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+# DeepSeek-V3.1
+
+## Introduction
+
+DeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode. Compared to the previous version, this upgrade brings improvements in multiple aspects:
+
+- Hybrid thinking mode: One model supports both thinking mode and non-thinking mode by changing the chat template.
+
+- Smarter tool calling: Through post-training optimization, the model's performance in tool usage and agent tasks has significantly improved.
+
+- Higher thinking efficiency: DeepSeek-V3.1-Think achieves comparable answer quality to DeepSeek-R1-0528, while responding more quickly.
+
+The `DeepSeek-V3.1` model is first supported in `vllm-ascend:v0.9.1rc3`
+
+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](../user_guide/support_matrix/supported_models.md) to get the model's supported feature matrix.
+
+Refer to [feature guide](../user_guide/feature_guide/index.md) to get the feature's configuration.
+
+## Environment Preparation
+
+### Model Weight
+- `DeepSeek-V3.1`(BF16 version): [Download model weight](https://www.modelscope.cn/models/deepseek-ai/DeepSeek-V3.1)
+- `DeepSeek-V3.1-w8a8`(Quantized version without mtp): [Download model weight](https://www.modelscope.cn/models/vllm-ascend/DeepSeek-V3.1-w8a8).
+- `DeepSeek-V3.1_w8a8mix_mtp`(Quantized version with mix mtp): [Download model weight](https://www.modelscope.cn/models/Eco-Tech/DeepSeek-V3.1-w8a8). Please modify `torch_dtype` from `float16` to `bfloat16` in `config.json`.
+- `Method of Quantify`: [msmodelslim](https://gitcode.com/Ascend/msit/blob/master/msmodelslim/example/DeepSeek/README.md#deepseek-v31-w8a8-%E6%B7%B7%E5%90%88%E9%87%8F%E5%8C%96-mtp-%E9%87%8F%E5%8C%96). You can use these methods to quantify the model.
+
+It is recommended to download the model weight to the shared directory of multiple nodes, such as `/root/.cache/`
+
+### Verify Multi-node Communication(Optional)
+
+If you want to deploy multi-node environment, you need to verify multi-node communication according to [verify multi-node communication environment](../installation.md#verify-multi-node-communication).
+
+### Installation
+
+You can using our official docker image to run `DeepSeek-V3.1` directly.
+
+Select an image based on your machine type and start the docker image on your node, refer to [using docker](../installation.md#set-up-using-docker).
+
+```{code-block} bash
+   :substitutions:
+# Update --device according to your device (Atlas A2: /dev/davinci[0-7] Atlas A3:/dev/davinci[0-15]).
+# Update the vllm-ascend image according to your environment.
+# Note you should download the weight to /root/.cache in advance.
+# Update the vllm-ascend image
+export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version|
+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/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 /mnt/sfs_turbo/.cache:/root/.cache \
+-it $IMAGE bash
+```
+
+If you want to deploy multi-node environment, you need to set up environment on each node.
+
+## Deployment
+
+### Single-node Deployment
+
+- Quantized model `DeepSeek-V3.1_w8a8mix_mtp` can be deployed on 1 Atlas 800 A3 (64G × 16).
+
+Run the following script to execute online inference.
+
+```shell
+#!/bin/sh
+# this obtained through ifconfig
+# nic_name is the network interface name corresponding to local_ip of the current node
+nic_name="xxxx"
+local_ip="xxxx"
+
+# [Optional] jemalloc
+# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
+
+# AIV
+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=100
+export VLLM_USE_V1=1
+export HCCL_BUFFSIZE=200
+export VLLM_ASCEND_ENABLE_MLAPO=1
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export VLLM_ASCEND_ENABLE_FLASHCOMM1=0
+export DISABLE_L2_CACHE=1
+
+vllm serve /weights/DeepSeek-V3.1_w8a8mix_mtp \
+--host 0.0.0.0 \
+--port 8015 \
+--data-parallel-size 4 \
+--tensor-parallel-size 4 \
+--quantization ascend \
+--seed 1024 \
+--served-model-name deepseek_v3 \
+--enable-expert-parallel \
+--max-num-seqs 16 \
+--max-model-len 8192 \
+--max-num-batched-tokens 4096 \
+--trust-remote-code \
+--no-enable-prefix-caching \
+--gpu-memory-utilization 0.92 \
+--speculative-config '{"num_speculative_tokens": 1, "method": "deepseek_mtp"}' \
+--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
+--additional-config '{"torchair_graph_config":{"enabled":false}}'
+```
+
+### Multi-node Deployment
+
+- `DeepSeek-V3.1_w8a8mix_mtp`: require at least 2 Atlas 800 A2 (64G × 8).
+
+Run the following scripts on two nodes respectively.
+
+**Node 0**
+
+```shell
+#!/bin/sh
+
+# this obtained through ifconfig
+# nic_name is the network interface name corresponding to local_ip of the current node
+nic_name="xxxx"
+local_ip="xxxx"
+
+# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
+node0_ip="xxxx"
+
+# [Optional] jemalloc
+# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
+
+# AIV
+export HCCL_OP_EXPANSION_MODE="AIV"
+
+export HCCL_IF_IP=$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=100
+export VLLM_USE_V1=1
+export HCCL_BUFFSIZE=200
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export VLLM_ASCEND_ENABLE_MLAPO=1
+export HCCL_INTRA_PCIE_ENABLE=1
+export HCCL_INTRA_ROCE_ENABLE=0
+
+vllm serve /weights/DeepSeek-V3.1_w8a8mix_mtp \
+--host 0.0.0.0 \
+--port 8004 \
+--data-parallel-size 4 \
+--data-parallel-size-local 2 \
+--data-parallel-address $node0_ip \
+--data-parallel-rpc-port 13389 \
+--tensor-parallel-size 4 \
+--quantization ascend \
+--seed 1024 \
+--served-model-name deepseek_v3 \
+--enable-expert-parallel \
+--max-num-seqs 20 \
+--max-model-len 8192 \
+--max-num-batched-tokens 4096 \
+--trust-remote-code \
+--no-enable-prefix-caching \
+--gpu-memory-utilization 0.94 \
+--speculative-config '{"num_speculative_tokens": 1, "method": "deepseek_mtp"}' \
+--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
+--additional-config '{"torchair_graph_config":{"enabled":false}}'
+```
+
+**Node 1**
+
+```shell
+#!/bin/sh
+
+# this obtained through ifconfig
+# nic_name is the network interface name corresponding to local_ip of the current node
+nic_name="xxx"
+local_ip="xxx"
+
+# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
+node0_ip="xxxx"
+
+# [Optional] jemalloc
+# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
+
+# AIV
+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=100
+export HCCL_BUFFSIZE=200
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export VLLM_ASCEND_ENABLE_MLAPO=1
+export HCCL_INTRA_PCIE_ENABLE=1
+export HCCL_INTRA_ROCE_ENABLE=0
+
+vllm serve /weights/DeepSeek-V3.1_w8a8mix_mtp \
+--host 0.0.0.0 \
+--port 8004 \
+--headless \
+--data-parallel-size 4 \
+--data-parallel-size-local 2 \
+--data-parallel-start-rank 2 \
+--data-parallel-address $node0_ip \
+--data-parallel-rpc-port 13389 \
+--tensor-parallel-size 4 \
+--quantization ascend \
+--seed 1024 \
+--served-model-name deepseek_v3 \
+--enable-expert-parallel \
+--max-num-seqs 20 \
+--max-model-len 8192 \
+--max-num-batched-tokens 4096 \
+--trust-remote-code \
+--no-enable-prefix-caching \
+--gpu-memory-utilization 0.94 \
+--speculative-config '{"num_speculative_tokens": 1, "method": "deepseek_mtp"}' \
+--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
+--additional-config '{"torchair_graph_config":{"enabled":false}}'
+```
+
+### Prefill-Decode Disaggregation
+
+There are two ways to deploy `Prefill-Decode Disaggregation`: [Llmdatadist](./multi_node_pd_disaggregation_llmdatadist.md) and [Mooncake](./multi_node_pd_disaggregation_mooncake.md). We recommend use Mooncake for deploy.
+
+Take Atlas 800 A3 (64G × 16) for example, we recommend to deploy 2P1D (4 nodes) rather than 1P1D (2 nodes), because there is no enough NPU memory to serve high concurrency in 1P1D case.
+ - `DeepSeek-V3.1_w8a8mix_mtp 2P1D Layerwise` require 4 Atlas 800 A3 (64G × 16).
+
+To run the vllm-ascend `Prefill-Decode Disaggregation` service, you need to deploy a `launch_dp_program.py` script and a `run_dp_template.sh` script on each node and deploy a `proxy.sh` script on prefill master node to forward requests.
+
+1. `launch_dp_program.py` script for each node:
+
+```python
+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. Prefill Node 0 `run_dp_template.sh` script
+
+```shell
+# this obtained through ifconfig
+# nic_name is the network interface name corresponding to local_ip of the current node
+nic_name="xxx"
+local_ip="141.xx.xx.1"
+
+# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
+node0_ip="xxxx"
+
+# [Optional] jemalloc
+# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
+
+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_VERSION="0.11.0"
+export VLLM_RPC_TIMEOUT=3600000
+export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
+export HCCL_EXEC_TIMEOUT=204
+export HCCL_CONNECT_TIMEOUT=120
+
+
+export OMP_PROC_BIND=false
+export OMP_NUM_THREADS=10
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export VLLM_ASCEND_ENABLE_MLAPO=1
+export HCCL_BUFFSIZE=256
+export TASK_QUEUE_ENABLE=1
+export HCCL_OP_EXPANSION_MODE="AIV"
+export VLLM_USE_V1=1
+export ASCEND_RT_VISIBLE_DEVICE=$1
+export ASCEND_BUFFER_POOL=4:8
+export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH
+
+vllm serve /weights/DeepSeek-V3.1_w8a8mix_mtp \
+  --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 $7 \
+  --enable-expert-parallel \
+  --seed 1024 \
+  --served-model-name deepseek_v3 \
+  --max-model-len 40000 \
+  --max-num-batched-tokens 16384 \
+  --max-num-seqs 8 \
+  --enforce-eager \
+  --trust-remote-code \
+  --gpu-memory-utilization 0.9 \
+  --quantization ascend \
+  --no-enable-prefix-caching \
+  --speculative-config '{"num_speculative_tokens": 1, "method": "deepseek_mtp"}' \
+  --additional-config '{"recompute_scheduler_enable":true,"enable_shared_expert_dp": true}' \
+  --kv-transfer-config \
+  '{"kv_connector": "MooncakeConnector",
+  "kv_role": "kv_producer",
+  "kv_port": "30000",
+  "engine_id": "0",
+  "kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
+  "kv_connector_extra_config": {
+            "use_ascend_direct": true,
+            "prefill": {
+                    "dp_size": 2,
+                    "tp_size": 8
+             },
+             "decode": {
+                    "dp_size": 32,
+                    "tp_size": 1
+             }
+      }
+  }'
+```
+
+3. Prefill Node 1 `run_dp_template.sh` script
+
+```shell
+# this obtained through ifconfig
+# nic_name is the network interface name corresponding to local_ip of the current node
+nic_name="xxx"
+local_ip="141.xx.xx.2"
+
+# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
+node0_ip="xxxx"
+
+# [Optional] jemalloc
+# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
+
+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_VERSION="0.11.0"
+export VLLM_RPC_TIMEOUT=3600000
+export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
+export HCCL_EXEC_TIMEOUT=204
+export HCCL_CONNECT_TIMEOUT=120
+
+
+export OMP_PROC_BIND=false
+export OMP_NUM_THREADS=10
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export VLLM_ASCEND_ENABLE_MLAPO=1
+export HCCL_BUFFSIZE=256
+export TASK_QUEUE_ENABLE=1
+export HCCL_OP_EXPANSION_MODE="AIV"
+export VLLM_USE_V1=1
+export ASCEND_RT_VISIBLE_DEVICE=$1
+export ASCEND_BUFFER_POOL=4:8
+export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH
+
+vllm serve /weights/DeepSeek-V3.1_w8a8mix_mtp \
+  --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 $7 \
+  --enable-expert-parallel \
+  --seed 1024 \
+  --served-model-name deepseek_v3 \
+  --max-model-len 40000 \
+  --max-num-batched-tokens 16384 \
+  --max-num-seqs 8 \
+  --enforce-eager \
+  --trust-remote-code \
+  --gpu-memory-utilization 0.9 \
+  --quantization ascend \
+  --no-enable-prefix-caching \
+  --speculative-config '{"num_speculative_tokens": 1, "method": "deepseek_mtp"}' \
+  --additional-config '{"recompute_scheduler_enable":true,"enable_shared_expert_dp": true}' \
+  --kv-transfer-config \
+  '{"kv_connector": "MooncakeConnector",
+  "kv_role": "kv_producer",
+  "kv_port": "30100",
+  "engine_id": "1",
+  "kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
+  "kv_connector_extra_config": {
+            "use_ascend_direct": true,
+            "prefill": {
+                    "dp_size": 2,
+                    "tp_size": 8
+             },
+             "decode": {
+                    "dp_size": 32,
+                    "tp_size": 1
+             }
+      }
+  }'
+```
+
+4. Decode Node 0 `run_dp_template.sh` script
+
+```shell
+# this obtained through ifconfig
+# nic_name is the network interface name corresponding to local_ip of the current node
+nic_name="xxx"
+local_ip="141.xx.xx.3"
+
+# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
+node0_ip="xxxx"
+
+# [Optional] jemalloc
+# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
+
+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_VERSION="0.11.0"
+export VLLM_RPC_TIMEOUT=3600000
+export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
+export HCCL_EXEC_TIMEOUT=204
+export HCCL_CONNECT_TIMEOUT=120
+
+
+export OMP_PROC_BIND=false
+export OMP_NUM_THREADS=10
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export VLLM_ASCEND_ENABLE_MLAPO=1
+export HCCL_BUFFSIZE=600
+export TASK_QUEUE_ENABLE=1
+export HCCL_OP_EXPANSION_MODE="AIV"
+export VLLM_USE_V1=1
+export ASCEND_RT_VISIBLE_DEVICE=$1
+export ASCEND_BUFFER_POOL=4:8
+export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH
+
+vllm serve /weights/DeepSeek-V3.1_w8a8mix_mtp \
+  --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 $7 \
+  --enable-expert-parallel \
+  --seed 1024 \
+  --served-model-name deepseek_v3 \
+  --max-model-len 40000 \
+  --max-num-batched-tokens 256 \
+  --max-num-seqs 40 \
+  --trust-remote-code \
+  --gpu-memory-utilization 0.94 \
+  --quantization ascend \
+  --no-enable-prefix-caching \
+  --speculative-config '{"num_speculative_tokens": 1, "method": "deepseek_mtp"}' \
+  --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
+  --additional-config '{"recompute_scheduler_enable":true,"multistream_overlap_shared_expert": true,"lm_head_tensor_parallel_size":16}' \
+  --kv-transfer-config \
+  '{"kv_connector": "MooncakeConnector",
+  "kv_role": "kv_consumer",
+  "kv_port": "30200",
+  "engine_id": "2",
+  "kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
+  "kv_connector_extra_config": {
+            "use_ascend_direct": true,
+            "prefill": {
+                    "dp_size": 2,
+                    "tp_size": 8
+             },
+             "decode": {
+                    "dp_size": 32,
+                    "tp_size": 1
+             }
+      }
+  }'
+```
+
+5. Decode Node 1 `run_dp_template.sh` script
+
+```shell
+# this obtained through ifconfig
+# nic_name is the network interface name corresponding to local_ip of the current node
+nic_name="xxx"
+local_ip="141.xx.xx.4"
+
+# The value of node0_ip must be consistent with the value of local_ip set in node0 (master node)
+node0_ip="xxxx"
+
+# [Optional] jemalloc
+# jemalloc is for better performance, if `libjemalloc.so` is install on your machine, you can turn it on.
+# export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
+
+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_VERSION="0.11.0"
+export VLLM_RPC_TIMEOUT=3600000
+export VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS=30000
+export HCCL_EXEC_TIMEOUT=204
+export HCCL_CONNECT_TIMEOUT=120
+
+
+export OMP_PROC_BIND=false
+export OMP_NUM_THREADS=10
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export VLLM_ASCEND_ENABLE_MLAPO=1
+export HCCL_BUFFSIZE=600
+export TASK_QUEUE_ENABLE=1
+export HCCL_OP_EXPANSION_MODE="AIV"
+export VLLM_USE_V1=1
+export ASCEND_RT_VISIBLE_DEVICE=$1
+export ASCEND_BUFFER_POOL=4:8
+export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/latest/python/site-packages/mooncake:$LD_LIBRARY_PATH
+
+vllm serve /weights/DeepSeek-V3.1_w8a8mix_mtp \
+  --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 deepseek_v3 \
+  --max-model-len 40000 \
+  --max-num-batched-tokens 256 \
+  --max-num-seqs 40 \
+  --trust-remote-code \
+  --gpu-memory-utilization 0.94 \
+  --quantization ascend \
+  --no-enable-prefix-caching \
+  --speculative-config '{"num_speculative_tokens": 1, "method": "deepseek_mtp"}' \
+  --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
+  --additional-config '{"recompute_scheduler_enable":true,"multistream_overlap_shared_expert": true,"lm_head_tensor_parallel_size":16}' \
+  --kv-transfer-config \
+  '{"kv_connector": "MooncakeConnector",
+  "kv_role": "kv_consumer",
+  "kv_port": "30300",
+  "engine_id": "3",
+  "kv_connector_module_path": "vllm_ascend.distributed.mooncake_connector",
+  "kv_connector_extra_config": {
+            "use_ascend_direct": true,
+            "prefill": {
+                    "dp_size": 2,
+                    "tp_size": 8
+             },
+             "decode": {
+                    "dp_size": 32,
+                    "tp_size": 1
+             }
+      }
+  }'
+```
+
+6. run server for each node
+
+```shell
+# p0
+python launch_dp_program.py --dp-size 2 --tp-size 8 --dp-size-local 2 --dp-rank-start 0 --dp-address 141.xx.xx.1 --dp-rpc-port 12321 --vllm-start-port 7100
+# p1
+python launch_dp_program.py --dp-size 2 --tp-size 8 --dp-size-local 2 --dp-rank-start 0 --dp-address 141.xx.xx.2 --dp-rpc-port 12321 --vllm-start-port 7100
+# d0
+python launch_dp_program.py --dp-size 32 --tp-size 1 --dp-size-local 16 --dp-rank-start 0 --dp-address 141.xx.xx.3 --dp-rpc-port 12321 --vllm-start-port 7100
+# d1
+python launch_dp_program.py --dp-size 32 --tp-size 1 --dp-size-local 16 --dp-rank-start 16 --dp-address 141.xx.xx.3 --dp-rpc-port 12321 --vllm-start-port 7100
+```
+
+7. Prefill master node `proxy.sh` scripts
+
+```shell
+python load_balance_proxy_server_example.py \
+  --port 1999 \
+  --host 141.xx.xx.1 \
+  --prefiller-hosts \
+    141.xx.xx.1 \
+    141.xx.xx.1 \
+    141.xx.xx.2 \
+    141.xx.xx.2 \
+  --prefiller-ports \
+    7100 7101 7100 7101 \
+  --decoder-hosts \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.3 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+    141.xx.xx.4 \
+  --decoder-ports \
+    7100 7101 7102 7103 7104 7105 7106 7107 7108 7109 7110 7111 7112 7113 7114 7115 \
+    7100 7101 7102 7103 7104 7105 7106 7107 7108 7109 7110 7111 7112 7113 7114 7115 \
+```
+
+8. run proxy
+
+Run a proxy server on the same node with the prefiller service instance. You can get the proxy program in the repository's examples: [load\_balance\_proxy\_layerwise\_server\_example.py](https://github.com/vllm-project/vllm-ascend/blob/main/examples/disaggregated_prefill_v1/load_balance_proxy_layerwise_server_example.py) or [load\_balance\_proxy\_server\_example.py](https://github.com/vllm-project/vllm-ascend/blob/main/examples/disaggregated_prefill_v1/load_balance_proxy_server_example.py)
+
+```shell
+cd vllm-ascend/examples/disaggregated_prefill_v1/
+bash proxy.sh
+```
+
+## Functional Verification
+
+Once your server is started, you can query the model with input prompts:
+
+```shell
+curl http://<node0_ip>:<port>/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "deepseek_v3",
+        "prompt": "The future of AI is",
+        "max_tokens": 50,
+        "temperature": 0
+    }'
+```
+
+## Accuracy Evaluation
+
+Here are two accuracy evaluation methods.
+
+### Using AISBench
+1. Refer to [Using AISBench](../developer_guide/evaluation/using_ais_bench.md) for details.
+
+2. After execution, you can get the result, here is the result of `DeepSeek-V3.1_w8a8mix_mtp` in `vllm-ascend:0.11.0rc1` for reference only.
+
+| dataset | version | metric | mode | vllm-api-general-chat | note |
+|----- | ----- | ----- | ----- | -----| ----- |
+| ceval | - | accuracy | gen | 90.94 | 1 Atlas 800 A3 (64G × 16) |
+| gsm8k | - | accuracy | gen | 96.28 | 1 Atlas 800 A3 (64G × 16) |
+
+### Using Language Model Evaluation Harness
+Not test yet.
+
+## Performance
+
+### Using AISBench
+
+Refer to [Using AISBench for performance evaluation](../developer_guide/evaluation/using_ais_bench.md#execute-performance-evaluation) for details.
+
+### Using vLLM Benchmark
+
+Run performance evaluation of `DeepSeek-V3.1_w8a8mix_mtp` as an example.
+
+Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.
+
+There are three `vllm bench` subcommand:
+- `latency`: Benchmark the latency of a single batch of requests.
+- `serve`: Benchmark the online serving throughput.
+- `throughput`: Benchmark offline inference throughput.
+
+Take the `serve` as an example. Run the code as follows.
+
+```shell
+vllm bench serve --model vllm-ascend/DeepSeek-V3.1_w8a8mix_mtp  --dataset-name random --random-input 1024 --num-prompt 200 --request-rate 1 --save-result --result-dir ./
+```
+
+After about several minutes, you can get the performance evaluation result.
diff --git a/docs/source/tutorials/index.md b/docs/source/tutorials/index.md
index 7897fdf3..48a9f3c5 100644
--- a/docs/source/tutorials/index.md
+++ b/docs/source/tutorials/index.md
@@ -16,6 +16,7 @@ multi_npu_moge
 multi_npu_qwen3_moe
 multi_npu_quantization
 single_node_300i
+DeepSeek-V3.1.md
 DeepSeek-V3.2-Exp.md
 Qwen3-Coder-30B-A3B
 multi_node