diff --git a/modules/GPTQ_loader.py b/modules/GPTQ_loader.py
index 1fdd23c0..2a9039a3 100644
--- a/modules/GPTQ_loader.py
+++ b/modules/GPTQ_loader.py
@@ -14,7 +14,7 @@ import llama_inference_offload
 from quant import make_quant
 from modelutils import find_layers
 
-def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=['lm_head']):
+def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=['lm_head'], kernel_switch_threshold=128):
     config = AutoConfig.from_pretrained(model)
     def noop(*args, **kwargs):
         pass
@@ -32,7 +32,7 @@ def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exc
     for name in exclude_layers:
         if name in layers:
             del layers[name]
-    make_quant(model, layers, wbits, groupsize, faster=faster_kernel)
+    make_quant(model, layers, wbits, groupsize, faster=faster_kernel, kernel_switch_threshold=kernel_switch_threshold)
 
     del layers
     
@@ -109,7 +109,8 @@ def load_quantized(model_name):
     if shared.args.pre_layer:
         model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, shared.args.pre_layer)
     else:
-        model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize)
+        threshold = False if model_type == 'gptj' else 128
+        model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, kernel_switch_threshold=threshold)
 
         # accelerate offload (doesn't work properly)
         if shared.args.gpu_memory: