diff --git a/exir/passes/_quant_patterns_and_replacements.py b/exir/passes/_quant_patterns_and_replacements.py
index bf06ce37c5c..e198ff383e9 100644
--- a/exir/passes/_quant_patterns_and_replacements.py
+++ b/exir/passes/_quant_patterns_and_replacements.py
@@ -46,7 +46,7 @@
 )
 
 
-def embedding_byte_weight_checks(weight, weight_scales, weight_zero_points):
+def embedding_weight_checks(weight, weight_scales, weight_zero_points):
     assert weight.dtype in [
         torch.int8,
         torch.uint8,
@@ -86,7 +86,7 @@ def embedding_byte(
     weight_quant_max: int,
     indices: torch.Tensor,
 ) -> torch.Tensor:
-    embedding_byte_weight_checks(weight, weight_scales, weight_zero_points)
+    embedding_weight_checks(weight, weight_scales, weight_zero_points)
     group_size = weight.size(1) // (
         weight_scales.size(1) if weight_scales.dim() == 2 else 1
     )
@@ -133,7 +133,7 @@ def embedding_byte_dtype(
     indices: torch.Tensor,
     dtype: Optional[torch.dtype],
 ) -> torch.Tensor:
-    embedding_byte_weight_checks(weight, weight_scales, weight_zero_points)
+    embedding_weight_checks(weight, weight_scales, weight_zero_points)
     group_size = weight.size(1) // (
         weight_scales.size(1) if weight_scales.dim() == 2 else 1
     )
@@ -172,6 +172,134 @@ def embedding_byte_dtype_out_meta(
     )
 
 
+quantized_decomposed_lib.define(
+    "embedding_4bit(Tensor weight, Tensor weight_scales, Tensor? weight_zero_points, "
+    "int weight_quant_min, int weight_quant_max, Tensor indices) -> Tensor",
+)
+
+quantized_decomposed_lib.define(
+    "embedding_4bit.dtype(Tensor weight, Tensor weight_scales, Tensor? weight_zero_points, "
+    "int weight_quant_min, int weight_quant_max, Tensor indices, ScalarType? dtype=None) -> Tensor",
+)
+
+quantized_decomposed_lib.define(
+    "embedding_4bit.out(Tensor weight, Tensor weight_scales, Tensor? weight_zero_points, "
+    "int weight_quant_min, int weight_quant_max, Tensor indices, *, Tensor(a!) out) -> Tensor(a!)",
+)
+
+quantized_decomposed_lib.define(
+    "embedding_4bit.dtype_out(Tensor weight, Tensor weight_scales, Tensor? weight_zero_points, "
+    "int weight_quant_min, int weight_quant_max, Tensor indices, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)",
+)
+
+
+@impl(quantized_decomposed_lib, "embedding_4bit", "CompositeExplicitAutograd")
+def embedding_4bit(
+    weight: torch.Tensor,
+    weight_scales: torch.Tensor,
+    weight_zero_points: Optional[torch.Tensor],
+    weight_quant_min: int,
+    weight_quant_max: int,
+    indices: torch.Tensor,
+) -> torch.Tensor:
+    embedding_weight_checks(weight, weight_scales, weight_zero_points)
+    group_size = (2 * weight.size(1)) // (
+        weight_scales.size(1) if weight_scales.dim() == 2 else 1
+    )
+    weight_even = weight.div(16, rounding_mode="trunc")
+    weight_odd = weight.remainder(16)
+    weight_unpacked = torch.stack((weight_even, weight_odd), dim=-1)
+    weight = weight_unpacked.view(weight.shape[0], -1)
+    weight = weight.view(torch.int8).add(-8)
+
+    weight = torch.ops.quantized_decomposed.dequantize_per_channel_group.default(
+        weight,
+        weight_scales,
+        weight_zero_points,
+        weight_quant_min,
+        weight_quant_max,
+        weight.dtype,
+        group_size,
+        weight_scales.dtype,
+    )
+    return torch.ops.aten.embedding.default(weight, indices)
+
+
+@impl_abstract("quantized_decomposed::embedding_4bit.out")
+def embedding_4bit_out_meta(
+    weight: torch.Tensor,
+    weight_scales: torch.Tensor,
+    weight_zero_points: Optional[torch.Tensor],
+    weight_quant_min: int,
+    weight_quant_max: int,
+    indices: torch.Tensor,
+    out: torch.Tensor,
+) -> torch.Tensor:
+    return embedding_4bit(
+        weight,
+        weight_scales,
+        weight_zero_points,
+        weight_quant_min,
+        weight_quant_max,
+        indices,
+    )
+
+
+@impl(quantized_decomposed_lib, "embedding_4bit.dtype", "CompositeExplicitAutograd")
+def embedding_4bit_dtype(
+    weight: torch.Tensor,
+    weight_scales: torch.Tensor,
+    weight_zero_points: Optional[torch.Tensor],
+    weight_quant_min: int,
+    weight_quant_max: int,
+    indices: torch.Tensor,
+    dtype: Optional[torch.dtype],
+) -> torch.Tensor:
+    embedding_weight_checks(weight, weight_scales, weight_zero_points)
+    group_size = (2 * weight.size(1)) // (
+        weight_scales.size(1) if weight_scales.dim() == 2 else 1
+    )
+    weight_even = weight.div(16, rounding_mode="trunc")
+    weight_odd = weight.remainder(16)
+    weight_unpacked = torch.stack((weight_even, weight_odd), dim=-1)
+    weight = weight_unpacked.view(weight.shape[0], -1)
+    weight = weight.view(torch.int8).add(-8)
+
+    weight = torch.ops.quantized_decomposed.dequantize_per_channel_group.default(
+        weight,
+        weight_scales,
+        weight_zero_points,
+        weight_quant_min,
+        weight_quant_max,
+        weight.dtype,
+        group_size,
+        dtype,
+    )
+    return torch.ops.aten.embedding.default(weight, indices)
+
+
+@impl_abstract("quantized_decomposed::embedding_4bit.dtype_out")
+def embedding_4bit_dtype_out_meta(
+    weight: torch.Tensor,
+    weight_scales: torch.Tensor,
+    weight_zero_points: Optional[torch.Tensor],
+    weight_quant_min: int,
+    weight_quant_max: int,
+    indices: torch.Tensor,
+    dtype: Optional[torch.dtype],
+    out: torch.Tensor,
+) -> torch.Tensor:
+    return embedding_4bit_dtype(
+        weight,
+        weight_scales,
+        weight_zero_points,
+        weight_quant_min,
+        weight_quant_max,
+        indices,
+        dtype,
+    )
+
+
 quantized_decomposed_lib.define(
     "mixed_mm(Tensor input, Tensor weight, Tensor weight_scales, Tensor? weight_zero_points) -> Tensor",
 )