diff --git a/README.md b/README.md
index 71fb25fa2..ba48dbf45 100644
--- a/README.md
+++ b/README.md
@@ -59,7 +59,7 @@ In practice these features alongside int4 weight only quantization allow us to *
 Post-training quantization can result in a fast and compact model, but may also lead to accuracy degradation. We recommend exploring Quantization Aware Training (QAT) to overcome this limitation. In collaboration with Torchtune, we've developed a QAT recipe that demonstrates significant accuracy improvements over traditional PTQ, recovering **96% of the accuracy degradation on hellaswag and 68% of the perplexity degradation on wikitext** for Llama3 compared to post-training quantization (PTQ). And we've provided a full recipe [here](https://pytorch.org/blog/quantization-aware-training/)
 
 ```python
-from torchao.quantization.prototype.qat import Int8DynActInt4WeightQATQuantizer
+from torchao.quantization.qat import Int8DynActInt4WeightQATQuantizer
 
 qat_quantizer = Int8DynActInt4WeightQATQuantizer()
 
diff --git a/test/quantization/test_qat.py b/test/quantization/test_qat.py
index 918697724..8b62b8f57 100644
--- a/test/quantization/test_qat.py
+++ b/test/quantization/test_qat.py
@@ -22,22 +22,22 @@
     PerRow,
     PerToken,
 )
-from torchao.quantization.prototype.qat.api import (
+from torchao.quantization.qat.api import (
     ComposableQATQuantizer,
     FakeQuantizeConfig,
 )
-from torchao.quantization.prototype.qat.fake_quantizer import (
+from torchao.quantization.qat.fake_quantizer import (
     FakeQuantizer,
 )
-from torchao.quantization.prototype.qat.embedding import (
+from torchao.quantization.qat.embedding import (
     FakeQuantizedEmbedding,
 )
-from torchao.quantization.prototype.qat.linear import (
+from torchao.quantization.qat.linear import (
     FakeQuantizedLinear,
     Int8DynActInt4WeightQATLinear,
     Int4WeightOnlyQATLinear
 )
-from torchao.quantization.prototype.qat.utils import (
+from torchao.quantization.qat.utils import (
     _choose_qparams_per_token_asymmetric,
     _fake_quantize_per_channel_group,
     _fake_quantize_per_token,
@@ -181,7 +181,7 @@ def _set_ptq_weight(
             Int8DynActInt4WeightLinear,
             WeightOnlyInt4Linear,
         )
-        from torchao.quantization.prototype.qat.linear import (
+        from torchao.quantization.qat.linear import (
             Int8DynActInt4WeightQATLinear,
             Int4WeightOnlyQATLinear,
         )
@@ -213,7 +213,7 @@ def _set_ptq_weight(
 
     @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower")
     def test_qat_8da4w_linear(self):
-        from torchao.quantization.prototype.qat.linear import Int8DynActInt4WeightQATLinear
+        from torchao.quantization.qat.linear import Int8DynActInt4WeightQATLinear
         from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear
 
         group_size = 128
@@ -238,7 +238,7 @@ def test_qat_8da4w_linear(self):
 
     @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower")
     def test_qat_8da4w_quantizer(self):
-        from torchao.quantization.prototype.qat import Int8DynActInt4WeightQATQuantizer
+        from torchao.quantization.qat import Int8DynActInt4WeightQATQuantizer
         from torchao.quantization.GPTQ import Int8DynActInt4WeightQuantizer
 
         group_size = 16
@@ -272,7 +272,7 @@ def test_qat_8da4w_quantizer(self):
 
     @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower")
     def test_qat_8da4w_quantizer_meta_weights(self):
-        from torchao.quantization.prototype.qat import Int8DynActInt4WeightQATQuantizer
+        from torchao.quantization.qat import Int8DynActInt4WeightQATQuantizer
 
         with torch.device("meta"):
             m = M()
@@ -287,7 +287,7 @@ def test_qat_8da4w_quantizer_disable_fake_quant(self):
         """
         Test that 8da4w QAT with disabled fake quant matches nn.Linear in forward.
         """
-        from torchao.quantization.prototype.qat import (
+        from torchao.quantization.qat.linear import (
             Int8DynActInt4WeightQATQuantizer,
             disable_8da4w_fake_quant,
             enable_8da4w_fake_quant,
@@ -346,7 +346,7 @@ def test_qat_8da4w_quantizer_disable_fake_quant_backward(self):
         """
         Test that 8da4w QAT with disabled fake quant matches nn.Linear in backward.
         """
-        from torchao.quantization.prototype.qat import (
+        from torchao.quantization.qat.linear import (
             Int8DynActInt4WeightQATQuantizer,
             disable_8da4w_fake_quant,
         )
@@ -428,7 +428,7 @@ def _test_qat_quantized_gradients(self, quantizer):
 
     @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower")
     def test_qat_8da4w_quantizer_gradients(self):
-        from torchao.quantization.prototype.qat import Int8DynActInt4WeightQATQuantizer
+        from torchao.quantization.qat import Int8DynActInt4WeightQATQuantizer
         quantizer = Int8DynActInt4WeightQATQuantizer(groupsize=16)
         self._test_qat_quantized_gradients(quantizer)
 
@@ -518,7 +518,7 @@ def test_qat_4w_primitives(self):
     @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower")
     @unittest.skipIf(not _CUDA_IS_AVAILABLE, "skipping when cuda is not available")
     def test_qat_4w_linear(self):
-        from torchao.quantization.prototype.qat.linear import Int4WeightOnlyQATLinear
+        from torchao.quantization.qat.linear import Int4WeightOnlyQATLinear
         from torchao.quantization.GPTQ import WeightOnlyInt4Linear
 
         group_size = 128
@@ -545,14 +545,14 @@ def test_qat_4w_linear(self):
 
     @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower")
     def test_qat_4w_quantizer_gradients(self):
-        from torchao.quantization.prototype.qat import Int4WeightOnlyQATQuantizer
+        from torchao.quantization.qat import Int4WeightOnlyQATQuantizer
         quantizer = Int4WeightOnlyQATQuantizer(groupsize=32, inner_k_tiles=8)
         self._test_qat_quantized_gradients(quantizer)
 
     @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower")
     @unittest.skipIf(not _CUDA_IS_AVAILABLE, "skipping when cuda is not available")
     def test_qat_4w_quantizer(self):
-        from torchao.quantization.prototype.qat import Int4WeightOnlyQATQuantizer
+        from torchao.quantization.qat import Int4WeightOnlyQATQuantizer
         from torchao.quantization.GPTQ import Int4WeightOnlyQuantizer
 
         group_size = 32
@@ -630,7 +630,7 @@ def test_composable_qat_quantizer(self):
 
     @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower")
     def test_qat_4w_embedding(self):
-        from torchao.quantization.prototype.qat import Int4WeightOnlyEmbeddingQATQuantizer
+        from torchao.quantization.qat import Int4WeightOnlyEmbeddingQATQuantizer
         model = M2()
         x = model.example_inputs()
         out = model(*x)
diff --git a/torchao/quantization/prototype/qat/_module_swap_api.py b/torchao/quantization/prototype/qat/_module_swap_api.py
deleted file mode 100644
index 0b44974f2..000000000
--- a/torchao/quantization/prototype/qat/_module_swap_api.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# For backward compatibility only
-# These will be removed in the future
-
-from .linear import (
-    Int8DynActInt4WeightQATQuantizer as Int8DynActInt4WeightQATQuantizerModuleSwap,
-    Int4WeightOnlyQATQuantizer as Int4WeightOnlyQATQuantizerModuleSwap,
-    enable_8da4w_fake_quant as enable_8da4w_fake_quant_module_swap,
-    disable_8da4w_fake_quant as disable_8da4w_fake_quant_module_swap,
-    enable_4w_fake_quant as enable_4w_fake_quant_module_swap,
-    disable_4w_fake_quant as disable_4w_fake_quant_module_swap,
-)
diff --git a/torchao/quantization/prototype/qat/README.md b/torchao/quantization/qat/README.md
similarity index 98%
rename from torchao/quantization/prototype/qat/README.md
rename to torchao/quantization/qat/README.md
index 286932229..6ecccd2b1 100644
--- a/torchao/quantization/prototype/qat/README.md
+++ b/torchao/quantization/qat/README.md
@@ -41,7 +41,7 @@ For example, on a single GPU:
 ```python
 import torch
 from torchtune.models.llama3 import llama3
-from torchao.quantization.prototype.qat import Int8DynActInt4WeightQATQuantizer
+from torchao.quantization.qat import Int8DynActInt4WeightQATQuantizer
 
 # Smaller version of llama3 to fit in a single GPU
 model = llama3(
diff --git a/torchao/quantization/prototype/qat/__init__.py b/torchao/quantization/qat/__init__.py
similarity index 55%
rename from torchao/quantization/prototype/qat/__init__.py
rename to torchao/quantization/qat/__init__.py
index 09ea6e708..09ef10af6 100644
--- a/torchao/quantization/prototype/qat/__init__.py
+++ b/torchao/quantization/qat/__init__.py
@@ -2,12 +2,7 @@
     ComposableQATQuantizer,
 )
 from .linear import (
-    disable_4w_fake_quant,
-    disable_8da4w_fake_quant,
-    enable_4w_fake_quant,
-    enable_8da4w_fake_quant,
     Int4WeightOnlyQATQuantizer,
-    Int8DynActInt4WeightQATLinear,
     Int8DynActInt4WeightQATQuantizer,
 )
 from .embedding import (
@@ -15,13 +10,8 @@
 )
 
 __all__ = [
-    "disable_4w_fake_quant",
-    "disable_8da4w_fake_quant",
-    "enable_4w_fake_quant",
-    "enable_8da4w_fake_quant",
     "ComposableQATQuantizer",
     "Int4WeightOnlyQATQuantizer",
     "Int4WeightOnlyEmbeddingQATQuantizer"
     "Int8DynActInt4WeightQATQuantizer",
-    "Int8DynActInt4WeightQATLinear",
 ]
diff --git a/torchao/quantization/prototype/qat/affine_fake_quantized_tensor.py b/torchao/quantization/qat/affine_fake_quantized_tensor.py
similarity index 100%
rename from torchao/quantization/prototype/qat/affine_fake_quantized_tensor.py
rename to torchao/quantization/qat/affine_fake_quantized_tensor.py
diff --git a/torchao/quantization/prototype/qat/api.py b/torchao/quantization/qat/api.py
similarity index 100%
rename from torchao/quantization/prototype/qat/api.py
rename to torchao/quantization/qat/api.py
diff --git a/torchao/quantization/prototype/qat/embedding.py b/torchao/quantization/qat/embedding.py
similarity index 100%
rename from torchao/quantization/prototype/qat/embedding.py
rename to torchao/quantization/qat/embedding.py
diff --git a/torchao/quantization/prototype/qat/fake_quantizer.py b/torchao/quantization/qat/fake_quantizer.py
similarity index 100%
rename from torchao/quantization/prototype/qat/fake_quantizer.py
rename to torchao/quantization/qat/fake_quantizer.py
diff --git a/torchao/quantization/prototype/qat/images/qat_diagram.png b/torchao/quantization/qat/images/qat_diagram.png
similarity index 100%
rename from torchao/quantization/prototype/qat/images/qat_diagram.png
rename to torchao/quantization/qat/images/qat_diagram.png
diff --git a/torchao/quantization/prototype/qat/linear.py b/torchao/quantization/qat/linear.py
similarity index 100%
rename from torchao/quantization/prototype/qat/linear.py
rename to torchao/quantization/qat/linear.py
diff --git a/torchao/quantization/prototype/qat/utils.py b/torchao/quantization/qat/utils.py
similarity index 97%
rename from torchao/quantization/prototype/qat/utils.py
rename to torchao/quantization/qat/utils.py
index 8f2dd9d13..e2234a255 100644
--- a/torchao/quantization/prototype/qat/utils.py
+++ b/torchao/quantization/qat/utils.py
@@ -46,7 +46,7 @@ def forward(
         zero_point_domain: ZeroPointDomain = ZeroPointDomain.INT,
     ) -> torch.Tensor:
         # avoid circular dependencies
-        from torchao.quantization.prototype.qat.affine_fake_quantized_tensor import (
+        from torchao.quantization.qat.affine_fake_quantized_tensor import (
             AffineFakeQuantizedTensor,
         )
 
@@ -88,7 +88,7 @@ def forward(
         input: torch.Tensor,
     ) -> torch.Tensor:
         # avoid circular dependencies
-        from torchao.quantization.prototype.qat.affine_fake_quantized_tensor import (
+        from torchao.quantization.qat.affine_fake_quantized_tensor import (
             AffineFakeQuantizedTensor,
         )
         assert isinstance(input, AffineFakeQuantizedTensor)
diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py
index 91803fe3f..ba6fe9b2c 100644
--- a/torchao/quantization/quant_api.py
+++ b/torchao/quantization/quant_api.py
@@ -220,7 +220,7 @@ def _replace_with_custom_fn_if_matches_filter(
 
 def _is_linear(mod, *args):
     # avoid circular dependencies
-    from torchao.quantization.prototype.qat.affine_fake_quantized_tensor import (
+    from torchao.quantization.qat.affine_fake_quantized_tensor import (
         AffineFakeQuantizedTensor,
     )