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+# DeepSeek-R1
+
+## Introduction
+
+DeepSeek-R1 is a high-performance Mixture-of-Experts (MoE) large language model developed by DeepSeek Company. It excels in complex logical reasoning, mathematical problem-solving, and code generation. By dynamically activating its expert networks, it delivers exceptional performance while maintaining computational efficiency. Building upon R1, DeepSeek-R1-W8A8 is a fully quantized version of the model. It employs 8-bit integer (INT8) quantization for both weights and activations, which significantly reduces the model's memory footprint and computational requirements, enabling more efficient deployment and application in resource-constrained environments.
+This article takes the deepseek- R1-W8A8 version as an example to introduce the deployment of the R1 series models.
+
+## 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-R1-W8A8`(Quantized version): require 1 Atlas 800 A3 (64G × 16) nodes or 2 Atlas 800 A2 (64G × 8) nodes. [Download model weight](https://www.modelscope.cn/models/vllm-ascend/DeepSeek-R1-W8A8)
+
+It is recommended to download the model weight to the shared directory of multiple nodes.
+
+### 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-R1-W8A8` 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 /etc/hccn.conf:/etc/hccn.conf \
+-v /usr/bin/hccn_tool:/usr/bin/hccn_tool \
+-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 \
+-it $IMAGE bash
+```
+
+If you want to deploy multi-node environment, you need to set up environment on each node.
+
+## Deployment
+### Service-oriented  Deployment
+
+- `DeepSeek-R1-W8A8`: require 1 Atlas 800 A3 (64G × 16) nodes or 2 Atlas 800 A2 (64G × 8).
+
+:::::{tab-set}
+:sync-group: install
+
+::::{tab-item} DeepSeek-R1-W8A8 A3 series
+
+```shell
+#!/bin/sh
+
+# this obtained through ifconfig
+# nic_name is the network interface name corresponding to local_ip of the current node
+nic_name="xxxx"
+local_ip="xxxx"
+
+# AIV
+export HCCL_OP_EXPANSION_MODE="AIV"
+export HCCL_IF_IP=$local_ip
+export GLOO_SOCKET_IFNAME=$nic_name
+export TP_SOCKET_IFNAME=$nic_name
+export HCCL_SOCKET_IFNAME=$nic_name
+export VLLM_ASCEND_ENABLE_MLAPO=1
+export VLLM_USE_MODELSCOPE=True
+
+vllm serve vllm-ascend/DeepSeek-R1-W8A8 \
+  --host 0.0.0.0 \
+  --port 8000 \
+  --data-parallel-size 4 \
+  --tensor-parallel-size 4 \
+  --quantization ascend \
+  --seed 1024 \
+  --served-model-name deepseek_r1 \
+  --enable-expert-parallel \
+  --max-num-seqs 16 \
+  --max-model-len 16384 \
+  --max-num-batched-tokens 4096 \
+  --trust-remote-code \
+  --gpu-memory-utilization 0.92 \
+  --speculative-config '{"num_speculative_tokens":1,"method":"mtp"}' \
+  --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}'
+```
+
+::::
+::::{tab-item} DeepSeek-R1-W8A8 A2 series
+
+Run the following scripts on two nodes respectively.
+
+**Node 0**
+
+```shell
+#!/bin/sh
+
+# this obtained through ifconfig
+# nic_name is the network interface name corresponding to local_ip of the current node
+nic_name="xxxx"
+local_ip="xxxx"
+
+# AIV
+export HCCL_OP_EXPANSION_MODE="AIV"
+export HCCL_IF_IP=$local_ip
+export GLOO_SOCKET_IFNAME=$nic_name
+export TP_SOCKET_IFNAME=$nic_name
+export HCCL_SOCKET_IFNAME=$nic_name
+export 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
+export VLLM_USE_MODELSCOPE=True
+
+vllm serve vllm-ascend/DeepSeek-R1-W8A8 \
+  --host 0.0.0.0 \
+  --port 8000 \
+  --data-parallel-size 4 \
+  --data-parallel-size-local 2 \
+  --data-parallel-address $local_ip \
+  --data-parallel-rpc-port 13389 \
+  --tensor-parallel-size 4 \
+  --quantization ascend \
+  --seed 1024 \
+  --served-model-name deepseek_r1 \
+  --enable-expert-parallel \
+  --max-num-seqs 16 \
+  --max-model-len 16384 \
+  --max-num-batched-tokens 4096 \
+  --trust-remote-code \
+  --gpu-memory-utilization 0.94 \
+  --speculative-config '{"num_speculative_tokens":1,"method":"mtp"}' \
+  --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}' 
+```
+
+**Node 1**
+
+```shell
+#!/bin/sh
+
+# this obtained through ifconfig
+# nic_name is the network interface name corresponding to local_ip of the current node
+nic_name="xxxx"
+local_ip="xxxx"
+node0_ip="xxxx" # same as the local_IP address in node 0
+
+# AIV
+export HCCL_OP_EXPANSION_MODE="AIV"
+export HCCL_IF_IP=$local_ip
+export GLOO_SOCKET_IFNAME=$nic_name
+export TP_SOCKET_IFNAME=$nic_name
+export HCCL_SOCKET_IFNAME=$nic_name
+export 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
+export VLLM_USE_MODELSCOPE=True
+
+vllm serve vllm-ascend/DeepSeek-R1-W8A8 \
+  --host 0.0.0.0 \
+  --port 8000 \
+  --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_r1 \
+  --enable-expert-parallel \
+  --max-num-seqs 16 \
+  --max-model-len 16384 \
+  --max-num-batched-tokens 4096 \
+  --trust-remote-code \
+  --gpu-memory-utilization 0.94 \
+  --speculative-config '{"num_speculative_tokens":1,"method":"mtp"}' \
+  --compilation-config '{"cudagraph_mode":"FULL_DECODE_ONLY"}'
+```
+
+::::
+:::::
+
+### Prefill-Decode Disaggregation
+
+We recommend using Mooncake for deployment: [Mooncake](./multi_node_pd_disaggregation_mooncake.md).
+
+This solution has been tested and demonstrates excellent performance.
+
+## 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_r1",
+        "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-R1-W8A8` in `vllm-ascend:0.11.0rc2` for reference only.
+
+| dataset | version | metric | mode | vllm-api-general-chat |
+|----- | ----- | ----- | ----- | -----|
+| aime2024dataset | - | accuracy | gen | 80.00 |
+| gpqadataset | - | accuracy | gen | 72.22 |
+
+### Using Language Model Evaluation Harness
+
+As an example, take the `gsm8k` dataset as a test dataset, and run accuracy evaluation of `DeepSeek-R1-W8A8` in online mode.
+
+1. Refer to [Using lm_eval](../developer_guide/evaluation/using_lm_eval.md) for `lm_eval` installation.
+
+2. Run `lm_eval` to execute the accuracy evaluation.
+
+```shell
+lm_eval \
+  --model local-completions \
+  --model_args model=path/DeepSeek-R1-W8A8,base_url=http://<node0_ip>:<port>/v1/completions,tokenized_requests=False,trust_remote_code=True \
+  --tasks gsm8k \
+  --output_path ./
+```
+
+3. After execution, you can get the result.
+
+## 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-R1-W8A8` 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
+export VLLM_USE_MODELSCOPE=true
+vllm bench serve --model path/DeepSeek-R1-W8A8  --dataset-name random --random-input 200 --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 7309656a..c3032a8a 100644
--- a/docs/source/tutorials/index.md
+++ b/docs/source/tutorials/index.md
@@ -18,6 +18,7 @@ Qwen3-Dense
 multi_npu_qwen3_moe
 multi_npu_quantization
 single_node_300i
+DeepSeek-R1.md
 DeepSeek-V3.1.md
 DeepSeek-V3.2-Exp.md
 Qwen3-235B-A22B.md