diff --git a/recipes/quantize.py b/recipes/quantize.py
index 3b90b62fe..1b0f65d0c 100644
--- a/recipes/quantize.py
+++ b/recipes/quantize.py
@@ -47,7 +47,7 @@ class QuantizationRecipe:
                 multiple of groupsize.
             `percdamp`: GPTQ stablization hyperparameter, recommended to be .01
 
-    8da4w:
+    8da4w (PyTorch 2.3+):
         torchtune.utils.quantization.Int8DynActInt4WeightQuantizer
         int8 per token dynamic activation with int4 weight only per axis group quantization
         Args:
diff --git a/torchtune/utils/quantization.py b/torchtune/utils/quantization.py
index 5f97dea0f..5486a84b6 100644
--- a/torchtune/utils/quantization.py
+++ b/torchtune/utils/quantization.py
@@ -11,15 +11,14 @@
     apply_weight_only_int8_quant,
     Int4WeightOnlyGPTQQuantizer,
     Int4WeightOnlyQuantizer,
-    Int8DynActInt4WeightQuantizer,
     Quantizer,
 )
+from torchao.quantization.utils import TORCH_VERSION_AFTER_2_3
 
 __all__ = [
     "Int4WeightOnlyQuantizer",
     "Int4WeightOnlyGPTQQuantizer",
     "Int8WeightOnlyQuantizer",
-    "Int8DynActInt4WeightQuantizer",
     "get_quantizer_mode",
 ]
 
@@ -36,10 +35,16 @@ def quantize(
     Int4WeightOnlyQuantizer: "4w",
     Int8WeightOnlyQuantizer: "8w",
     Int4WeightOnlyGPTQQuantizer: "4w-gptq",
-    Int8DynActInt4WeightQuantizer: "8da4w",
 }
 
 
+if TORCH_VERSION_AFTER_2_3:
+    from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer
+
+    __all__.append("Int8DynActInt4WeightQuantizer")
+    _quantizer_to_mode[Int8DynActInt4WeightQuantizer] = "8da4w"
+
+
 def get_quantizer_mode(quantizer: Optional[Callable]) -> Optional[str]:
     """Given a quantizer object, returns a string that specifies the type of quantization e.g.
     4w, which means int4 weight only quantization.