diff --git a/.env.template b/.env.template
index f3370e229..3ff2d9077 100644
--- a/.env.template
+++ b/.env.template
@@ -123,3 +123,8 @@ PROXY_SERVER_URL=https://api.openai.com/v1/chat/completions
 # **    SUMMARY_CONFIG
 #*******************************************************************#
 SUMMARY_CONFIG=FAST
+
+#*******************************************************************#
+# **    MUlti-GPU
+#*******************************************************************#
+NUM_GPUS = 1
diff --git a/pilot/configs/config.py b/pilot/configs/config.py
index 804176357..1c8026df5 100644
--- a/pilot/configs/config.py
+++ b/pilot/configs/config.py
@@ -28,6 +28,8 @@ class Config(metaclass=Singleton):
         self.skip_reprompt = False
         self.temperature = float(os.getenv("TEMPERATURE", 0.7))
 
+        self.NUM_GPUS = int(os.getenv("NUM_GPUS",1))
+
         self.execute_local_commands = (
             os.getenv("EXECUTE_LOCAL_COMMANDS", "False") == "True"
         )
diff --git a/pilot/model/adapter.py b/pilot/model/adapter.py
index 2c1089cec..16179af6b 100644
--- a/pilot/model/adapter.py
+++ b/pilot/model/adapter.py
@@ -73,6 +73,40 @@ class VicunaLLMAdapater(BaseLLMAdaper):
         )
         return model, tokenizer
 
+def auto_configure_device_map(num_gpus):
+    """handling multi gpu calls"""
+    # transformer.word_embeddings occupying 1 floors
+    # transformer.final_layernorm and lm_head occupying 1 floors
+    # transformer.layers occupying 28 floors
+    # Allocate a total of 30 layers to number On gpus cards
+    num_trans_layers = 28
+    per_gpu_layers = 30 / num_gpus
+    #Bugfix: call torch.embedding in Linux and the incoming weight and input are not on the same device, resulting in a RuntimeError
+    #Under Windows, model. device will be set to transformer. word_ Embeddings. device
+    #Under Linux, model. device will be set to lm_ Head.device
+    #When calling chat or stream_ During chat, input_ IDS will be placed on model. device
+    #If transformer. word_ If embeddings. device and model. device are different, it will cause a RuntimeError
+    #Therefore, here we will transform. word_ Embeddings, transformer. final_ Layernorm, lm_ Put all the heads on the first card
+    device_map = {
+        'transformer.embedding.word_embeddings': 0,
+        'transformer.encoder.final_layernorm': 0,
+        'transformer.output_layer': 0,
+        'transformer.rotary_pos_emb': 0,
+        'lm_head': 0
+    }
+
+    used = 2
+    gpu_target = 0
+    for i in range(num_trans_layers):
+        if used >= per_gpu_layers:
+            gpu_target += 1
+            used = 0
+        assert gpu_target < num_gpus
+        device_map[f'transformer.encoder.layers.{i}'] = gpu_target
+        used += 1
+
+    return device_map
+
 
 class ChatGLMAdapater(BaseLLMAdaper):
     """LLM Adatpter for THUDM/chatglm-6b"""
@@ -80,7 +114,7 @@ class ChatGLMAdapater(BaseLLMAdaper):
     def match(self, model_path: str):
         return "chatglm" in model_path
 
-    def loader(self, model_path: str, from_pretrained_kwargs: dict):
+    def loader(self, model_path: str, from_pretrained_kwargs: dict, device_map=None, num_gpus=CFG.NUM_GPUS):
         tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
 
         if DEVICE != "cuda":
@@ -91,11 +125,22 @@ class ChatGLMAdapater(BaseLLMAdaper):
         else:
             model = (
                 AutoModel.from_pretrained(
-                    model_path, trust_remote_code=True, **from_pretrained_kwargs
+                    model_path, trust_remote_code=True,
+                    **from_pretrained_kwargs
                 )
                 .half()
-                .cuda()
+                # .cuda()
             )
+            from accelerate import dispatch_model
+
+            # model = AutoModel.from_pretrained(model_path, trust_remote_code=True,
+            #                                   **from_pretrained_kwargs).half()
+            #
+            if device_map is None:
+                device_map = auto_configure_device_map(num_gpus)
+
+            model = dispatch_model(model, device_map=device_map)
+
             return model, tokenizer