GLM-5/GLM-5.1

1 Introduction

This document applies to both GLM-5 and GLM-5.1. Unless otherwise specified, all descriptions, configurations, and deployment procedures for GLM-5 in this document also apply to GLM-5.1. For brevity, GLM-5 is used hereafter as a unified reference to both GLM-5 and GLM-5.1.

GLM-5 uses a Mixture-of-Experts (MoE) architecture and targets complex systems engineering and long-horizon agentic tasks.

The GLM-5 model is first supported in vllm-ascend:v0.17.0rc1, and all v0.17.0rc1 and later versions can run stably. To use the latest features (e.g., PD separation, MTP), it is recommended to use the latest release candidate or official version. The version of transformers need to be upgraded to 5.2.0 or later versions.

This document will show the main verification steps of the model, including supported features, feature configuration, environment preparation, single-node and multi-node deployment, accuracy and performance evaluation.

2 Supported Features

Refer to supported features to get the model's supported feature matrix.

Refer to feature guide to get the feature's configuration.

3 Prerequisites

3.1 Model Weight

• GLM-5(BF16 version): Download model weight.
• GLM-5-w4a8(Quantized version): Download model weight.
• GLM-5-w8a8(Quantized version): Download model weight.
• GLM-5.1(BF16 version): Download model weight.
• GLM-5.1-w4a8(Quantized version): Download model weight.
• GLM-5.1-w8a8(Quantized version): Download model weight.

It is recommended to download the model weight to the shared directory of multiple nodes, such as /root/.cache/

3.2 Verify Multi-node Communication (Optional)

If multi-node deployment is required, please follow the Verify Multi-node Communication Environment guide for communication verification.

4 Installation

4.1 Docker Image Installation

You can use our official docker image to run GLM-5/5.1 directly.

A3 seriesA2 series

Start the docker image on your each node.

export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1-a3
export NAME=vllm-ascend

# Run the container using the defined variables
# Note: If you are running bridge network with docker, please expose available ports for multiple nodes communication in advance
docker run --rm \
--name $NAME \
--net=host \
--shm-size=1g \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci8 \
--device /dev/davinci9 \
--device /dev/davinci10 \
--device /dev/davinci11 \
--device /dev/davinci12 \
--device /dev/davinci13 \
--device /dev/davinci14 \
--device /dev/davinci15 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-it $IMAGE bash

Start the docker image on your each node.

export IMAGE=quay.io/ascend/vllm-ascend:v0.22.1rc1
docker run --rm \
    --name vllm-ascend \
    --shm-size=1g \
    --net=host \
    --device /dev/davinci0 \
    --device /dev/davinci1 \
    --device /dev/davinci2 \
    --device /dev/davinci3 \
    --device /dev/davinci4 \
    --device /dev/davinci5 \
    --device /dev/davinci6 \
    --device /dev/davinci7 \
    --device /dev/davinci_manager \
    --device /dev/devmm_svm \
    --device /dev/hisi_hdc \
    -v /usr/local/dcmi:/usr/local/dcmi \
    -v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
    -v /etc/ascend_install.info:/etc/ascend_install.info \
    -v /root/.cache:/root/.cache \
    -it $IMAGE bash

If you want to deploy multi-node environment, you need to set up environment on each node.

To verify the successful installation of the environment, please refer to installation.

4.2 Source Code Installation

In addition, if you don't want to use the docker image as above, you can also build all from source:

• Install vllm-ascend from source, refer to installation.

If you want to deploy multi-node environment, you need to set up environment on each node.

5 Online Service Deployment

5.1 Single-Node Online Deployment

A3 seriesA2 series

• Quantized model glm-5-w4a8 and glm-5.1-w4a8 can be deployed on 1 Atlas 800 A3 (64G × 16) .

Run the following script to execute online inference.

Common Issues Tip: If you encounter issues, Refer to FAQs.

# The version of transformers needs to be upgraded to 5.2.0.
# pip install transformers==5.2.0 --upgrade

export HCCL_OP_EXPANSION_MODE="AIV"
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_BALANCE_SCHEDULING=1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w4a8 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 1 \
--tensor-parallel-size 16 \
--enable-expert-parallel \
--seed 1024 \
--served-model-name glm-5 \
--max-num-seqs 8 \
--max-model-len 200000 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--quantization ascend \
--enable-chunked-prefill \
--enable-prefix-caching \
--additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true}' \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}' 

• Quantized model glm-5-w8a8 and glm-5.1-w8a8 can be deployed on 1 Atlas 800 A3 (64G × 16) .

Run the following script to execute online inference.

export HCCL_OP_EXPANSION_MODE="AIV"
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_BALANCE_SCHEDULING=1
export VLLM_ASCEND_ENABLE_MLAPO=1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w8a8 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 1 \
--tensor-parallel-size 16 \
--enable-expert-parallel \
--seed 1024 \
--served-model-name glm-5 \
--max-num-seqs 8 \
--max-model-len 40960 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--quantization ascend \
--enable-chunked-prefill \
--enable-prefix-caching \
--additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true}' \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}' 

• Quantized model glm-5-w4a8 can be deployed on 1 Atlas 800 A2 (64G × 8) .

Run the following script to execute online inference.

Common Issues Tip: If you encounter issues, Refer to FAQs.

export HCCL_OP_EXPANSION_MODE="AIV"
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_BALANCE_SCHEDULING=1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w4a8 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 1 \
--tensor-parallel-size 8 \
--enable-expert-parallel \
--seed 1024 \
--served-model-name glm-5 \
--max-num-seqs 2 \
--max-model-len 32768 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--quantization ascend \
--enable-chunked-prefill \
--enable-prefix-caching \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

Notice: The parameters are explained as follows:

• For single-node deployment, we recommend using dp1tp16 and turn off expert parallel in low-latency scenarios.

5.2 Multi-node Deployment

If you want to deploy multi-node environment, you need to verify multi-node communication according to verify multi-node communication environment.

Common Issues Tip: If you encounter issues, Refer to FAQs.

A3 seriesA2 series

• glm-5-bf16 and glm-5.1-bf16: require at least 2 Atlas 800 A3 (64G × 16).

Run the following scripts on two nodes respectively.

node 0

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"

# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"

export HCCL_OP_EXPANSION_MODE="AIV"

export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export VLLM_ASCEND_BALANCE_SCHEDULING=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-bf16 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 12890 \
--tensor-parallel-size 16 \
--seed 1024 \
--served-model-name glm-5 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 8192 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

node 1

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"

# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"

export HCCL_OP_EXPANSION_MODE="AIV"

export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export VLLM_ASCEND_BALANCE_SCHEDULING=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-bf16 \
--host 0.0.0.0 \
--port 8077 \
--headless \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-start-rank 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 12890 \
--tensor-parallel-size 16 \
--seed 1024 \
--served-model-name glm-5 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 8192 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

Run the following scripts on two nodes respectively.

node 0

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"

# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxx"

export HCCL_OP_EXPANSION_MODE="AIV"

export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export VLLM_ASCEND_BALANCE_SCHEDULING=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w4a8 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 8 \
--quantization ascend \
--seed 1024 \
--served-model-name glm-5 \
--enable-expert-parallel \
--max-num-seqs 2 \
--max-model-len 131072 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

node 1

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"

# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxx"

export HCCL_OP_EXPANSION_MODE="AIV"

export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export VLLM_ASCEND_BALANCE_SCHEDULING=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w4a8 \
--host 0.0.0.0 \
--port 8077 \
--headless \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-start-rank 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 8 \
--quantization ascend \
--seed 1024 \
--served-model-name glm-5 \
--enable-expert-parallel \
--max-num-seqs 2 \
--max-model-len 131072 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

• For bf16 weight, use this script on each node to enable Multi Token Prediction (MTP).

python adjust_weight.py "path_of_bf16_weight"

# adjust_weight.py
from safetensors.torch import safe_open, save_file
import torch
import json
import os
import sys

target_keys = ["model.embed_tokens.weight", "lm_head.weight"]

def get_tensor_info(file_path):
   with safe_open(file_path, framework="pt", device="cpu") as f:
         tensor_names = f.keys()
         tensor_dict = {}
         for name in tensor_names:
            tensor = f.get_tensor(name)
            tensor_dict[name] = tensor
         return tensor_dict

if __name__ == "__main__":
   directory_path = sys.argv[1]
   json_name = "model.safetensors.index.json"
   json_path = os.path.join(directory_path, json_name)
   with open(json_path, 'r', encoding='utf-8') as f:
         json_data = json.load(f)
   weight_map = json_data.get('weight_map', {})
   file_list = []
   for key in target_keys:
         safetensor_file = weight_map.get(key)
         file_list.append(directory_path + safetensor_file)

   new_dict = {}
   for file_path in file_list:
         tensor_dict = get_tensor_info(file_path)
         for key in target_keys:
            if key in tensor_dict:
               if key == "model.embed_tokens.weight":
                     new_key = "model.layers.78.embed_tokens.weight"
               elif key == "lm_head.weight":
                     new_key = "model.layers.78.shared_head.head.weight"
               new_dict[new_key] = tensor_dict[key]

   new_file_name = os.path.join(directory_path, "mtp-others.safetensors")
   new_keys = ["model.layers.78.embed_tokens.weight", "model.layers.78.shared_head.head.weight"]
   save_file(tensors=new_dict, filename=new_file_name)
   for key in new_keys:
         json_data["weight_map"][key] = "mtp-others.safetensors"
   with open(json_path, 'w', encoding='utf-8') as f:
         json.dump(json_data, f, indent=2)

A3 series

• glm-5-w8a8: require 2 Atlas 800 A3 (64G × 16).

Run the following scripts on two nodes respectively.

node 0

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"

# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"

export HCCL_OP_EXPANSION_MODE="AIV"

export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_BALANCE_SCHEDULING=1
export VLLM_ASCEND_ENABLE_MLAPO=1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w8a8 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 12890 \
--tensor-parallel-size 16 \
--seed 1024 \
--served-model-name glm-5 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 200000 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--quantization ascend \
--enable-chunked-prefill \
--enable-prefix-caching \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

node 1

# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip of the current node
nic_name="xxx"
local_ip="xxx"

# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
node0_ip="xxxx"

export HCCL_OP_EXPANSION_MODE="AIV"

export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=200
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_BALANCE_SCHEDULING=1
export VLLM_ASCEND_ENABLE_MLAPO=1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w8a8 \
--host 0.0.0.0 \
--port 8077 \
--headless \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-start-rank 1 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 12890 \
--tensor-parallel-size 16 \
--seed 1024 \
--served-model-name glm-5 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 200000 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--quantization ascend \
--enable-chunked-prefill \
--enable-prefix-caching \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true}' \
--speculative-config '{"num_speculative_tokens": 3, "method": "deepseek_mtp"}'

5.3 Prefill-Decode Disaggregation

We'd like to show the deployment guide of GLM-5 on multi-node environment with 1P1D for better performance. Prefill-Decode Disaggregation refers to the separation of the prefill stage and the decode stage across different nodes to improve throughput and latency.

Before you start, please

1. prepare the script launch_online_dp.py on each node:

   import argparse
   import multiprocessing
   import os
   import subprocess
   import sys
   
   def parse_args():
       parser = argparse.ArgumentParser()
       parser.add_argument(
           "--dp-size",
           type=int,
           required=True,
           help="Data parallel size."
       )
       parser.add_argument(
           "--tp-size",
           type=int,
           default=1,
           help="Tensor parallel size."
       )
       parser.add_argument(
           "--dp-size-local",
           type=int,
           default=-1,
           help="Local data parallel size."
       )
       parser.add_argument(
           "--dp-rank-start",
           type=int,
           default=0,
           help="Starting rank for data parallel."
       )
       parser.add_argument(
           "--dp-address",
           type=str,
           required=True,
           help="IP address for data parallel master node."
       )
       parser.add_argument(
           "--dp-rpc-port",
           type=str,
           default=12345,
           help="Port for data parallel master node."
       )
       parser.add_argument(
           "--vllm-start-port",
           type=int,
           default=9000,
           help="Starting port for the engine."
       )
       return parser.parse_args()
   
   args = parse_args()
   dp_size = args.dp_size
   tp_size = args.tp_size
   dp_size_local = args.dp_size_local
   if dp_size_local == -1:
       dp_size_local = dp_size
   dp_rank_start = args.dp_rank_start
   dp_address = args.dp_address
   dp_rpc_port = args.dp_rpc_port
   vllm_start_port = args.vllm_start_port
   
   def run_command(visible_devices, dp_rank, vllm_engine_port):
       command = [
           "bash",
           "./run_dp_template.sh",
           visible_devices,
           str(vllm_engine_port),
           str(dp_size),
           str(dp_rank),
           dp_address,
           dp_rpc_port,
           str(tp_size),
       ]
       subprocess.run(command, check=True)
   
   if __name__ == "__main__":
       template_path = "./run_dp_template.sh"
       if not os.path.exists(template_path):
           print(f"Template file {template_path} does not exist.")
           sys.exit(1)
   
       processes = []
       num_cards = dp_size_local * tp_size
       for i in range(dp_size_local):
           dp_rank = dp_rank_start + i
           vllm_engine_port = vllm_start_port + i
           visible_devices = ",".join(str(x) for x in range(i * tp_size, (i + 1) * tp_size))
           process = multiprocessing.Process(target=run_command,
                                           args=(visible_devices, dp_rank,
                                                   vllm_engine_port))
           processes.append(process)
           process.start()
   
       for process in processes:
           process.join()
   

2. prepare the script run_dp_template.sh on each node.

   To support a 200k context window on the stage of prefill, the parameter "layer_sharding": ["q_b_proj", "o_proj"] needs to be added to --additional_config on each prefill node. 1. Prefill node 0

   ```shell
   nic_name="xxxx" # change to your own nic name
   local_ip="xxxx" # change to your own ip
   
   export HCCL_OP_EXPANSION_MODE="AIV"
   
   export HCCL_IF_IP=$local_ip
   export GLOO_SOCKET_IFNAME=$nic_name
   export TP_SOCKET_IFNAME=$nic_name
   export HCCL_SOCKET_IFNAME=$nic_name
   
   export OMP_PROC_BIND=false
   export OMP_NUM_THREADS=1
   export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
   export HCCL_BUFFSIZE=256
   
   export ASCEND_AGGREGATE_ENABLE=1
   export ASCEND_TRANSPORT_PRINT=1
   export ACL_OP_INIT_MODE=1
   export ASCEND_A3_ENABLE=1
   # Timeout (in seconds) for automatically releasing the prefiller’s KV cache for a particular request.
   export VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT=480
   
   export ASCEND_RT_VISIBLE_DEVICES=$1
   export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
   
   export VLLM_ASCEND_ENABLE_FUSED_MC2=1
   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
   
   vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w8a8 \
       --host 0.0.0.0 \
       --port $2 \
       --data-parallel-size $3 \
       --data-parallel-rank $4 \
       --data-parallel-address $5 \
       --data-parallel-rpc-port $6 \
       --tensor-parallel-size $7 \
       --enable-expert-parallel \
       --speculative-config '{"num_speculative_tokens": 3, "method":"deepseek_mtp"}' \
       --profiler-config \
       '{"profiler": "torch",
       "torch_profiler_dir": "./vllm_profile",
       "torch_profiler_with_stack": false}' \
       --seed 1024 \
       --served-model-name glm-5 \
       --max-model-len 131072 \
       --additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true, "enable_dsa_cp": true}' \
       --max-num-batched-tokens 4096 \
       --trust-remote-code \
       --max-num-seqs 64 \
       --enable-chunked-prefill \
       --quantization ascend \
       --gpu-memory-utilization 0.95 \
       --enforce-eager \
       --enable-auto-tool-choice \
       --tool-call-parser glm47 \
       --reasoning-parser glm45 \
       --kv-transfer-config \
       '{"kv_connector": "MooncakeConnectorV1",
       "kv_role": "kv_producer",
       "kv_port": "30000",
       "kv_connector_extra_config": {
                   "use_ascend_direct": true,
                   "prefill": {
                           "dp_size": 2,
                           "tp_size": 16
                   },
                   "decode": {
                           "dp_size": 16,
                           "tp_size": 4
                   }
           }
       }'
   
   ```
   

   1. Prefill node 1

      nic_name="xxxx" # change to your own nic name
      local_ip="xxxx" # change to your own ip
      
      export HCCL_OP_EXPANSION_MODE="AIV"
      
      export HCCL_IF_IP=$local_ip
      export GLOO_SOCKET_IFNAME=$nic_name
      export TP_SOCKET_IFNAME=$nic_name
      export HCCL_SOCKET_IFNAME=$nic_name
      
      export OMP_PROC_BIND=false
      export OMP_NUM_THREADS=1
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=256
      
      export ASCEND_AGGREGATE_ENABLE=1
      export ASCEND_TRANSPORT_PRINT=1
      export ACL_OP_INIT_MODE=1
      export ASCEND_A3_ENABLE=1
      # Timeout (in seconds) for automatically releasing the prefiller’s KV cache for a particular request.
      export VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT=480
      
      export ASCEND_RT_VISIBLE_DEVICES=$1
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      
      export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
      
      export VLLM_ASCEND_ENABLE_FUSED_MC2=1
      export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
      
      vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w8a8 \
          --host 0.0.0.0 \
          --port $2 \
          --data-parallel-size $3 \
          --data-parallel-rank $4 \
          --data-parallel-address $5 \
          --data-parallel-rpc-port $6 \
          --tensor-parallel-size $7 \
          --enable-expert-parallel \
          --speculative-config '{"num_speculative_tokens": 3, "method":"deepseek_mtp"}' \
          --profiler-config \
          '{"profiler": "torch",
          "torch_profiler_dir": "./vllm_profile",
          "torch_profiler_with_stack": false}' \
          --seed 1024 \
          --served-model-name glm-5 \
          --max-model-len 131072 \
          --additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true, "enable_dsa_cp": true}' \
          --max-num-batched-tokens 4096 \
          --trust-remote-code \
          --max-num-seqs 64 \
          --enable-chunked-prefill \
          --gpu-memory-utilization 0.95 \
          --quantization ascend \
          --enforce-eager \
          --enable-auto-tool-choice \
          --tool-call-parser glm47 \
          --reasoning-parser glm45 \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeConnectorV1",
          "kv_role": "kv_producer",
          "kv_port": "30000",
          "kv_connector_extra_config": {
                      "use_ascend_direct": true,
                      "prefill": {
                              "dp_size": 2,
                              "tp_size": 16
                      },
                      "decode": {
                              "dp_size": 16,
                              "tp_size": 4
                      }
              }
          }'
      

   2. Decode node 0

      nic_name="xxxx" # change to your own nic name
      local_ip="xxxx" # change to your own ip
      
      export HCCL_OP_EXPANSION_MODE="AIV"
      
      export HCCL_IF_IP=$local_ip
      export GLOO_SOCKET_IFNAME=$nic_name
      export TP_SOCKET_IFNAME=$nic_name
      export HCCL_SOCKET_IFNAME=$nic_name
      
      #Mooncake
      export OMP_PROC_BIND=false
      export OMP_NUM_THREADS=1
      
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=256
      
      
      export ASCEND_AGGREGATE_ENABLE=1
      export ASCEND_TRANSPORT_PRINT=1
      export ACL_OP_INIT_MODE=1
      export ASCEND_A3_ENABLE=1
      # Timeout (in seconds) for automatically releasing the prefiller’s KV cache for a particular request.
      export VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT=480
      
      export TASK_QUEUE_ENABLE=1
      
      export ASCEND_RT_VISIBLE_DEVICES=$1
      
      export VLLM_ASCEND_ENABLE_FUSED_MC2=1
      export VLLM_ASCEND_ENABLE_MLAPO=1
      export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
      
      vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w8a8 \
          --host 0.0.0.0 \
          --port $2 \
          --data-parallel-size $3 \
          --data-parallel-rank $4 \
          --data-parallel-address $5 \
          --data-parallel-rpc-port $6 \
          --tensor-parallel-size $7 \
          --enable-expert-parallel \
          --speculative-config '{"num_speculative_tokens": 3,  "method":"deepseek_mtp"}' \
          --profiler-config \
          '{"profiler": "torch",
          "torch_profiler_dir": "./vllm_profile",
          "torch_profiler_with_stack": false}' \
          --seed 1024 \
          --served-model-name glm-5 \
          --max-model-len 200000 \
          --max-num-batched-tokens 32 \
          --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}' \
          --additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true, "recompute_scheduler_enable": true}' \
          --trust-remote-code \
          --max-num-seqs 8 \
          --gpu-memory-utilization 0.92 \
          --quantization ascend \
          --enable-auto-tool-choice \
          --tool-call-parser glm47 \
          --reasoning-parser glm45 \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeConnectorV1",
          "kv_role": "kv_consumer",
          "kv_port": "30100",
          "kv_connector_extra_config": {
                      "use_ascend_direct": true,
                      "prefill": {
                              "dp_size": 2,
                              "tp_size": 16
                      },
                      "decode": {
                              "dp_size": 16,
                              "tp_size": 4
                      }
              }
          }'
      

   3. Decode node 1

      nic_name="xxxx" # change to your own nic name
      local_ip="xxxx" # change to your own ip
      
      export HCCL_OP_EXPANSION_MODE="AIV"
      
      export HCCL_IF_IP=$local_ip
      export GLOO_SOCKET_IFNAME=$nic_name
      export TP_SOCKET_IFNAME=$nic_name
      export HCCL_SOCKET_IFNAME=$nic_name
      
      #Mooncake
      export OMP_PROC_BIND=false
      export OMP_NUM_THREADS=1
      
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=256
      
      export ASCEND_AGGREGATE_ENABLE=1
      export ASCEND_TRANSPORT_PRINT=1
      export ACL_OP_INIT_MODE=1
      export ASCEND_A3_ENABLE=1
      # Timeout (in seconds) for automatically releasing the prefiller’s KV cache for a particular request.
      export VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT=480
      
      export TASK_QUEUE_ENABLE=1
      
      export ASCEND_RT_VISIBLE_DEVICES=$1
      
      export VLLM_ASCEND_ENABLE_FUSED_MC2=1
      export VLLM_ASCEND_ENABLE_MLAPO=1
      export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
      
      vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w8a8 \
          --host 0.0.0.0 \
          --port $2 \
          --data-parallel-size $3 \
          --data-parallel-rank $4 \
          --data-parallel-address $5 \
          --data-parallel-rpc-port $6 \
          --tensor-parallel-size $7 \
          --enable-expert-parallel \
          --speculative-config '{"num_speculative_tokens": 3,  "method":"deepseek_mtp"}' \
          --profiler-config \
          '{"profiler": "torch",
          "torch_profiler_dir": "./vllm_profile",
          "torch_profiler_with_stack": false}' \
          --seed 1024 \
          --served-model-name glm-5 \
          --max-model-len 200000 \
          --max-num-batched-tokens 32 \
          --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}' \
          --additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true, "recompute_scheduler_enable": true}' \
          --trust-remote-code \
          --max-num-seqs 8 \
          --gpu-memory-utilization 0.92 \
          --quantization ascend \
          --enable-auto-tool-choice \
          --tool-call-parser glm47 \
          --reasoning-parser glm45 \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeConnectorV1",
          "kv_role": "kv_consumer",
          "kv_port": "30100",
          "kv_connector_extra_config": {
                      "use_ascend_direct": true,
                      "prefill": {
                              "dp_size": 2,
                              "tp_size": 16
                      },
                      "decode": {
                              "dp_size": 16,
                              "tp_size": 4
                      }
              }
          }'
      

   4. Decode node 2

      nic_name="xxxx" # change to your own nic name
      local_ip="xxxx" # change to your own ip
      
      export HCCL_OP_EXPANSION_MODE="AIV"
      
      export HCCL_IF_IP=$local_ip
      export GLOO_SOCKET_IFNAME=$nic_name
      export TP_SOCKET_IFNAME=$nic_name
      export HCCL_SOCKET_IFNAME=$nic_name
      
      #Mooncake
      export OMP_PROC_BIND=false
      export OMP_NUM_THREADS=1
      
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=256
      
      export ASCEND_AGGREGATE_ENABLE=1
      export ASCEND_TRANSPORT_PRINT=1
      export ACL_OP_INIT_MODE=1
      export ASCEND_A3_ENABLE=1
      # Timeout (in seconds) for automatically releasing the prefiller’s KV cache for a particular request.
      export VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT=480
      
      export TASK_QUEUE_ENABLE=1
      
      export ASCEND_RT_VISIBLE_DEVICES=$1
      
      export VLLM_ASCEND_ENABLE_FUSED_MC2=1
      export VLLM_ASCEND_ENABLE_MLAPO=1
      export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
      
      vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w8a8 \
          --host 0.0.0.0 \
          --port $2 \
          --data-parallel-size $3 \
          --data-parallel-rank $4 \
          --data-parallel-address $5 \
          --data-parallel-rpc-port $6 \
          --tensor-parallel-size $7 \
          --enable-expert-parallel \
          --speculative-config '{"num_speculative_tokens": 3,  "method":"deepseek_mtp"}' \
          --profiler-config \
          '{"profiler": "torch",
          "torch_profiler_dir": "./vllm_profile",
          "torch_profiler_with_stack": false}' \
          --seed 1024 \
          --served-model-name glm-5 \
          --max-model-len 200000 \
          --max-num-batched-tokens 32 \
          --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}' \
          --additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true, "recompute_scheduler_enable": true}' \
          --trust-remote-code \
          --max-num-seqs 8 \
          --gpu-memory-utilization 0.92 \
          --quantization ascend \
          --enable-auto-tool-choice \
          --tool-call-parser glm47 \
          --reasoning-parser glm45 \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeConnectorV1",
          "kv_role": "kv_consumer",
          "kv_port": "30100",
          "kv_connector_extra_config": {
                      "use_ascend_direct": true,
                      "prefill": {
                              "dp_size": 2,
                              "tp_size": 16
                      },
                      "decode": {
                              "dp_size": 16,
                              "tp_size": 4
                      }
              }
          }'
      

   5. Decode node 3

      nic_name="xxxx" # change to your own nic name
      local_ip="xxxx" # change to your own ip
      
      export HCCL_OP_EXPANSION_MODE="AIV"
      
      export HCCL_IF_IP=$local_ip
      export GLOO_SOCKET_IFNAME=$nic_name
      export TP_SOCKET_IFNAME=$nic_name
      export HCCL_SOCKET_IFNAME=$nic_name
      
      #Mooncake
      export OMP_PROC_BIND=false
      export OMP_NUM_THREADS=1
      
      export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
      export HCCL_BUFFSIZE=256
      
      export ASCEND_AGGREGATE_ENABLE=1
      export ASCEND_TRANSPORT_PRINT=1
      export ACL_OP_INIT_MODE=1
      export ASCEND_A3_ENABLE=1
      # Timeout (in seconds) for automatically releasing the prefiller’s KV cache for a particular request.
      export VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT=480
      
      export TASK_QUEUE_ENABLE=1
      
      export ASCEND_RT_VISIBLE_DEVICES=$1
      
      export VLLM_ASCEND_ENABLE_FUSED_MC2=1
      export VLLM_ASCEND_ENABLE_MLAPO=1
      export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
      
      vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM5-w8a8 \
          --host 0.0.0.0 \
          --port $2 \
          --data-parallel-size $3 \
          --data-parallel-rank $4 \
          --data-parallel-address $5 \
          --data-parallel-rpc-port $6 \
          --tensor-parallel-size $7 \
          --enable-expert-parallel \
          --speculative-config '{"num_speculative_tokens": 3,  "method":"deepseek_mtp"}' \
          --profiler-config \
          '{"profiler": "torch",
          "torch_profiler_dir": "./vllm_profile",
          "torch_profiler_with_stack": false}' \
          --seed 1024 \
          --served-model-name glm-5 \
          --max-model-len 200000 \
          --max-num-batched-tokens 32 \
          --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}' \
          --additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true, "recompute_scheduler_enable": true}' \
          --trust-remote-code \
          --max-num-seqs 8 \
          --gpu-memory-utilization 0.92 \
          --quantization ascend \
          --enable-auto-tool-choice \
          --tool-call-parser glm47 \
          --reasoning-parser glm45 \
          --kv-transfer-config \
          '{"kv_connector": "MooncakeConnectorV1",
          "kv_role": "kv_consumer",
          "kv_port": "30100",
          "kv_connector_extra_config": {
                      "use_ascend_direct": true,
                      "prefill": {
                              "dp_size": 2,
                              "tp_size": 16
                      },
                      "decode": {
                              "dp_size": 16,
                              "tp_size": 4
                      }
              }
          }'
      

Once the preparation is done, you can start the server with the following command on each node:

1. Prefill node 0

   # change ip to your own
   python launch_online_dp.py --dp-size 2 --tp-size 16 --dp-size-local 1 --dp-rank-start 0 --dp-address $node_p0_ip --dp-rpc-port 10521 --vllm-start-port 6700
   

2. Prefill node 1

   # change ip to your own
   python launch_online_dp.py --dp-size 2 --tp-size 16 --dp-size-local 1 --dp-rank-start 1 --dp-address $node_p0_ip --dp-rpc-port 10521 --vllm-start-port 6700
   

3. Decode node 0

   # change ip to your own
   python launch_online_dp.py --dp-size 16 --tp-size 4 --dp-size-local 4 --dp-rank-start 0 --dp-address $node_d0_ip --dp-rpc-port 10523 --vllm-start-port 6721
   

4. Decode node 1

   # change ip to your own
   python launch_online_dp.py --dp-size 16 --tp-size 4 --dp-size-local 4 --dp-rank-start 4 --dp-address $node_d0_ip --dp-rpc-port 10523 --vllm-start-port 6721
   

5. Decode node 2

   # change ip to your own
   python launch_online_dp.py --dp-size 16 --tp-size 4 --dp-size-local 4 --dp-rank-start 8 --dp-address $node_d0_ip --dp-rpc-port 10523 --vllm-start-port 6721
   

6. Decode node 3

   # change ip to your own
   python launch_online_dp.py --dp-size 16 --tp-size 4 --dp-size-local 4 --dp-rank-start 12 --dp-address $node_d0_ip --dp-rpc-port 10523 --vllm-start-port 6721
   

5.4 Request Forwarding

To set up request forwarding, run the following script on any machine. You can get the proxy program in the repository's examples: load_balance_proxy_server_example.py

unset http_proxy
unset https_proxy

python load_balance_proxy_server_example.py \
    --port 8000 \
    --host 0.0.0.0 \
    --prefiller-hosts \
       $node_p0_ip \
       $node_p1_ip \
    --prefiller-ports \
       6700 \
       6700 \
    --decoder-hosts \
      $node_d0_ip \
      $node_d0_ip \
      $node_d0_ip \
      $node_d0_ip \
      $node_d1_ip \
      $node_d1_ip \
      $node_d1_ip \
      $node_d1_ip \
      $node_d2_ip \
      $node_d2_ip \
      $node_d2_ip \
      $node_d2_ip \
      $node_d3_ip \
      $node_d3_ip \
      $node_d3_ip \
      $node_d3_ip \
    --decoder-ports \
      6721 6722 6723 6724 \
      6721 6722 6723 6724 \
      6721 6722 6723 6724 \
      6721 6722 6723 6724      

Notice:

Some configurations for optimization are shown below:

• VLLM_ASCEND_ENABLE_FLASHCOMM1: Enable FlashComm optimization to reduce communication and computation overhead on prefill node. With FlashComm enabled, layer_sharding list cannot include o_proj as an element.
• VLLM_ASCEND_ENABLE_FUSED_MC2: Enable following fused operators: dispatch_gmm_combine_decode and dispatch_ffn_combine operator.
• VLLM_ASCEND_ENABLE_MLAPO: Enable fused operator MlaPreprocessOperation.

Please refer to the following python file for further explanation and restrictions of the environment variables above: envs.py

6 Functional Verification

Once your server is started, you can query the model with input prompts:

curl http://<node0_ip>:<port>/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "glm-5",
        "prompt": "The future of AI is",
        "max_completion_tokens": 15,
        "temperature": 0
    }'

Expected Result:

{"id": "chatcmlib-bc44ad093dec79a2", "object": "chat.completion", "created": "1770903266", "model": "glm-5", "choices": [{ "index": 0, "message": {"role": "assistant", "content": "The future of AI is not one thing, but a convergence of several powerful trends.", "annotations": "null", "audio": "null", "function_call": "null", "tool_calls": [], "reasoning": "null"}, "logprobs": "null", "finish_reason": "length", "stop_reason": "null", "token_ids": null}], "service_tier": "null", "system fingerprint": "null", "usage": {"prompt_tokens": 5, "total_tokens": 20, "completion_tokens": 15, "prompt_tokens_details": null}, "prompt_logprobs": "null", "prompt_token_ids": "null", "kv_transfer_params": null}

7 Accuracy Evaluation

7.1 Using AISBench

1. Refer to Using AISBench for details.

2. After execution, you can get the result.

8 Performance Evaluation

8.1 Using AISBench

Refer to Using AISBench for performance evaluation for details.

8.2 Using vLLM Benchmark

Refer to vllm benchmark for more details.

9 Performance Tuning

9.1 Recommended Configurations

Note: The following configurations are validated in specific test environments and are for reference only. The optimal configuration depends on factors such as maximum input/output length, prefix cache hit rate, precision requirements, and deployment machine ratios. It is recommended to refer to Section 9.2 for tuning based on actual conditions.

Table 1: Scenario Overview

Scenario	Deployment Mode	*Total NPUs	Weight Version	Key Considerations
High Throughput	1P1D deployment	32 (A3)	GLM5-w8a8/GLM5.1-w8a8	dp4 tp8 on P nodes and dp8 dp4 on D nodes to balanced latency and throughput
Low Latency	1P1D deployment	32 (A3)	GLM5-w8a8/GLM5.1-w8a8	dp4 tp8 on both P and D nodes to reduce latency

*Total NPUs indicates the total number of NPUs used across all nodes.

Table 2: Detailed Node Configuration

Scenario	Configuration	NPUs	TP	DP	Max Num Seqs	Max Num Batched Tokens	Max Model Len	MTP Speculation Num
High Throughput (A3)	1P1D deployment	32	P:8 D:4	P:4 D:8	P:64 D:128	P:4096 D:32	P:133120 D:150000	3
Low Latency (A3)	1P1D deployment	32	4	8	P:64 D:128	P:4096 D:32	P:133120 D:150000	3

10 FAQ

• Common Issues Tip: If you encounter issues, Refer to FAQs.

• Q: How to solve ValueError: Tokenizer class TokenizersBackend does not exist or is not currently imported?

A: Please update the version of transformers to 5.2.0

• Q: How to enable function calling for GLM-5?

A: Please add following configurations in vLLM startup command

--tool-call-parser glm47 \
--reasoning-parser glm45 \
--enable-auto-tool-choice \