diff --git a/src/compressed_tensors/compressors/quantized_compressors/nvfp4_quantized.py b/src/compressed_tensors/compressors/quantized_compressors/nvfp4_quantized.py
index 5f348e91..419d47c6 100644
--- a/src/compressed_tensors/compressors/quantized_compressors/nvfp4_quantized.py
+++ b/src/compressed_tensors/compressors/quantized_compressors/nvfp4_quantized.py
@@ -71,7 +71,6 @@ def compress_weight(
         zero_point: Optional[torch.Tensor] = None,
         g_idx: Optional[torch.Tensor] = None,
     ) -> Dict[str, torch.Tensor]:
-
         quantized_weight = quantize(
             x=weight,
             scale=scale,
@@ -91,7 +90,6 @@ def decompress_weight(
         compressed_data: Dict[str, Tensor],
         quantization_args: Optional[QuantizationArgs] = None,
     ) -> torch.Tensor:
-
         weight = compressed_data["weight_packed"]
         scale = compressed_data["weight_scale"]
         global_scale = compressed_data["weight_global_scale"]
@@ -105,6 +103,7 @@ def decompress_weight(
         return decompressed_weight
 
 
+@torch.compile(fullgraph=True, dynamic=True)
 def pack_fp4_to_uint8(x: torch.Tensor) -> torch.Tensor:
     """
     Packs a tensor with values in the fp4 range into uint8.
@@ -127,12 +126,11 @@ def pack_fp4_to_uint8(x: torch.Tensor) -> torch.Tensor:
 
     # Find closest valid FP4 value index for each element
     abs_x = torch.abs(x)
-    abs_indices = torch.zeros_like(abs_x, dtype=torch.long)
-    for i, val in enumerate(kE2M1):
-        abs_indices = torch.where(torch.isclose(abs_x, val), i, abs_indices)
+    abs_diff_x = torch.abs(abs_x.unsqueeze(-1) - kE2M1)  # [m, n, 8]
+    abs_indices = torch.argmin(abs_diff_x, dim=-1)  # [m, n]
 
     # Apply sign bit (bit 3) to get final 4-bit representation
-    indices = abs_indices + (torch.signbit(x) << 3).to(torch.long)
+    indices = abs_indices + (torch.signbit(x).to(torch.long) << 3)
 
     # Reshape to prepare for packing pairs of values
     indices = indices.reshape(-1)
@@ -154,7 +152,9 @@ def pack_fp4_to_uint8(x: torch.Tensor) -> torch.Tensor:
     [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0], dtype=torch.float32
 )
 
+
 # reference: : https://github.com/vllm-project/vllm/pull/16362
+@torch.compile(fullgraph=True, dynamic=True)
 def unpack_fp4_from_uint8(
     a: torch.Tensor, m: int, n: int, dtype: Optional[torch.dtype] = torch.bfloat16
 ) -> torch.Tensor: