diff --git a/docs_new/docs.json b/docs_new/docs.json
index 136b250ef..0000ca998 100644
--- a/docs_new/docs.json
+++ b/docs_new/docs.json
@@ -896,6 +896,7 @@
                     "pages": [
                       "docs/hardware-platforms/ascend-npus/model-tutorials/deepseek_r1",
                       "docs/hardware-platforms/ascend-npus/model-tutorials/deepseek_v3_2",
+                      "docs/hardware-platforms/ascend-npus/model-tutorials/glm_5_2",
                       "docs/hardware-platforms/ascend-npus/model-tutorials/glm_5_1",
                       "docs/hardware-platforms/ascend-npus/model-tutorials/kimi_k2_6",
                       "docs/hardware-platforms/ascend-npus/model-tutorials/minimax_m2_5",
diff --git a/docs_new/docs/hardware-platforms/ascend-npus/model-tutorials/glm_5_2.mdx b/docs_new/docs/hardware-platforms/ascend-npus/model-tutorials/glm_5_2.mdx
new file mode 100644
index 000000000..5fb9a9577
--- /dev/null
+++ b/docs_new/docs/hardware-platforms/ascend-npus/model-tutorials/glm_5_2.mdx
@@ -0,0 +1,559 @@
+---
+title: "GLM-5.2"
+metatags:
+  description: "Deploy GLM-5.2 model with SGLang on Ascend NPUs, including single-node and multi-node deployment modes."
+---
+
+## Introduction
+
+GLM-5.2 is a large language model in the GLM (General Language Model) series, jointly developed by the KEG Laboratory
+of Tsinghua University and Zhipu AI. GLM-5.2 adopts the DeepSeek-V3/V3.2 architecture, including DeepSeek Sparse
+Attention (DSA) and multi-token prediction (MTP), and supports high-throughput inference with SGLang on Ascend NPUs.
+
+This document demonstrates the deployment of GLM-5.2 on Ascend NPUs using SGLang, including single-node deployment,
+multi-node deployment, prefill-decode disaggregation, feature configuration, and performance optimization.
+
+## Supported features
+
+| Feature                       | Example usage                                                                     |
+|-------------------------------|-----------------------------------------------------------------------------------|
+| Tensor Parallelism            | `--tp-size 16`                                                                    |
+| Data Parallelism              | `--dp-size 16`                                                                    |
+| Expert Parallelism            | `--ep-size 16 \`<br/>`--moe-a2a-backend deepep \`<br/>`--deepep-mode auto`         |
+| PD Disaggregation             | `--disaggregation-mode prefill \`<br/>`--disaggregation-transfer-backend ascend`  |
+| Quantization                  | `--quantization modelslim`                                                        |
+| Chunked Prefill               | auto based on device memory, or set explicit value;<br/>disable with `--chunked-prefill-size -1`; e.g. `--chunked-prefill-size 16384` |
+| NPU Graph                     | enabled by default; disable with `--disable-cuda-graph`;<br/>control range via `--cuda-graph-bs` or `--cuda-graph-max-bs`; e.g. `--cuda-graph-bs 16` |
+| Speculative Decoding          | `--speculative-algorithm NEXTN \`<br/>`--speculative-num-steps 3 \`<br/>`--speculative-eagle-topk 1 \`<br/>`--speculative-num-draft-tokens 4 \`<br/>`--speculative-draft-model-quantization unquant` |
+| Overlap Schedule              | `export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1`                                      |
+| DP LM Head                    | `--enable-dp-lm-head`                                                             |
+
+<Note>
+The values in the **Example usage** column are for illustration only. Adjust them according to your hardware, deployment
+mode, and workload. For parameter details, see
+[Feature descriptions](/docs/hardware-platforms/ascend-npus/ascend_npu_optimization#feature-descriptions); for
+recommended configurations for each deployment scenario, see [Best practices](#best-practices).
+</Note>
+
+For feature compatibility and conflict information between features,
+see [Feature Compatibility](/docs/hardware-platforms/ascend-npus/ascend_npu_optimization#feature-compatibility).
+
+## Prerequisites
+
+### Model weights
+
+<Warning>
+If you need to download model weights, check the model size before downloading to reserve enough space.
+</Warning>
+
+- [GLM-5.2](https://huggingface.co/collections/zai-org/glm-52) (BF16)
+- [GLM-5.2-w8a8](https://www.modelscope.cn/models/Eco-Tech/GLM-5.2-w8a8/) (Quantized version without MTP)
+- You can use [msmodelslim](https://gitcode.com/Ascend/msmodelslim) to quantize the model naively.
+
+Ensure the available device memory exceeds the model weight size before deployment. For optimal throughput and latency,
+refer to the [best practice configurations](#best-practices) which may require additional nodes or cards.
+
+It is recommended to download the model weights to a shared directory across multiple nodes.
+
+## Installation
+
+<Warning>
+Ensure sufficient disk space before pulling images. The Docker image requires at least **30 GB** of free space.
+</Warning>
+
+The dependencies required for the NPU runtime environment have been integrated into a Docker image and uploaded to the
+online platform. You can directly pull it.
+
+<Note>
+The GLM-5.2 images below use daily build tags because 0Day support was released before the related code was merged into
+the main branch. These tags will be switched to stable release images after the support lands in a stable release.
+</Note>
+
+<Tabs>
+  <Tab title="Atlas 800I A3">
+
+```bash Command
+docker pull swr.cn-southwest-2.myhuaweicloud.com/base_image/dockerhub/lmsysorg/sglang:cann9.0.0-a3-glm5.2-20260615
+
+docker run -itd --shm-size=16g --name ${NAME} \
+--privileged=true --net=host \
+-v /var/queue_schedule:/var/queue_schedule \
+-v /etc/ascend_install.info:/etc/ascend_install.info \
+-v /usr/local/sbin:/usr/local/sbin \
+-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
+-v /usr/local/Ascend/firmware:/usr/local/Ascend/firmware \
+--device=/dev/davinci0:/dev/davinci0  \
+--device=/dev/davinci1:/dev/davinci1  \
+--device=/dev/davinci2:/dev/davinci2  \
+--device=/dev/davinci3:/dev/davinci3  \
+--device=/dev/davinci4:/dev/davinci4  \
+--device=/dev/davinci5:/dev/davinci5  \
+--device=/dev/davinci6:/dev/davinci6  \
+--device=/dev/davinci7:/dev/davinci7  \
+--device=/dev/davinci8:/dev/davinci8  \
+--device=/dev/davinci9:/dev/davinci9  \
+--device=/dev/davinci10:/dev/davinci10  \
+--device=/dev/davinci11:/dev/davinci11  \
+--device=/dev/davinci12:/dev/davinci12  \
+--device=/dev/davinci13:/dev/davinci13  \
+--device=/dev/davinci14:/dev/davinci14  \
+--device=/dev/davinci15:/dev/davinci15  \
+--device=/dev/davinci_manager:/dev/davinci_manager \
+--device=/dev/hisi_hdc:/dev/hisi_hdc \
+--entrypoint=bash \
+swr.cn-southwest-2.myhuaweicloud.com/base_image/dockerhub/lmsysorg/sglang:${TAG}
+```
+
+  </Tab>
+  <Tab title="Atlas 800I A2">
+
+```bash Command
+docker pull swr.cn-southwest-2.myhuaweicloud.com/base_image/dockerhub/lmsysorg/sglang:cann9.0.0-910b-glm5.2-20260615
+
+docker run -itd --shm-size=16g --name ${NAME} \
+--privileged=true --net=host \
+-v /var/queue_schedule:/var/queue_schedule \
+-v /etc/ascend_install.info:/etc/ascend_install.info \
+-v /usr/local/sbin:/usr/local/sbin \
+-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
+-v /usr/local/Ascend/firmware:/usr/local/Ascend/firmware \
+--device=/dev/davinci0:/dev/davinci0  \
+--device=/dev/davinci1:/dev/davinci1  \
+--device=/dev/davinci2:/dev/davinci2  \
+--device=/dev/davinci3:/dev/davinci3  \
+--device=/dev/davinci4:/dev/davinci4  \
+--device=/dev/davinci5:/dev/davinci5  \
+--device=/dev/davinci6:/dev/davinci6  \
+--device=/dev/davinci7:/dev/davinci7  \
+--device=/dev/davinci8:/dev/davinci8  \
+--device=/dev/davinci9:/dev/davinci9  \
+--device=/dev/davinci10:/dev/davinci10  \
+--device=/dev/davinci11:/dev/davinci11  \
+--device=/dev/davinci12:/dev/davinci12  \
+--device=/dev/davinci13:/dev/davinci13  \
+--device=/dev/davinci14:/dev/davinci14  \
+--device=/dev/davinci15:/dev/davinci15  \
+--device=/dev/davinci_manager:/dev/davinci_manager \
+--device=/dev/hisi_hdc:/dev/hisi_hdc \
+--entrypoint=bash \
+swr.cn-southwest-2.myhuaweicloud.com/base_image/dockerhub/lmsysorg/sglang:${TAG}
+```
+
+  </Tab>
+</Tabs>
+
+<Tip>
+- If the model weights have already been downloaded to a shared directory, use `-v` to mount the model path into the
+  container, for example: `-v /path/to/models:/models`.
+- Replace `${NAME}` with your own container name or remove `--name` to use default name.
+- Replace `${TAG}` with the image tag for the corresponding hardware platform.
+</Tip>
+
+## Online service deployment
+
+### Single-node deployment
+
+Quantized model `GLM-5.2-w8a8` can be deployed on one Atlas 800I A3 node or one Atlas 800I A2 node.
+
+<Tabs>
+  <Tab title="Atlas 800I A3">
+
+Run the following script to execute online inference.
+
+```shell
+# ============================================================
+# Before running, update the following variables:
+#   MODEL_PATH: path to the model weights directory
+# ============================================================
+
+# high performance cpu
+echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
+sysctl -w vm.swappiness=0
+sysctl -w kernel.numa_balancing=0
+sysctl -w kernel.sched_migration_cost_ns=50000
+# bind cpu
+export SGLANG_SET_CPU_AFFINITY=1
+
+unset https_proxy
+unset http_proxy
+unset HTTPS_PROXY
+unset HTTP_PROXY
+unset ASCEND_LAUNCH_BLOCKING
+# cann
+source /usr/local/Ascend/ascend-toolkit/set_env.sh
+source /usr/local/Ascend/nnal/atb/set_env.sh
+
+export STREAMS_PER_DEVICE=32
+export SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT=600
+export SGLANG_ENABLE_SPEC_V2=1
+# MTP OVERLAP
+export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
+export SGLANG_NPU_USE_MULTI_STREAM=1
+
+export HCCL_BUFFSIZE=1000
+export HCCL_OP_EXPANSION_MODE=AIV
+export HCCL_SOCKET_IFNAME=lo
+export GLOO_SOCKET_IFNAME=lo
+# DEEPEP
+export DEEPEP_NORMAL_LONG_SEQ_ROUND=72
+export DEEPEP_NORMAL_LONG_SEQ_PER_ROUND_TOKENS=1024
+export DEEPEP_NORMAL_COMBINE_ENABLE_LONG_SEQ=1
+export DEEP_NORMAL_MODE_USE_INT8_QUANT=1
+
+MODEL_PATH=/path/to/model-weights
+
+python3 -m sglang.launch_server \
+        --model-path $MODEL_PATH \
+        --attention-backend ascend \
+        --device npu \
+        --tp-size 16 --nnodes 1 --node-rank 0 \
+        --chunked-prefill-size 16384 --max-prefill-tokens 280000 \
+        --trust-remote-code \
+        --host 127.0.0.1 \
+        --mem-fraction-static 0.7 \
+        --port 8000 \
+        --served-model-name glm-5 \
+        --cuda-graph-bs 16 \
+        --quantization modelslim \
+        --speculative-draft-model-quantization unquant \
+        --speculative-algorithm NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4  \
+        --moe-a2a-backend deepep --deepep-mode auto
+```
+
+  </Tab>
+  <Tab title="Atlas 800I A2">
+
+Run the following script to execute online inference.
+
+```shell
+# ============================================================
+# Before running, update the following variables:
+#   MODEL_PATH: path to the model weights directory
+# ============================================================
+
+export SGLANG_SET_CPU_AFFINITY=1
+
+unset https_proxy
+unset http_proxy
+unset HTTPS_PROXY
+unset HTTP_PROXY
+unset ASCEND_LAUNCH_BLOCKING
+# cann
+source /usr/local/Ascend/ascend-toolkit/set_env.sh
+source /usr/local/Ascend/nnal/atb/set_env.sh
+
+export STREAMS_PER_DEVICE=32
+export SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT=600
+
+export HCCL_BUFFSIZE=1000
+export HCCL_SOCKET_IFNAME=lo
+export GLOO_SOCKET_IFNAME=lo
+export TRANSFORMERS_VERBOSITY=error
+
+#DEEPEP
+export DEEPEP_NORMAL_LONG_SEQ_ROUND=72
+export DEEPEP_NORMAL_LONG_SEQ_PER_ROUND_TOKENS=1024
+export DEEPEP_NORMAL_COMBINE_ENABLE_LONG_SEQ=1
+export DEEP_NORMAL_MODE_USE_INT8_QUANT=1
+
+MODEL_PATH=/path/to/model-weights
+
+python3 -m sglang.launch_server \
+        --model-path $MODEL_PATH \
+        --attention-backend ascend \
+        --device npu \
+        --tp-size 8 \
+        --nnodes 1 \
+        --dp-size 1 \
+        --enable-dp-attention \
+        --chunked-prefill-size -1 \
+        --max-prefill-tokens 65536 \
+        --trust-remote-code \
+        --mem-fraction-static 0.9 \
+        --served-model-name glm-5 \
+        --cuda-graph-bs 8 \
+        --max-running-requests 102 \
+        --quantization modelslim \
+        --speculative-draft-model-quantization unquant \
+        --moe-a2a-backend deepep --deepep-mode auto \
+        --load-balance-method round_robin
+```
+
+  </Tab>
+</Tabs>
+
+### Multi-node deployment
+
+Quantized model `GLM-5.2-w8a8` can be deployed on two Atlas 800I A3 nodes.
+
+Modify the IP addresses of the two nodes, then run the same script on both nodes.
+
+```shell
+# ============================================================
+# Before running, update the following variables:
+#   IPS: IP addresses of each node in the cluster
+#   IP_MASTER: rank 0 node IP address with port
+#   MODEL_PATH: path to the model weights directory
+#   HCCL_SOCKET_IFNAME: network interface name for HCCL
+#   GLOO_SOCKET_IFNAME: network interface name for Gloo
+# ============================================================
+
+echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
+sysctl -w vm.swappiness=0
+sysctl -w kernel.numa_balancing=0
+sysctl -w kernel.sched_migration_cost_ns=50000
+# bind cpu
+export SGLANG_SET_CPU_AFFINITY=1
+
+unset https_proxy
+unset http_proxy
+unset HTTPS_PROXY
+unset HTTP_PROXY
+unset ASCEND_LAUNCH_BLOCKING
+# cann
+source /usr/local/Ascend/ascend-toolkit/set_env.sh
+source /usr/local/Ascend/nnal/atb/set_env.sh
+
+export STREAMS_PER_DEVICE=32
+export SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT=600
+# MTP OVERLAP
+export SGLANG_ENABLE_SPEC_V2=1
+export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
+
+export SGLANG_NPU_USE_MULTI_STREAM=1
+export HCCL_BUFFSIZE=1000
+export HCCL_OP_EXPANSION_MODE=AIV
+
+# Run command ifconfig on two nodes, find out which inet addr has same IP with your node IP. That is your public interface, which should be added here
+export HCCL_SOCKET_IFNAME=lo
+export GLOO_SOCKET_IFNAME=lo
+
+# DEEPEP
+export DEEPEP_NORMAL_LONG_SEQ_ROUND=72
+export DEEPEP_NORMAL_LONG_SEQ_PER_ROUND_TOKENS=1024
+export DEEPEP_NORMAL_COMBINE_ENABLE_LONG_SEQ=1
+
+
+IPS=('<your node1 ip>' '<your node2 ip>')
+IP_MASTER="${IPS[0]}:5000"
+
+MODEL_PATH=/path/to/model-weights
+
+LOCAL_HOST1=`hostname -I|awk -F " " '{print$1}'`
+LOCAL_HOST2=`hostname -I|awk -F " " '{print$2}'`
+for i in "${!IPS[@]}";
+do
+    if [[ "$LOCAL_HOST1" == "${IPS[$i]}" || "$LOCAL_HOST2" == "${IPS[$i]}" ]];
+    then
+        echo "${IPS[$i]}"
+        python3 -m sglang.launch_server \
+        --model-path $MODEL_PATH \
+        --attention-backend ascend \
+        --device npu \
+        --tp-size 32 --nnodes 2 --node-rank $i --dist-init-addr $IP_MASTER \
+        --chunked-prefill-size 16384 --max-prefill-tokens 131072 \
+        --trust-remote-code \
+        --host 127.0.0.1 \
+        --mem-fraction-static 0.8 \
+        --port 8000 \
+        --served-model-name glm-5 \
+        --cuda-graph-max-bs 32 \
+        --moe-a2a-backend deepep \
+        --deepep-mode auto \
+        --speculative-draft-model-quantization unquant \
+        --speculative-algorithm NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4  \
+        --disable-radix-cache
+        NODE_RANK=$i
+        break
+    fi
+done
+```
+
+### Prefill-decode disaggregation deployment
+
+PD disaggregation splits the prefill and decode stages onto separate nodes, reducing interference and improving
+throughput for high-concurrency scenarios.
+
+```shell
+# ============================================================
+# Before running, update the following variables:
+#   ASCEND_MF_STORE_URL: prefill master IP address with port
+#   P_IP: prefill node IP address
+#   D_IP: decode node IP address
+#   MODEL_PATH: path to the model weights directory
+#   HCCL_SOCKET_IFNAME: network interface name for HCCL
+#   GLOO_SOCKET_IFNAME: network interface name for Gloo
+# ============================================================
+
+echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
+sysctl -w vm.swappiness=0
+sysctl -w kernel.numa_balancing=0
+sysctl -w kernel.sched_migration_cost_ns=50000
+
+export SGLANG_SET_CPU_AFFINITY=1
+
+unset https_proxy
+unset http_proxy
+unset HTTPS_PROXY
+unset HTTP_PROXY
+unset ASCEND_LAUNCH_BLOCKING
+source /usr/local/Ascend/ascend-toolkit/set_env.sh
+source /usr/local/Ascend/nnal/atb/set_env.sh
+
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export STREAMS_PER_DEVICE=32
+# pd transfer, prefill master IP
+export ASCEND_MF_STORE_URL="tcp://<your prefill ip>:24707"
+export SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT=600
+
+P_IP=('<your prefill ip>')
+D_IP=('<your decode ip>')
+
+MODEL_PATH=/path/to/model-weights
+
+export TRANSFORMERS_VERBOSITY=error
+
+LOCAL_HOST1=`hostname -I|awk -F " " '{print$1}'`
+LOCAL_HOST2=`hostname -I|awk -F " " '{print$2}'`
+echo "${LOCAL_HOST1}"
+echo "${LOCAL_HOST2}"
+
+# prefill
+for i in "${!P_IP[@]}";
+do
+    if [[ "$LOCAL_HOST1" == "${P_IP[$i]}" || "$LOCAL_HOST2" == "${P_IP[$i]}" ]];
+    then
+        echo "${P_IP[$i]}"
+        export DEEPEP_NORMAL_LONG_SEQ_ROUND=72
+        export DEEPEP_NORMAL_LONG_SEQ_PER_ROUND_TOKENS=1024
+        export DEEPEP_NORMAL_COMBINE_ENABLE_LONG_SEQ=1
+        export DEEP_NORMAL_MODE_USE_INT8_QUANT=1
+        export TASK_QUEUE_ENABLE=2
+        export HCCL_SOCKET_IFNAME=lo
+        export GLOO_SOCKET_IFNAME=lo
+
+        # prefill node
+        python -m sglang.launch_server --model-path ${MODEL_PATH}  --disaggregation-mode prefill --host ${P_IP[$i]} \
+        --port 8000 --disaggregation-bootstrap-port 8998 --trust-remote-code --nnodes 1 --node-rank $i \
+        --tp-size 16 --mem-fraction-static 0.8 --attention-backend ascend --device npu --quantization modelslim \
+        --disaggregation-transfer-backend ascend --max-running-requests 64 \
+        --served-model-name glm-5 --chunked-prefill-size 524288 --max-prefill-tokens 180000 --moe-a2a-backend deepep --deepep-mode normal \
+        --disable-shared-experts-fusion --disable-cuda-graph --dtype bfloat16 \
+        --dp-size 4 --enable-dp-attention \
+        --load-balance-method round_robin \
+        --enable-dp-lm-head --moe-dense-tp 1 \
+        --speculative-draft-model-quantization unquant \
+        --speculative-algorithm NEXTN --speculative-num-steps 1 --speculative-eagle-topk 1 --speculative-num-draft-tokens 2
+
+        # cp
+        #--enable-nsa-prefill-context-parallel \
+        #--nsa-prefill-cp-mode in-seq-split \
+        #--attn-cp-size 4 \
+        NODE_RANK=$i
+        break
+    fi
+done
+
+# decode
+for i in "${!D_IP[@]}";
+do
+    if [[ "$LOCAL_HOST1" == "${D_IP[$i]}" || "$LOCAL_HOST2" == "${D_IP[$i]}" ]];
+    then
+        echo "${D_IP[$i]}"
+
+        export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
+        export SGLANG_ENABLE_SPEC_V2=1
+        export HCCL_BUFFSIZE=650
+
+        export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=32
+        export TASK_QUEUE_ENABLE=0
+
+        export HCCL_SOCKET_IFNAME=lo
+        export GLOO_SOCKET_IFNAME=lo
+
+        export SGLANG_NPU_USE_MULTI_STREAM=1
+
+        python -m sglang.launch_server --model-path ${MODEL_PATH} --disaggregation-mode decode --host ${D_IP[$i]} \
+        --port 8003 --trust-remote-code --nnodes 1 --node-rank $i --tp-size 16 --dp-size 16 --ep-size 16 \
+        --mem-fraction-static 0.8 --max-running-requests 128 --attention-backend ascend --device npu --quantization modelslim \
+        --served-model-name glm-5 --moe-a2a-backend deepep --enable-dp-attention --deepep-mode low_latency \
+        --cuda-graph-max-bs 4 --disaggregation-transfer-backend ascend --watchdog-timeout 9000 --context-length 180000 \
+        --tokenizer-worker-num 4 --prefill-round-robin-balance --disable-shared-experts-fusion --dtype bfloat16  --load-balance-method round_robin \
+        --speculative-draft-model-quantization unquant \
+        --speculative-algorithm NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4
+        NODE_RANK=$i
+        break
+    fi
+done
+
+exit 1
+```
+
+Launch the router after the prefill and decode services are ready.
+
+```shell
+# ============================================================
+# Before running, update the following variables:
+#   P_MASTER_IP: prefill master IP address
+#   D_MASTER_IP: decode master IP address
+#   ROUTER_HOST_IP: router node IP address
+# ============================================================
+
+P_MASTER_IP="<your prefill ip>"
+D_MASTER_IP="<your decode ip>"
+ROUTER_HOST_IP="<your router ip>"
+
+python3 -m sglang_router.launch_router \
+--pd-disaggregation \
+--policy round_robin \
+--prefill http://${P_MASTER_IP}:8000 8998 \
+--decode http://${D_MASTER_IP}:8003 \
+--host ${ROUTER_HOST_IP} \
+--port 6688
+```
+
+## Functional verification
+
+After the service is started, you can invoke the model by sending a prompt:
+
+```shell
+# ============================================================
+# Before running, update the following variables:
+#   HOST: the server host address (e.g., localhost)
+#   PORT: the server port number (e.g., 8000)
+# ============================================================
+
+curl http://${HOST}:${PORT}/generate \
+    -H "Content-Type: application/json" \
+    -d '{
+        "text": "What is the capital of France?",
+        "sampling_params": {
+            "max_new_tokens": 64,
+            "temperature": 0
+        }
+    }'
+```
+
+Expected result: an HTTP 200 response with the generated text containing "Paris".
+
+Once the server prints `The server is fired up and ready to roll!` in the logs, it is ready to accept requests. For more
+testing examples (Health Check, Generate, Chat Completions, and port usage guidance),
+see [Testing the Service](/docs/hardware-platforms/ascend-npus/ascend_npu#testing-the-service).
+
+## Accuracy evaluation
+
+For accuracy evaluation methods and datasets, see [Accuracy Evaluation on Ascend NPU](/docs/hardware-platforms/ascend-npus/ascend_npu_accuracy_evaluation).
+
+## Performance
+
+For performance data and benchmark commands, see [Performance Testing on Ascend NPU](/docs/hardware-platforms/ascend-npus/ascend_npu_performance_testing).
+
+## Performance tuning
+
+For the full list of supported features, see [Supported features](#supported-features). For detailed optimization
+guidance, see [Optimization on Ascend NPU](/docs/hardware-platforms/ascend-npus/ascend_npu_optimization).
+
+## FAQ
+
+For common environment, installation, and general parameter issues, please refer to the [Ascend NPU FAQ](/docs/hardware-platforms/ascend-npus/ascend_npu_faq).
+This section only covers model-specific issues.