+## (To be moved to prototype) A8W8 Dynamic Quantization with Smoothquant
 
 We've also implemented a version of [smoothquant](https://arxiv.org/abs/2211.10438) with the same GEMM format as above. Due to requiring calibration, the API is more complicated.
 
@@ -186,118 +292,6 @@ model = torch.compile(model, mode='max-autotune')
 model(input)
 ```
 
-## Affine Quantization
-Affine quantization refers to the type of quantization that maps from floating point numbers to quantized numbers (typically integer) with an affine transformation, i.e.: `quantized_val = float_val / scale + zero_point` where `scale` and `zero_point` are quantization parameters for some granularity and based on some data.
-
-### Quantization Primitives
-We used to have different quantize and dequantize operators for quantization with different granularities. But in the end these can all be expressed with a `block_size` argument with different settings, so we unified existing quant primitives to `choose_qparams_affine`, `quantize_affine` and `dequantize_affine` that can represent symmetric/asymmetric per tensor/channel/token/channel_group quantization, this can be used to implement the unified quantized tensor subclass.
-
-### Quantized Tensor Subclass
-We also have a unified quantized tensor subclass that implements how to get a quantized tensor from floating point tensor and what does it mean to call linear ops on an instance of the tensor, e.g. `F.linear` and `aten.addmm`, with this we could dispatch to different operators (e.g. `int4mm` op) based on device (cpu, cuda) and quantization settings (`int4`, `int8`) and also packing formats (e.g. format optimized for cpu int4 mm kernel)
-
-### Quantization Flow
-What we need to do afterwards is roughly the following
-
-```
-from torchao.dtypes.aqt import to_aq
-def apply_int8wo_quant(weight):
-    mapping_type = MappingType.SYMMETRIC
-    target_dtype = torch.int8
-    eps = torch.finfo(torch.float32).eps
-    zero_point_dtype = torch.int64
-    block_size = (1, weight.shape[1])
-    return to_aq(weight, mapping_type, block_size, target_dtype, eps=eps, zero_point_dtype=zero_point_dtype)
-
-for n, m in model.named_modules():
-    if isinstance(m, torch.nn.Linear):
-        # optional filtering for module name, shape etc.
-        m.weight = nn.Parameter(apply_int8wo_quant(m.weight))
-        # note: quantization for activation need to be applied after the weight quantization
-        # quantization activation (needed by dynamic quantization)
-        # input_quant_func = apply_int8wo_quant  # specify how input activation is quantized
-        # m.weight = nn.Parameter(to_laq(m.weight, input_quant_func))
-```
-The model/tensor subclass should also be compatible with AOTI and torch.export, currently we can support
-`torch.export.export` and `torch.aot_compile` with the following workaround:
-```
-from torchao.quantization.utils import unwrap_tensor_subclass
-m_unwrapped = unwrap_tensor_subclass(m)
-
-
-# export
-m = torch.export.export(m_unwrapped, example_inputs).module()
-
-# aot_compile
-torch._export.aot_compile(m_unwrapped, example_inputs)
-```
-
-But we expect this will be integrated into the export path by default in the future.
-
-
-### Example
-Let's use int4 weight only quantization that's targeting tinygemm int4 weight only quantized matmul
-as an example:
-```python
-import torch
-from torchao.quantization.quant_primitives import MappingType, ZeroPointDomain
-from torchao.dtypes import to_aq
-from torch._inductor.runtime.runtime_utils import do_bench_gpu
-import copy
-from torchao.quantization.quant_api import (
-    quantize,
-    get_apply_int4wo_quant,
-)
-
-class ToyLinearModel(torch.nn.Module):
-    def __init__(self, m=64, n=32, k=64):
-        super().__init__()
-        self.linear1 = torch.nn.Linear(m, n, bias=False)
-        self.linear2 = torch.nn.Linear(n, k, bias=False)
-
-    def example_inputs(self, batch_size=1, dtype=torch.float32, device="cpu"):
-        return (torch.randn(batch_size, self.linear1.in_features, dtype=dtype, device=device),)
-
-    def forward(self, x):
-        x = self.linear1(x)
-        x = self.linear2(x)
-        return x
-
-dtype = torch.bfloat16
-m = ToyLinearModel(1024, 1024, 1024).eval().to(dtype).to("cuda")
-m_bf16 = copy.deepcopy(m)
-example_inputs = m.example_inputs(dtype=dtype, device="cuda")
-
-m_bf16 = torch.compile(m_bf16, mode='max-autotune')
-# apply int4 weight only quant (compatible with tinygemm int4 weight only quant mm kernel in torchao)
-groupsize = 32
-m = quantize(m, get_apply_int4wo_quant(groupsize=groupsize))
-
-torch._inductor.config.force_fuse_int_mm_with_mul = True
-torch._inductor.config.use_mixed_mm = True
-
-# temporary workaround for tensor subclass + torch.compile
-from torchao.quantization.utils import unwrap_tensor_subclass
-m = unwrap_tensor_subclass(m)
-# compile the model to improve performance
-m = torch.compile(m, mode='max-autotune')
-
-# benchmark to see the speedup
-from torchao.utils import benchmark_model
-
-num_runs = 100
-torch._dynamo.reset()
-bf16_time = benchmark_model(m_bf16, num_runs, example_inputs[0])
-print(f"bf16 mean time: {bf16_time}")
-int4_time = benchmark_model(m, num_runs, example_inputs[0])
-print(f"int4 weight only quantized mean time: {int4_time}")
-print(f"speedup: {bf16_time / int4_time}")
-
-# output (1xA100 GPU machine)
-bf16 mean time: 71.457685546875
-int4 weight only quantized mean time: 31.4580908203125
-speedup: 2.2715200981216173
-```
-
 ## Notes
 
 1. APIs have been hardware tested on A100 and T4(colab)
diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py
index 510db85512..5b899a1afa 100644
--- a/torchao/quantization/quant_api.py
+++ b/torchao/quantization/quant_api.py
@@ -13,10 +13,6 @@
 both because primitives were designed based on the fusions that
 come along with it and because that is how we access the intended quantized
 and mixed GEMM kernels
-
-TODO: There are 2 different approaches to quantizing a model. The first and more historically
-popular approach is to use module swaps which explicitly change the linear modules and the second
-approach is to instead use subclasses to change the interpretation of the linear module
 """
 
 import torch
@@ -24,18 +20,15 @@
 import torch.nn.functional as F
 from typing import Any, Callable
 
-from .dynamic_quant import DynamicallyPerAxisQuantizedLinear
 from torchao.utils import (
     TORCH_VERSION_AFTER_2_4,
     unwrap_tensor_subclass,
 )
 
 from .subclass import (
-    Int4WeightOnlyQuantizedLinearWeight,
-    Int8DynamicallyQuantizedLinearWeight,
-    Int8WeightOnlyQuantizedLinearWeight,
     QuantizedLinearWeightBase,
-    to_laq,
+    LinearActQuantizedTensor,
+    to_linear_act_quantized,
 )
 
 from .quant_primitives import (
@@ -52,11 +45,6 @@
 
 
 __all__ = [
-    "apply_weight_only_int8_quant",
-    "apply_dynamic_quant",
-    "change_linear_weights_to_int8_dqtensors",
-    "change_linear_weights_to_int8_woqtensors",
-    "change_linear_weights_to_int4_woqtensors",
     "swap_conv2d_1x1_to_linear",
     "Quantizer",
     "TwoStepQuantizer",
@@ -65,10 +53,10 @@
     "quantize",
     "autoquant",
     "_get_subclass_inserter",
-    "get_apply_8da4w_quant",
-    "get_apply_int4wo_quant",
-    "get_apply_int8wo_quant",
-    "get_apply_int8dyn_quant",
+    "int8da_int4w",
+    "int8da_int8w",
+    "int4wo",
+    "int8wo",
 ]
 
 from .GPTQ import (
@@ -81,6 +69,68 @@
     "Int8DynActInt4WeightGPTQQuantizer",
 ]
 
+### TO BE DEPRECATED START
+from .subclass import (
+    Int4WeightOnlyQuantizedLinearWeight,
+    Int8DynamicallyQuantizedLinearWeight,
+    Int8WeightOnlyQuantizedLinearWeight,
+)
+
+def _in_features_greater_than_16(mod, *args):
+    return hasattr(mod, "in_features") and mod.in_features > 16
+
+def change_linear_weights_to_int8_dqtensors(model, filter_fn=None, **kwargs):
+    """
+    Converts all linear weight tensors to the `Int8DynamicallyQuantizedLinearWeight`
+    Tensor subclass, effectively applying the same form of quantization
+    as apply_dynamic_quant while not modifying the linear modules.
+    """
+    if filter_fn is None:
+        filter_fn = lambda *args: _is_linear(*args) and _in_features_greater_than_16(
+            *args
+        )
+
+    _replace_with_custom_fn_if_matches_filter(
+        model, _get_subclass_inserter(Int8DynamicallyQuantizedLinearWeight, enable_parametrization=False, **kwargs), filter_fn
+    )
+
+
+def change_linear_weights_to_int8_woqtensors(model, filter_fn=None, **kwargs):
+    """
+    Converts all linear weight tensors to the
+    `Int8WeightOnlyQuantizedLinearWeight` tensor subclass,
+    effectively applying the same form of quantization
+    as apply_weight_only_int8_quant while not modifying the linear modules.
+    """
+    _replace_with_custom_fn_if_matches_filter(
+        model,
+        _get_subclass_inserter(Int8WeightOnlyQuantizedLinearWeight, enable_parametrization=False, **kwargs),
+        _is_linear if filter_fn is None else filter_fn,
+    )
+
+def change_linear_weights_to_int4_woqtensors(model, groupsize=128, inner_k_tiles=8, filter_fn=None):
+    """
+    Converts all linear weight tensors to the
+    `Int4WeightOnlyQuantizedLinearWeight` tensor subclass,
+    effectively applying the same form of quantization
+    as apply_dynamic_quant while not modifying the linear modules.
+    Args:
+        `groupsize`: parameter for quantization, controls the granularity of quantization, smaller
+         size is more fine grained, choices are [256, 128, 64, 32]
+        `inner_k_tiles`: parameter for int4 mm kernel, choices are [8, 4, 2]
+    """
+    if filter_fn is None:
+        filter_fn = _is_linear
+
+    _replace_with_custom_fn_if_matches_filter(
+        model,
+        _get_subclass_inserter(Int4WeightOnlyQuantizedLinearWeight, enable_parametrization=False, groupsize=groupsize, inner_k_tiles=inner_k_tiles),
+        filter_fn,
+    )
+
+### TO BE DEPRECATED END
+
+
 
 def _replace_with_custom_fn_if_matches_filter(
     model,
@@ -115,39 +165,20 @@ def _replace_with_custom_fn_if_matches_filter(
 
 
 def _is_linear(mod, *args):
+    # avoid circular dep
+    from torchao.dtypes import AffineQuantizedTensor
+
+    # adding weight tensor subclass isinstance check to make sure the weight is only quantized once
+    # when it is shared by multiple linear modules
     return (
         isinstance(mod, torch.nn.Linear)
         and hasattr(mod, "weight")
         and not isinstance(mod.weight, QuantizedLinearWeightBase)
         and not isinstance(mod.weight, AutoQuantizableLinearWeight)
+        and not isinstance(mod.weight, AffineQuantizedTensor)
+        and not isinstance(mod.weight, LinearActQuantizedTensor)
     )
 
-
-def _in_features_greater_than_16(mod, *args):
-    return hasattr(mod, "in_features") and mod.in_features > 16
-
-
-def apply_weight_only_int8_quant(model, filter_fn=None):
-    """
-    Applies weight-only symmetric per-channel int8 quantization to all linear layers
-    in the given model using module swaps.
-    """
-    _replace_with_custom_fn_if_matches_filter(
-        model,
-        WeightOnlyInt8QuantLinear.from_float,
-        _is_linear if filter_fn is None else filter_fn,
-    )
-
-
-def apply_dynamic_quant(model, filter_fn=None):
-    """
-    Applies dynamic symmetric per-token activation and per-channel weight
-    quantization to all linear layers by converting all linear weight
-    tensors to the `Int8DynamicallyQuantizedLinearWeight` Tensor subclass.
-    """
-    change_linear_weights_to_int8_dqtensors(model, filter_fn)
-
-
 import torch.nn.utils.parametrize as parametrize
 
 def _get_subclass_inserter(cls, enable_parametrization=False, **kwargs):
@@ -178,70 +209,6 @@ def insert_subclass(lin):
     return insert_subclass
 
 
-def change_linear_weights_to_int8_dqtensors(model, filter_fn=None, **kwargs):
-    """
-    Converts all linear weight tensors to the `Int8DynamicallyQuantizedLinearWeight`
-    Tensor subclass, effectively applying the same form of quantization
-    as apply_dynamic_quant while not modifying the linear modules.
-    """
-    if filter_fn is None:
-        filter_fn = lambda *args: _is_linear(*args) and _in_features_greater_than_16(
-            *args
-        )
-
-    if TORCH_VERSION_AFTER_2_4:
-        quantize(model, get_apply_int8dyn_quant(), filter_fn)
-        unwrap_tensor_subclass(model, filter_fn)
-    else:
-        _replace_with_custom_fn_if_matches_filter(
-            model, _get_subclass_inserter(Int8DynamicallyQuantizedLinearWeight, enable_parametrization=False, **kwargs), filter_fn
-        )
-
-
-def change_linear_weights_to_int8_woqtensors(model, filter_fn=None, **kwargs):
-    """
-    Converts all linear weight tensors to the
-    `Int8WeightOnlyQuantizedLinearWeight` tensor subclass,
-    effectively applying the same form of quantization
-    as apply_weight_only_int8_quant while not modifying the linear modules.
-    """
-
-    if TORCH_VERSION_AFTER_2_4:
-        quantize(model, get_apply_int8wo_quant(), filter_fn)
-        unwrap_tensor_subclass(model, filter_fn)
-    else:
-        _replace_with_custom_fn_if_matches_filter(
-            model,
-            _get_subclass_inserter(Int8WeightOnlyQuantizedLinearWeight, enable_parametrization=False, **kwargs),
-            _is_linear if filter_fn is None else filter_fn,
-        )
-
-
-def change_linear_weights_to_int4_woqtensors(model, groupsize=128, inner_k_tiles=8, filter_fn=None):
-    """
-    Converts all linear weight tensors to the
-    `Int4WeightOnlyQuantizedLinearWeight` tensor subclass,
-    effectively applying the same form of quantization
-    as apply_dynamic_quant while not modifying the linear modules.
-
-    Args:
-        `groupsize`: parameter for quantization, controls the granularity of quantization, smaller
-         size is more fine grained, choices are [256, 128, 64, 32]
-        `inner_k_tiles`: parameter for int4 mm kernel, choices are [8, 4, 2]
-    """
-    if filter_fn is None:
-        filter_fn = _is_linear
-
-    if TORCH_VERSION_AFTER_2_4:
-        quantize(model, get_apply_int4wo_quant(groupsize=groupsize, inner_k_tiles=inner_k_tiles), filter_fn)
-        unwrap_tensor_subclass(model, filter_fn)
-    else:
-        _replace_with_custom_fn_if_matches_filter(
-            model,
-            _get_subclass_inserter(Int4WeightOnlyQuantizedLinearWeight, enable_parametrization=False, groupsize=groupsize, inner_k_tiles=inner_k_tiles),
-            filter_fn,
-        )
-
 def swap_conv2d_1x1_to_linear(model, filter_fn=None):
     """
     Changes all conv2d 1x1 modules to equivalent linear modules so that they can then be quantized.
@@ -303,7 +270,9 @@ def quantize(model: torch.nn.Module, apply_tensor_subclass: Callable[[torch.Tens
         zero_point_dtype = torch.bfloat16
         zero_point_domain = ZeroPointDomain.FLOAT
 
-        apply_weight_quant = lambda x: to_aq(x, mapping_type, block_size, target_dtype, quant_min, quant_max, eps, zero_point_dtype=zero_point_dtype, preserve_zero=preserve_zero, zero_point_domain=zero_point_domain)
+        apply_weight_quant = lambda x: to_aq(
+          x, mapping_type, block_size, target_dtype, quant_min, quant_max, eps,
+          zero_point_dtype=zero_point_dtype, preserve_zero=preserve_zero, zero_point_domain=zero_point_domain)
 
         # apply to modules under block0 submodule
         def filter_fn(module, fqn):
@@ -319,11 +288,18 @@ def filter_fn(module, fqn):
     )
     return model
 
-def get_apply_8da4w_quant(groupsize=32):
+def int8da_int4w(groupsize=32):
+    """Applies int8 dynamic per token asymmetric activation quantization and int4 per group weight symmetric quantization to linear
+    This is used to produce a model for executorch backend, but currently executorch did not
+    support lowering for the quantized model from this flow yet
 
+    Args:
+        `groupsize`: parameter for quantization, controls the granularity of quantization, smaller
+         size is more fine grained
+    """
     def apply_8da4w_quant(weight):
         # avoid circular dep
-        from torchao.dtypes.aqt import to_aq
+        from torchao.dtypes import to_aq
 
         # weight settings
         mapping_type = MappingType.SYMMETRIC
@@ -348,16 +324,25 @@ def get_per_token_block_size(x):
         input_quant_func = lambda x: to_aq(x, input_mapping_type, get_per_token_block_size(x), input_target_dtype)
 
         weight = to_aq(weight, mapping_type, block_size, target_dtype, quant_min, quant_max, eps)
-        weight = to_laq(weight, input_quant_func)
+        weight = to_linear_act_quantized(weight, input_quant_func)
         return weight
 
     return apply_8da4w_quant
 
 
-def get_apply_int4wo_quant(groupsize=32, inner_k_tiles=8):
+def int4wo(groupsize=128, inner_k_tiles=8):
+    """
+    Applies uint4 weight-only asymmetric per-group quantization to linear layers, using
+    "tensor_core_tiled" layout for speedup with tinygemm kernel
+
+    Args:
+        `groupsize`: parameter for quantization, controls the granularity of quantization, smaller
+         size is more fine grained, choices are [256, 128, 64, 32]
+        `inner_k_tiles`: parameter for int4 mm kernel, choices are [8, 4, 2]
+    """
     def apply_int4wo_quant(weight):
         # avoid circular dep
-        from torchao.dtypes.aqt import to_aq
+        from torchao.dtypes import to_aq
 
         mapping_type = MappingType.ASYMMETRIC
         block_size = (1, groupsize)
@@ -373,10 +358,13 @@ def apply_int4wo_quant(weight):
     return apply_int4wo_quant
 
 
-def get_apply_int8wo_quant():
+def int8wo():
+    """
+    Applies int8 weight-only symmetric per-channel quantization to linear layers.
+    """
     def apply_int8wo_quant(weight):
         # avoid circular dep
-        from torchao.dtypes.aqt import to_aq
+        from torchao.dtypes import to_aq
 
         mapping_type = MappingType.SYMMETRIC
         target_dtype = torch.int8
@@ -386,10 +374,19 @@ def apply_int8wo_quant(weight):
         return to_aq(weight, mapping_type, block_size, target_dtype, eps=eps, zero_point_dtype=zero_point_dtype)
     return apply_int8wo_quant
 
-def get_apply_int8dyn_quant():
+def int8da_int8w():
+    """
+    Applies int8 dynamic symmetric per-token activation and int8 per-channel weight
+    quantization to linear layers
+    """
     def apply_int8dyn_quant(weight):
+        in_features = weight.shape[1]
+        # int8 dynamic quantization only has benefit when in_feature > 16
+        if in_features <= 16:
+            return weight
+
         # avoid circular dep
-        from torchao.dtypes.aqt import to_aq
+        from torchao.dtypes import to_aq
         # weight settings
         mapping_type = MappingType.SYMMETRIC
         def get_weight_block_size(x):
@@ -414,6 +411,6 @@ def get_per_token_block_size(x):
 
         block_size = get_weight_block_size(weight)
         weight = to_aq(weight, mapping_type, block_size, target_dtype, eps=eps, zero_point_dtype=zero_point_dtype)
-        weight = to_laq(weight, input_quant_func)
+        weight = to_linear_act_quantized(weight, input_quant_func)
         return weight
     return apply_int8dyn_quant
diff --git a/torchao/quantization/subclass.py b/torchao/quantization/subclass.py
index c299a5834f..a2801a622f 100644
--- a/torchao/quantization/subclass.py
+++ b/torchao/quantization/subclass.py
@@ -31,7 +31,7 @@
     "Int8WeightOnlyQuantizedLinearWeight",
     "Int4WeightOnlyQuantizedLinearWeight",
     "LinearActQuantizedTensor",
-    "to_laq",
+    "to_linear_act_quantized",
 ]
 
 
@@ -751,4 +751,4 @@ def __torch_dispatch__(cls, func, types, args, kwargs):
             f"LinearActQuantizedTensor dispatch: attempting to run {func}, this is not supported"
         )
 
-to_laq = LinearActQuantizedTensor.from_float
+to_linear_act_quantized = LinearActQuantizedTensor.from_float
diff --git a/torchao/utils.py b/torchao/utils.py
index 27650dae1c..4e2afc795d 100644
--- a/torchao/utils.py
+++ b/torchao/utils.py
@@ -126,6 +126,11 @@ def right_inverse(self, tensor):
         return plain_tensors
 
 def unwrap_tensor_subclass(model, filter_fn=None):
+    """Unwraps (nested) tensor subclass in the model to plain tensors
+    This is a workaround to make a model with tensor subclass to work with `torch.export.export`
+    and `torch.aot_compile`, we hope this can be integrated into compile stack soon
+    tracking issue: https://github.com/pytorch/ao/issues/345
+    """
     for name, child in model.named_children():
         # make sure child.weight is a tensor subclass
         if (
diff --git a/tutorials/quantize_vit/run_vit_b_quant.py b/tutorials/quantize_vit/run_vit_b_quant.py
index 918284ae1e..96ed6e00c8 100644
--- a/tutorials/quantize_vit/run_vit_b_quant.py
+++ b/tutorials/quantize_vit/run_vit_b_quant.py
@@ -3,6 +3,7 @@
 
 from torchao.utils import benchmark_model, profiler_runner
 from torchvision import models
+import torchao.quantization.quant_api as quant_api
 
 torch.set_float32_matmul_precision("high")
 # Load Vision Transformer model
@@ -16,7 +17,7 @@
 
 ## Quantization code - start
 # int8 act, int8 weight dynamic quantization, see README for other APIs
-torchao.apply_dynamic_quant(model)
+quant_api.quantize(model, quant_api.get_apply_int8dyn_quant())
 ## Quantization code - end
 
 ## compilation configs