diff --git a/aiter/configs/tuned_gemm.csv b/aiter/configs/bf16_tuned_gemm.csv
similarity index 100%
rename from aiter/configs/tuned_gemm.csv
rename to aiter/configs/bf16_tuned_gemm.csv
diff --git a/aiter/configs/untuned_gemm.csv b/aiter/configs/bf16_untuned_gemm.csv
similarity index 100%
rename from aiter/configs/untuned_gemm.csv
rename to aiter/configs/bf16_untuned_gemm.csv
diff --git a/aiter/jit/core.py b/aiter/jit/core.py
index 068a17d687..42e35f1529 100644
--- a/aiter/jit/core.py
+++ b/aiter/jit/core.py
@@ -68,49 +68,8 @@ def mp_lock(
 AITER_LOG_MORE = int(os.getenv("AITER_LOG_MORE", 0))
 AITER_LOG_TUNED_CONFIG = int(os.getenv("AITER_LOG_TUNED_CONFIG", 0))
 
-# config_env start here
-AITER_CONFIG_GEMM_A4W4 = os.getenv(
-    "AITER_CONFIG_GEMM_A4W4",
-    f"{AITER_ROOT_DIR}/aiter/configs/a4w4_blockscale_tuned_gemm.csv",
-)
-AITER_CONFIG_GEMM_A8W8 = os.getenv(
-    "AITER_CONFIG_GEMM_A8W8",
-    f"{AITER_ROOT_DIR}/aiter/configs/a8w8_tuned_gemm.csv",
-)
-AITER_CONFIG_GEMM_A8W8_BPRESHUFFLE = os.getenv(
-    "AITER_CONFIG_GEMM_A8W8_BPRESHUFFLE",
-    f"{AITER_ROOT_DIR}/aiter/configs/a8w8_bpreshuffle_tuned_gemm.csv",
-)
-AITER_CONFIG_GEMM_A8W8_BLOCKSCALE = os.getenv(
-    "AITER_CONFIG_GEMM_A8W8_BLOCKSCALE",
-    f"{AITER_ROOT_DIR}/aiter/configs/a8w8_blockscale_tuned_gemm.csv",
-)
-AITER_CONFIG_FMOE = os.getenv(
-    "AITER_CONFIG_FMOE",
-    f"{AITER_ROOT_DIR}/aiter/configs/tuned_fmoe.csv",
-)
-
-AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_BPRESHUFFLE = os.getenv(
-    "AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_BPRESHUFFLE",
-    f"{AITER_ROOT_DIR}/aiter/configs/a8w8_blockscale_bpreshuffle_tuned_gemm.csv",
-)
-
-AITER_CONFIG_A8W8_BATCHED_GEMM = os.getenv(
-    "AITER_CONFIG_A8W8_BATCHED_GEMM",
-    f"{AITER_ROOT_DIR}/aiter/configs/a8w8_tuned_batched_gemm.csv",
-)
-
-AITER_CONFIG_BF16_BATCHED_GEMM = os.getenv(
-    "AITER_CONFIG_BATCHED_GEMM_BF16",
-    f"{AITER_ROOT_DIR}/aiter/configs/bf16_tuned_batched_gemm.csv",
-)
-
-AITER_CONFIG_GEMM_BF16 = os.getenv(
-    "AITER_CONFIG_GEMM_BF16",
-    f"{AITER_ROOT_DIR}/aiter/configs/tuned_gemm.csv",
-)
-
 
+# config_env start here
 def update_config_files(file_path: str, merge_name: str):
     path_list = file_path.split(os.pathsep) if file_path else []
     if len(path_list) <= 1:
@@ -133,38 +92,84 @@ def update_config_files(file_path: str, merge_name: str):
         else:
             print(f"path {i+1}: {path} (not exist)")
     merge_df = pd.concat(df_list, ignore_index=True) if df_list else pd.DataFrame()
-    merge_df = merge_df.drop_duplicates(keep="last")
+    ## get keys from untuned file to drop_duplicates
+    untuned_name = (
+        re.sub(r"(?:_)?tuned$", r"\1untuned", merge_name)
+        if re.search(r"(?:_)?tuned$", merge_name)
+        else merge_name.replace("tuned", "untuned")
+    )
+    untuned_path = f"{AITER_ROOT_DIR}/aiter/configs/{untuned_name}.csv"
+    if os.path.exists(untuned_path):
+        untunedf = pd.read_csv(untuned_path)
+        keys = untunedf.columns
+        merge_df = merge_df.drop_duplicates(subset=keys, keep="last")
+    else:
+        logger.warning(
+            f"Untuned config file not found: {untuned_path}. Using all columns for deduplication."
+        )
     new_file_path = f"/tmp/{merge_name}.csv"
     merge_df.to_csv(new_file_path, index=False)
     return new_file_path
 
 
-AITER_CONFIG_GEMM_A4W4_FILE = update_config_files(
-    AITER_CONFIG_GEMM_A4W4, "a4w4_blockscale_tuned_gemm"
+def get_config_file(env_name, tuned_file_name):
+    config_env_file = os.getenv(env_name)
+    default_file = f"{AITER_ROOT_DIR}/aiter/configs/{tuned_file_name}.csv"
+    from pathlib import Path
+
+    if not config_env_file:
+        model_config_dir = Path(f"{AITER_ROOT_DIR}/aiter/configs/model_configs/")
+        op_tuned_file_list = [
+            p
+            for p in model_config_dir.glob(f"*{tuned_file_name}*")
+            if (p.is_file() and "untuned" not in str(p))
+        ]
+
+        if not op_tuned_file_list:
+            config_file = default_file
+        else:
+            tuned_files = ":".join(str(p) for p in op_tuned_file_list)
+            tuned_files = default_file + ":" + tuned_files
+            print(f"merge tuned file under model_configs/ and configs/")
+            config_file = update_config_files(tuned_files, tuned_file_name)
+    else:
+        config_file = update_config_files(config_env_file, tuned_file_name)
+        print(f"get {env_name} from environment ", config_file)
+    return config_file
+
+
+AITER_CONFIG_GEMM_A4W4_FILE = get_config_file(
+    "AITER_CONFIG_GEMM_A4W4", "a4w4_blockscale_tuned_gemm"
 )
-AITER_CONFIG_GEMM_A8W8_FILE = update_config_files(
-    AITER_CONFIG_GEMM_A8W8, "a8w8_tuned_gemm"
+
+AITER_CONFIG_GEMM_A8W8_FILE = get_config_file(
+    "AITER_CONFIG_GEMM_A8W8", "a8w8_tuned_gemm"
 )
-AITER_CONFIG_GEMM_A8W8_BPRESHUFFLE_FILE = update_config_files(
-    AITER_CONFIG_GEMM_A8W8_BPRESHUFFLE, "a8w8_bpreshuffle_tuned_gemm"
+AITER_CONFIG_GEMM_A8W8_BPRESHUFFLE_FILE = get_config_file(
+    "AITER_CONFIG_GEMM_A8W8_BPRESHUFFLE", "a8w8_bpreshuffle_tuned_gemm"
 )
-AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_FILE = update_config_files(
-    AITER_CONFIG_GEMM_A8W8_BLOCKSCALE, "a8w8_blockscale_tuned_gemm"
+AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_FILE = get_config_file(
+    "AITER_CONFIG_GEMM_A8W8_BLOCKSCALE", "a8w8_blockscale_tuned_gemm"
 )
-AITER_CONFIG_FMOE_FILE = update_config_files(AITER_CONFIG_FMOE, "tuned_fmoe")
-AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_BPRESHUFFLE_FILE = update_config_files(
-    AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_BPRESHUFFLE,
+AITER_CONFIG_FMOE_FILE = get_config_file("AITER_CONFIG_FMOE", "tuned_fmoe")
+
+AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_BPRESHUFFLE_FILE = get_config_file(
+    "AITER_CONFIG_GEMM_A8W8_BLOCKSCALE_BPRESHUFFLE",
     "a8w8_blockscale_bpreshuffle_tuned_gemm",
 )
-AITER_CONFIG_A8W8_BATCHED_GEMM_FILE = update_config_files(
-    AITER_CONFIG_A8W8_BATCHED_GEMM, "a8w8_tuned_batched_gemm"
+
+AITER_CONFIG_A8W8_BATCHED_GEMM_FILE = get_config_file(
+    "AITER_CONFIG_A8W8_BATCHED_GEMM", "a8w8_tuned_batched_gemm"
 )
-AITER_CONFIG_BF16_BATCHED_GEMM_FILE = update_config_files(
-    AITER_CONFIG_BF16_BATCHED_GEMM, "bf16_tuned_batched_gemm"
+
+AITER_CONFIG_BF16_BATCHED_GEMM_FILE = get_config_file(
+    "AITER_CONFIG_BATCHED_GEMM_BF16", "bf16_tuned_batched_gemm"
 )
-AITER_CONFIG_GEMM_BF16_FILE = update_config_files(
-    AITER_CONFIG_GEMM_BF16, "bf16_tuned_gemm"
+
+AITER_CONFIG_GEMM_BF16_FILE = get_config_file(
+    "AITER_CONFIG_GEMM_BF16", "bf16_tuned_gemm"
 )
+
 # config_env end here
 
 find_aiter = importlib.util.find_spec("aiter")
diff --git a/aiter/tuned_gemm.py b/aiter/tuned_gemm.py
index d054d37090..fe1818506c 100644
--- a/aiter/tuned_gemm.py
+++ b/aiter/tuned_gemm.py
@@ -310,7 +310,7 @@ class TunedGemm:
     def __init__(self):
         self.extensions_created = False
         self.save_gemm = int(os.environ.get("AITER_TUNE_GEMM", 0))
-        self.untune_path = f"{this_dir}/configs/untuned_gemm.csv"
+        self.untune_path = f"{this_dir}/configs/bf16_untuned_gemm.csv"
         self.tune_path = AITER_CONFIG_GEMM_BF16_FILE
 
     def mm(
diff --git a/gradlib/README.md b/gradlib/README.md
index 710c6c732c..a92cf12195 100644
--- a/gradlib/README.md
+++ b/gradlib/README.md
@@ -20,9 +20,9 @@ By gradlib, we can confirm the parameter of GEMMs with best performance in the s
     AITER_TUNE_GEMM=1 python {workload_tests}
    `
 
-    then shapes will be captured in aiter/configs/untuned_gemm.csv
-2. to tune GEMMs in aiter/configs/untuned_gemm.csv,
-    You can find the results of this tuning in `aiter/configs/tuned_gemm.csv`.
+    then shapes will be captured in aiter/configs/bf16_untuned_gemm.csv
+2. to tune GEMMs in aiter/configs/bf16_untuned_gemm.csv,
+    You can find the results of this tuning in `aiter/configs/bf16_tuned_gemm.csv`.
     |**cu_num**|**M**|**N**|**K**|**bias**|   **dtype**  | **outdtype** |**scaleAB**|**libtype**|**solidx**|**splitK**|**soltimes**|**kernelName**|**tflops**|**bw**|
     |----------|-----|-----|-----|--------|--------------|--------------|-----------|-----------|----------|----------|------------|--------------|----------|------|
     |80        |128  |1536 |7168 |  False |torch.bfloat16|torch.float32 | False     | hipblast  |667788    |0         | 10.6       | xxxxxxx      |  xx      | xx   |
@@ -37,6 +37,6 @@ By gradlib, we can confirm the parameter of GEMMs with best performance in the s
    run
    
    ` 
-    python3 gradlib/gradlib/gemm_tuner.py --tuned_file aiter/configs/tuned_gemm.csv  --input_file aiter/configs/untuned_gemm.csv
+    python3 gradlib/gradlib/gemm_tuner.py --tuned_file aiter/configs/bf16_tuned_gemm.csv  --input_file aiter/configs/bf16_untuned_gemm.csv
    `
 3. then run your test as normal~