diff --git a/apps/language_models/scripts/vicuna.py b/apps/language_models/scripts/vicuna.py
index de2ec698..a661be24 100644
--- a/apps/language_models/scripts/vicuna.py
+++ b/apps/language_models/scripts/vicuna.py
@@ -535,6 +535,11 @@ class ShardedVicuna(VicunaBase):
             self.dir_path.mkdir(parents=True, exist_ok=True)
         self.shark_model = self.compile(device=device)
 
+    def check_all_artifacts_present(self):
+        file_list = [f"{i}_full" for i in range(self.n_layers_dict[self.model_name])] + ["norm", "embedding", "lmhead"]
+        file_exists_list = [Path(f"{self.dir_name}/{x}.vmfb").exists() or Path(f"{self.dir_name}/{x}.mlir").exists() for x in file_list]
+        return all(file_exists_list)
+
     def get_tokenizer(self):
         kwargs = {}
         if "llama2" in self.model_name:
@@ -1321,34 +1326,40 @@ class ShardedVicuna(VicunaBase):
             )
 
         if self.precision in ["int4", "int8"]:
-            from brevitas_examples.common.generative.quantize import (
-                quantize_model,
-            )
-            from brevitas_examples.llm.llm_quant.run_utils import (
-                get_model_impl,
-            )
 
-            print("Applying weight quantization..")
-            weight_bit_width = 4 if self.precision == "int4" else 8
-            quantize_model(
-                get_model_impl(vicuna_model).layers,
-                dtype=torch.float32,
-                weight_quant_type="asym",
-                weight_bit_width=weight_bit_width,
-                weight_param_method="stats",
-                weight_scale_precision="float_scale",
-                weight_quant_granularity="per_group",
-                weight_group_size=self.weight_group_size,
-                quantize_weight_zero_point=False,
-                input_bit_width=None,
-                input_scale_type="float",
-                input_param_method="stats",
-                input_quant_type="asym",
-                input_quant_granularity="per_tensor",
-                quantize_input_zero_point=False,
-                seqlen=2048,
-            )
-            print("Weight quantization applied.")
+            if not self.check_all_artifacts_present():
+                print("Applying weight quantization..")
+                from brevitas_examples.common.generative.quantize import (
+                    quantize_model,
+                )
+                from brevitas_examples.llm.llm_quant.run_utils import (
+                    get_model_impl,
+                )
+                weight_bit_width = 4 if self.precision == "int4" else 8
+
+                quantize_model(
+                    get_model_impl(vicuna_model).layers,
+                    dtype=torch.float32,
+                    weight_quant_type="asym",
+                    weight_bit_width=weight_bit_width,
+                    weight_param_method="stats",
+                    weight_scale_precision="float_scale",
+                    weight_quant_granularity="per_group",
+                    weight_group_size=self.weight_group_size,
+                    quantize_weight_zero_point=False,
+                    input_bit_width=None,
+                    input_scale_type="float",
+                    input_param_method="stats",
+                    input_quant_type="asym",
+                    input_quant_granularity="per_tensor",
+                    quantize_input_zero_point=False,
+                    seqlen=2048,
+                )
+
+                print("Weight quantization applied.")
+
+            else:
+                print("Skipping quantization, as all required artifacts are present")
 
         placeholder_pkv_segment = tuple(
             (