GLM-5.2

GLM-5.2#

Introduction#

GLM-5.2 use a Mixture-of-Experts (MoE) architecture and targets complex systems engineering and long-horizon agentic tasks.

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.

Supported Features#

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

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

Environment Preparation#

Model Weight#

GLM-5.2(BF16 version)require 2 Atlas 800 A3 (128G × 8) node or 4 Atlas 800 A2 (64G × 8) node.: Download model weight.
GLM-5.2-w8a8: require 1 Atlas 800 A3 (128G × 8) node or 2 Atlas 800 A2 (64G × 8) node.Download model weight.
You can use msmodelslim to quantify the model naively.

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

Installation#

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

A3 series

Start the docker image on your each node.

export IMAGE=quay.io/ascend/vllm-ascend:glm5.2-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

A2 series

Start the docker image on your each node.

export IMAGE=quay.io/ascend/vllm-ascend:glm5.2
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.

Deployment#

Single-node Deployment#

Quantized model glm-5.2-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_VERSION=0.21.0
vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM-5.2-w8a8 \
--host 0.0.0.0 \
--port 8077 \
--data-parallel-size 2 \
--tensor-parallel-size 8 \
--enable-expert-parallel \
--seed 1024 \
--served-model-name glm-52 \
--max-num-seqs 48 \
--max-model-len 20480 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.95 \
--quantization ascend \
--async-scheduling \
--additional-config '{"enable_npugraph_ex": true,"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"}'

Notice: The parameters are explained as follows:

For single-node deployment, we recommend using dp2tp8 and turn off expert parallel in low-latency scenarios.

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.

A3 series

glm-5.2-w8a8: can be deployed on 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 VLLM_VERSION=0.21.0
export HCCL_OP_EXPANSION_MODE="AIV"
export VLLM_ASCEND_BALANCE_SCHEDULING=0
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=400
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_MLAPO=1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
export ASCEND_LAUNCH_BLOCKING=0

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM-5.2-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 12980 \
--tensor-parallel-size 16 \
--seed 1024 \
--served-model-name glm-5 \
--max-num-seqs 48 \
--max-model-len 64000 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.93 \
--quantization ascend \
--enable-prefix-caching \
--async-scheduling \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config '{"enable_npugraph_ex": true,"fuse_muls_add":true,"multistream_overlap_shared_expert":true}' \
--speculative-config '{"num_speculative_tokens": 5, "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 VLLM_VERSION=0.21.0
export HCCL_OP_EXPANSION_MODE="AIV"
export VLLM_ASCEND_BALANCE_SCHEDULING=0
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=400
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export VLLM_ASCEND_ENABLE_MLAPO=1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
export ASCEND_LAUNCH_BLOCKING=0

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM-5.2-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-rpc-port 12980 \
--data-parallel-address $node0_ip \
--tensor-parallel-size 16 \
--seed 1024 \
--served-model-name glm-5 \
--max-num-seqs 48 \
--max-model-len 64000 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--gpu-memory-utilization 0.93 \
--quantization ascend \
--enable-prefix-caching \
--async-scheduling \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config '{"enable_npugraph_ex": true,"fuse_muls_add":true,"multistream_overlap_shared_expert":true}' \
--speculative-config '{"num_speculative_tokens": 5, "method": "deepseek_mtp"}'

A2 series

glm-5.2-w8a8: can be deployed on 2 Atlas 800 A2 (64G × 32).

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 VLLM_RPC_TIMEOUT=360000
export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=3000
export HCCL_EXEC_TIMEOUT=200
export HCCL_CONNECT_TIMEOUT=120
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export ACL_OP_INIT_MODE=1
#export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
#export USE_MULTI_GROUPS_KV_CACHE=1
#export USE_MULTI_BLOCK_POOL=1
export TASK_QUEUE_ENABLE=1
export CPU_AFFINITY_CONF=1
export VLLM_ENGINE_READY_TIMEOUT_S=1200

export VLLM_VERSION=0.21.0
vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM-5.2-w8a8 \
--max_model_len 40000 \
--max-num-batched-tokens 4096 \
--served-model-name glm-52 \
--seed 1024 \
--gpu-memory-utilization 0.95 \
--api-server-count 1 \
--max-num-seqs 16 \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-address $local_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 8 \
--enable-expert-parallel \
--quantization ascend \
--port 7000 \
--safetensors-load-strategy 'prefetch' \
--block-size 128 \
--async-scheduling \
--additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true, "ascend_compilation_config": {"enable_npugraph_ex": true}}' \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 5, "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 VLLM_RPC_TIMEOUT=360000
export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=3000
export HCCL_EXEC_TIMEOUT=200
export HCCL_CONNECT_TIMEOUT=120
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export ACL_OP_INIT_MODE=1
#export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
#export USE_MULTI_GROUPS_KV_CACHE=1
#export USE_MULTI_BLOCK_POOL=1
export TASK_QUEUE_ENABLE=1
export CPU_AFFINITY_CONF=1
export VLLM_ENGINE_READY_TIMEOUT_S=1200

export VLLM_VERSION=0.21.0
vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM-5.2-w8a8 \
--max_model_len 40000 \
--max-num-batched-tokens 4096 \
--served-model-name glm-52 \
--seed 1024 \
--gpu-memory-utilization 0.95 \
--api-server-count 1 \
--max-num-seqs 16 \
--headless \
--data-parallel-size 2 \
--data-parallel-size-local 1 \
--data-parallel-start-rank 1 \
--data-parallel-address $local_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 8 \
--enable-expert-parallel \
--quantization ascend \
--port 7000 \
--safetensors-load-strategy 'prefetch' \
--block-size 128 \
--async-scheduling \
--additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true, "ascend_compilation_config": {"enable_npugraph_ex": true}}' \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--speculative-config '{"num_speculative_tokens": 5, "method": "deepseek_mtp"}'

Co-located Deployment on 4 Nodes (200k context)#

In a co-located (mixed) deployment, prefill and decode run together on the same nodes, in contrast to the disaggregated setup below. The following templates deploy GLM-5.2 across 4 nodes with DP4 TP8 (data-parallel-size-local=1 per node), a 200k context window, and MTP (num_speculative_tokens=5). Node 0 hosts the API server and is the DP master; Node 1 to Node 3 run with --headless. Prefix caching is disabled (--no-enable-prefix-caching) in this configuration. All IPs, NIC names, ports and weight paths are placeholders.

Node 0 (API server / DP master):

#!/usr/bin/bash

nic_name="<NIC_NAME>"
local_ip=$(hostname -I | awk -F " " '{print $1}')
echo "$local_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 VLLM_RPC_TIMEOUT=360000
export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=3000
export HCCL_EXEC_TIMEOUT=200
export HCCL_CONNECT_TIMEOUT=120

export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export ACL_OP_INIT_MODE=1

export TASK_QUEUE_ENABLE=1
export CPU_AFFINITY_CONF=1
export VLLM_ENGINE_READY_TIMEOUT_S=1200

export VLLM_VERSION=0.21.0

vllm serve <MODEL_PATH> \
  --max_model_len 200000 \
  --max-num-batched-tokens 4096 \
  --served-model-name glm \
  --seed 1024 \
  --api-server-count 1 \
  --gpu-memory-utilization 0.95 \
  --max-num-seqs 32 \
  --data-parallel-size 4 \
  --data-parallel-size-local 1 \
  --data-parallel-address $local_ip \
  --data-parallel-rpc-port 13389 \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --quantization ascend \
  --port 7000 \
  --safetensors-load-strategy 'prefetch' \
  --block-size 128 \
  --enable-chunked-prefill \
  --no-enable-prefix-caching \
  --async-scheduling \
  --additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true, "ascend_compilation_config": {"enable_npugraph_ex": true}}' \
  --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
  --speculative-config '{"num_speculative_tokens": 5, "method": "deepseek_mtp"}'

Node 1 (headless, --data-parallel-start-rank 1):

#!/usr/bin/bash

nic_name="<NIC_NAME>"
local_ip=$(hostname -I | awk -F " " '{print $1}')
node0_ip="<NODE0_IP>"
echo "$local_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 VLLM_RPC_TIMEOUT=360000
export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=3000
export HCCL_EXEC_TIMEOUT=200
export HCCL_CONNECT_TIMEOUT=120

export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export ACL_OP_INIT_MODE=1

export TASK_QUEUE_ENABLE=1
export CPU_AFFINITY_CONF=1
export VLLM_ENGINE_READY_TIMEOUT_S=1200

export VLLM_VERSION=0.21.0

vllm serve <MODEL_PATH> \
  --max_model_len 200000 \
  --max-num-batched-tokens 4096 \
  --headless \
  --served-model-name glm \
  --seed 1024 \
  --gpu-memory-utilization 0.95 \
  --max-num-seqs 32 \
  --safetensors-load-strategy 'prefetch' \
  --data-parallel-size 4 \
  --data-parallel-size-local 1 \
  --data-parallel-start-rank 1 \
  --data-parallel-address $node0_ip \
  --data-parallel-rpc-port 13389 \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --quantization ascend \
  --port 7000 \
  --block-size 128 \
  --enable-chunked-prefill \
  --no-enable-prefix-caching \
  --async-scheduling \
  --additional-config '{"fuse_muls_add": true, "multistream_overlap_shared_expert": true, "ascend_compilation_config": {"enable_npugraph_ex": true}}' \
  --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
  --speculative-config '{"num_speculative_tokens": 5, "method": "deepseek_mtp"}'

Node 2 and Node 3 use the same script as Node 1, with --data-parallel-start-rank set to 2 and 3 respectively (and node0_ip pointing to Node 0).

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 can be deployed on 4 Atlas 800 A3 (64G × 32).

Before you start, please

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()

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"] needs to be added to --additional_config on each prefill node.

Prefill node 0

nic_name="xxxx" # change to your own nic name
local_ip="xxxx" # change to your own ip

export VLLM_VERSION=0.21.0
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
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
export ASCEND_RT_VISIBLE_DEVICES=$1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
export VLLM_ASCEND_ENABLE_FUSED_MC2=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM-5.2-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 \
    --seed 1024 \
    --served-model-name glm-52 \
    --max-model-len 135000 \
    --speculative-config '{"num_speculative_tokens": 5, "method":"deepseek_mtp"}' \
    --additional-config '{"enable_sparse_c8":false,"fuse_muls_add": true, "multistream_overlap_shared_expert": true, "recompute_scheduler_enable": true, "ascend_compilation_config": {"enable_npugraph_ex": true},"enable_dsa_cp": true}' \
    --max-num-batched-tokens 4096 \
    --trust-remote-code \
    --max-num-seqs 64 \
    --async-scheduling \
    --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",
    "engine_id": "0",
    "kv_connector_extra_config": {
                "use_ascend_direct": true,
                "prefill": {
                        "dp_size": 4,
                        "tp_size": 8
                },
                "decode": {
                        "dp_size": 8,
                        "tp_size": 4
                }
        }
    }'

Prefill node 1

nic_name="xxxx" # change to your own nic name
local_ip="xxxx" # change to your own ip

export VLLM_VERSION=0.21.0
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
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
export ASCEND_RT_VISIBLE_DEVICES=$1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
export VLLM_ASCEND_ENABLE_FUSED_MC2=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM-5.2-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 \
    --seed 1024 \
    --served-model-name glm-52 \
    --max-model-len 135000 \
    --speculative-config '{"num_speculative_tokens": 5, "method":"deepseek_mtp"}' \
    --additional-config '{"enable_sparse_c8":false,"fuse_muls_add": true, "multistream_overlap_shared_expert": true, "recompute_scheduler_enable": true, "ascend_compilation_config": {"enable_npugraph_ex": true},"enable_dsa_cp": true}' \
    --max-num-batched-tokens 4096 \
    --trust-remote-code \
    --max-num-seqs 64 \
    --async-scheduling \
    --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",
    "engine_id": "0",
    "kv_connector_extra_config": {
                "use_ascend_direct": true,
                "prefill": {
                        "dp_size": 4,
                        "tp_size": 8
                },
                "decode": {
                        "dp_size": 8,
                        "tp_size": 4
                }
        }
    }'

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
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_BUFFSIZE=500
export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
export VLLM_VERSION=0.21.0
export TASK_QUEUE_ENABLE=1
export ASCEND_RT_VISIBLE_DEVICES=$1
export DYNAMIC_EPLB=1
export VLLM_ASCEND_ENABLE_FUSED_MC2=1
export VLLM_ASCEND_ENABLE_MLAPO=1

vllm serve /root/.cache/modelscope/hub/models/vllm-ascend/GLM-5.2-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 \
    --seed 1024 \
    --served-model-name glm-52 \
    --max-model-len 135000 \
    --max-num-batched-tokens 164 \
    --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}' \
    --speculative-config '{"num_speculative_tokens": 5, "method":"deepseek_mtp"}' \
    --additional-config '{"enable_sparse_c8":false,"fuse_muls_add": true, "multistream_overlap_shared_expert": true, "recompute_scheduler_enable": true, "ascend_compilation_config": {"enable_npugraph_ex": true}}' \
    --trust-remote-code \
    --max-num-seqs 48 \
    --gpu-memory-utilization 0.92 \
    --async-scheduling \
    --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",
    "engine_id": "1",
    "kv_connector_extra_config": {
                "use_ascend_direct": true,
                "prefill": {
                        "dp_size": 4,
                        "tp_size": 8
                },
                "decode": {
                        "dp_size": 8,
                        "tp_size": 4
                }
        }
    }'

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
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_BUFFSIZE=500
export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export ASCEND_A3_ENABLE=1
export TASK_QUEUE_ENABLE=1
export VLLM_VERSION=0.21.0
export ASCEND_RT_VISIBLE_DEVICES=$1
export DYNAMIC_EPLB=1
export VLLM_ASCEND_ENABLE_FUSED_MC2=1
export VLLM_ASCEND_ENABLE_MLAPO=1

vllm serve /mnt/share/weight/GLM-5.2-0610-Provider-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 \
   --seed 1024 \
   --served-model-name glm-52 \
   --max-model-len 135000 \
   --max-num-batched-tokens 164 \
   --speculative-config '{"num_speculative_tokens": 5, "method":"deepseek_mtp"}' \
   --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}' \
   --additional-config '{"enable_sparse_c8":false,"fuse_muls_add": true, "multistream_overlap_shared_expert": true, "recompute_scheduler_enable": true, "ascend_compilation_config": {"enable_npugraph_ex": true}}' \
   --trust-remote-code \
   --max-num-seqs 48 \
   --gpu-memory-utilization 0.92 \
   --async-scheduling \
   --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",
   "engine_id": "1",
   "kv_connector_extra_config": {
                "use_ascend_direct": true,
                "prefill": {
                        "dp_size": 4,
                        "tp_size": 8
                },
                "decode": {
                        "dp_size": 8,
                        "tp_size": 4
                }
        }
    }'

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

Prefill node 0

# change ip to your own
python launch_online_dp.py --dp-size 4 --tp-size 8  --dp-size-local 2 --dp-rank-start 0 --dp-address $node_p0_ip --dp-rpc-port 16591 --vllm-start-port 9081

Prefill node 1

# change ip to your own
python launch_online_dp.py --dp-size 4 --tp-size 8  --dp-size-local 2 --dp-rank-start 2 --dp-address $node_p0_ip --dp-rpc-port 16591 --vllm-start-port 9081

Decode node 0

# change ip to your own
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 4 --dp-rank-start 0 --dp-address $node_p0_ip --dp-rpc-port 16600 --vllm-start-port 9900

Decode node 1

# change ip to your own
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 4 --dp-rank-start 4 --dp-address $node_p0_ip --dp-rpc-port 16600 --vllm-start-port 9900

Deployment on 8 Atlas 800 A2#

On Atlas 800 A2, where each node exposes 8 cards, the same global P/D topology (Prefill DP4 TP8, Decode DP8 TP4) is split across 8 nodes: 4 prefill nodes hosting 1 DP rank each (8 cards per rank), and 4 decode nodes hosting 2 DP ranks each (4 cards per rank). The launch_online_dp.py above is reused as-is. The prefill side enables FlashComm1 and DSA CP; the decode side enables MLAPO and DYNAMIC_EPLB with a FULL_DECODE_ONLY graph. Both sides enable prefix caching and MTP (num_speculative_tokens=3). All IPs, NIC names, ports and weight paths below are placeholders.

run_dp_template.sh for the prefill nodes:

#!/usr/bin/bash
nic_name="<NIC_NAME>"
local_ip="<CURRENT_NODE_IP>"

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 VLLM_HOST_IP=$local_ip

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
export HCCL_OP_EXPANSION_MODE="AIV"
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=256
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export VLLM_NIXL_ABORT_REQUEST_TIMEOUT=300000
export VLLM_VERSION=0.21.0

export ASCEND_RT_VISIBLE_DEVICES=$1
export VLLM_ASCEND_ENABLE_FLASHCOMM1=1

vllm serve <MODEL_PATH> \
  --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 \
  --seed 1024 \
  --served-model-name glm5.2 \
  --max-model-len 115168 \
  --max-num-batched-tokens 4096 \
  --trust-remote-code \
  --max-num-seqs 64 \
  --gpu-memory-utilization 0.95 \
  --quantization ascend \
  --async-scheduling \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --enforce-eager \
  --enable-auto-tool-choice \
  --tool-call-parser glm47 \
  --reasoning-parser glm45 \
  --kv-transfer-config \
  '{
    "kv_connector": "MooncakeConnector",
    "kv_role": "kv_producer",
    "kv_port": "30000",
    "engine_id": "0",
    "kv_connector_module_path": "vllm_ascend.distributed.kv_transfer.kv_p2p.mooncake_connector",
    "kv_connector_extra_config": {
      "use_ascend_direct": true,
      "prefill": {
        "dp_size": 4,
        "tp_size": 8
      },
      "decode": {
        "dp_size": 8,
        "tp_size": 4
      }
    }
  }' \
  --additional-config \
  '{
    "enable_sparse_c8": false,
    "fuse_muls_add": true,
    "multistream_overlap_shared_expert": true,
    "recompute_scheduler_enable": true,
    "ascend_compilation_config": {
      "enable_npugraph_ex": true
    },
    "enable_dsa_cp": true
  }' \
  --speculative-config '{"num_speculative_tokens": 3, "method":"deepseek_mtp"}'

run_dp_template.sh for the decode nodes:

#!/usr/bin/bash

nic_name="<NIC_NAME>"
local_ip="<CURRENT_NODE_IP>"

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 VLLM_HOST_IP=$local_ip

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
export VLLM_ASCEND_ENABLE_MLAPO=1
export HCCL_OP_EXPANSION_MODE="AIV"
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=1
export HCCL_BUFFSIZE=500
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export TASK_QUEUE_ENABLE=1
export ASCEND_AGGREGATE_ENABLE=1
export ASCEND_TRANSPORT_PRINT=1
export ACL_OP_INIT_MODE=1
export VLLM_VERSION=0.21.0
export DYNAMIC_EPLB=1

export ASCEND_RT_VISIBLE_DEVICES=$1

vllm serve <MODEL_PATH> \
  --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 \
  --seed 1024 \
  --served-model-name glm5.2 \
  --max-model-len 135168 \
  --max-num-batched-tokens 164 \
  --trust-remote-code \
  --max-num-seqs 48 \
  --gpu-memory-utilization 0.92 \
  --async-scheduling \
  --quantization ascend \
  --enable-prefix-caching \
  --enable-auto-tool-choice \
  --tool-call-parser glm47 \
  --reasoning-parser glm45 \
  --kv-transfer-config \
  '{
    "kv_connector": "MooncakeConnector",
    "kv_role": "kv_consumer",
    "kv_port": "30100",
    "engine_id": "1",
    "kv_connector_module_path": "vllm_ascend.distributed.kv_transfer.kv_p2p.mooncake_connector",
    "kv_connector_extra_config": {
      "use_ascend_direct": true,
      "prefill": {
        "dp_size": 4,
        "tp_size": 8
      },
      "decode": {
        "dp_size": 8,
        "tp_size": 4
      }
    }
  }' \
  --compilation-config \
  '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
  --additional-config \
  '{
    "enable_sparse_c8": false,
    "fuse_muls_add": true,
    "multistream_overlap_shared_expert": true,
    "recompute_scheduler_enable": true,
    "ascend_compilation_config": {
      "enable_npugraph_ex": true
    }
  }' \
  --speculative-config '{"num_speculative_tokens": 3, "method":"deepseek_mtp"}'

Once the preparation is done, start the server with the following commands:

Prefill nodes — run on $node_p0_ip, $node_p1_ip, $node_p2_ip, $node_p3_ip with --dp-rank-start 0/1/2/3:

python launch_online_dp.py --dp-size 4 --tp-size 8 --dp-size-local 1 --dp-rank-start 0 --dp-address $node_p0_ip --dp-rpc-port 16591 --vllm-start-port 9081
python launch_online_dp.py --dp-size 4 --tp-size 8 --dp-size-local 1 --dp-rank-start 1 --dp-address $node_p0_ip --dp-rpc-port 16591 --vllm-start-port 9081
python launch_online_dp.py --dp-size 4 --tp-size 8 --dp-size-local 1 --dp-rank-start 2 --dp-address $node_p0_ip --dp-rpc-port 16591 --vllm-start-port 9081
python launch_online_dp.py --dp-size 4 --tp-size 8 --dp-size-local 1 --dp-rank-start 3 --dp-address $node_p0_ip --dp-rpc-port 16591 --vllm-start-port 9081

Decode nodes — run on $node_d0_ip, $node_d1_ip, $node_d2_ip, $node_d3_ip with --dp-rank-start 0/2/4/6:

python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 2 --dp-rank-start 0 --dp-address $node_d0_ip --dp-rpc-port 16600 --vllm-start-port 9900
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 2 --dp-rank-start 2 --dp-address $node_d0_ip --dp-rpc-port 16600 --vllm-start-port 9900
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 2 --dp-rank-start 4 --dp-address $node_d0_ip --dp-rpc-port 16600 --vllm-start-port 9900
python launch_online_dp.py --dp-size 8 --tp-size 4 --dp-size-local 2 --dp-rank-start 6 --dp-address $node_d0_ip --dp-rpc-port 16600 --vllm-start-port 9900

For request forwarding on this 8-node A2 layout, use 4 prefiller hosts (1 endpoint each) and 4 decoder hosts (2 endpoints each) in the Request Forwarding command below.

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_p0_ip \
       $node_p1_ip \
       $node_p1_ip \
    --prefiller-ports \
       6700 6701 \
       6700 6701 \
    --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 \
    --decoder-ports \
      6800 6801 6802 6803 \
      6800 6801 6802 6803 \  

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.

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

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-52",
        "prompt": "The future of AI is",
        "max_completion_tokens": 50,
        "temperature": 0
    }'

Accuracy Evaluation#

Here are two accuracy evaluation methods.

Using AISBench#

Refer to Using AISBench for details.
After execution, you can get the result.

Using Language Model Evaluation Harness#

Not tested yet.

Performance#

Using AISBench#

Refer to Using AISBench for performance evaluation for details.

Using vLLM Benchmark#

Refer to vllm benchmark for more details.

Notice: max-model-len and max-num-seqs need to be set according to the actual usage scenario. For other settings, please refer to the Deployment chapter.

FAQ#

Q: How to enable function calling for GLM-5.2?

A: Please add following configurations in vLLM startup command
```
--tool-call-parser glm47 \
--reasoning-parser glm45 \
--enable-auto-tool-choice \
```

GLM-5.2

Contents

GLM-5.2#

Introduction#

Supported Features#

Environment Preparation#

Model Weight#

Installation#

Deployment#

Single-node Deployment#

Multi-node Deployment#

Co-located Deployment on 4 Nodes (200k context)#

Prefill-Decode Disaggregation#

Deployment on 8 Atlas 800 A2#

Request Forwarding#

Functional Verification#

Accuracy Evaluation#

Using AISBench#

Using Language Model Evaluation Harness#

Performance#

Using AISBench#

Using vLLM Benchmark#

FAQ#