diff --git a/docs/advanced_features/separate_reasoning.ipynb b/docs/advanced_features/separate_reasoning.ipynb
index 0c20c5a08bd2..56a28f03ceae 100644
--- a/docs/advanced_features/separate_reasoning.ipynb
+++ b/docs/advanced_features/separate_reasoning.ipynb
@@ -13,7 +13,7 @@
"| Model | Reasoning tags | Parser | Notes |\n",
"|---------|-----------------------------|------------------|-------|\n",
"| [DeepSeek‑R1 series](https://huggingface.co/collections/deepseek-ai/deepseek-r1-678e1e131c0169c0bc89728d) | `` … `` | `deepseek-r1` | Supports all variants (R1, R1-0528, R1-Distill) |\n",
- "| [DeepSeek‑V3.1](https://huggingface.co/deepseek-ai/DeepSeek-V3.1) | `` … `` | `deepseek-v3` | Supports `thinking` parameter |\n",
+ "| [DeepSeek‑V3 series](https://huggingface.co/deepseek-ai/DeepSeek-V3.1) | `` … `` | `deepseek-v3` | Including [DeepSeek‑V3.2](https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp). Supports `thinking` parameter |\n",
"| [Standard Qwen3 models](https://huggingface.co/collections/Qwen/qwen3-67dd247413f0e2e4f653967f) | `` … `` | `qwen3` | Supports `enable_thinking` parameter |\n",
"| [Qwen3-Thinking models](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) | `` … `` | `qwen3` or `qwen3-thinking` | Always generates thinking content |\n",
"| [Kimi models](https://huggingface.co/moonshotai/models) | `◁think▷` … `◁/think▷` | `kimi` | Uses special thinking delimiters |\n",
@@ -26,7 +26,7 @@
"- Both are handled by the same `deepseek-r1` parser\n",
"\n",
"**DeepSeek-V3 Family:**\n",
- "- DeepSeek-V3.1: Hybrid model supporting both thinking and non-thinking modes, use the `deepseek-v3` parser and `thinking` parameter (NOTE: not `enable_thinking`)\n",
+ "- DeepSeek-V3.1/V3.2: Hybrid model supporting both thinking and non-thinking modes, use the `deepseek-v3` parser and `thinking` parameter (NOTE: not `enable_thinking`)\n",
"\n",
"**Qwen3 Family:**\n",
"- Standard Qwen3 (e.g., Qwen3-2507): Use `qwen3` parser, supports `enable_thinking` in chat templates\n",
diff --git a/docs/basic_usage/deepseek.md b/docs/basic_usage/deepseek.md
index 96f43ab0a7a9..39d3f4ab6fbc 100644
--- a/docs/basic_usage/deepseek.md
+++ b/docs/basic_usage/deepseek.md
@@ -170,7 +170,7 @@ python3 -m sglang.launch_server \
- The best configuration for `--speculative-num-steps`, `--speculative-eagle-topk` and `--speculative-num-draft-tokens` can be searched with [bench_speculative.py](https://github.com/sgl-project/sglang/blob/main/scripts/playground/bench_speculative.py) script for given batch size. The minimum configuration is `--speculative-num-steps 1 --speculative-eagle-topk 1 --speculative-num-draft-tokens 2`, which can achieve speedup for larger batch sizes.
- FlashAttention3, FlashMLA, and Triton backend fully supports MTP usage. For FlashInfer backend (`--attention-backend flashinfer`) with speculative decoding,`--speculative-eagle-topk` parameter should be set to `1`. MTP support for the CutlassMLA and TRTLLM MLA backends are still under development.
- To enable DeepSeek MTP for large batch sizes (>32), there are some parameters should be changed (Reference [this discussion](https://github.com/sgl-project/sglang/issues/4543#issuecomment-2737413756)):
- - Adjust `--max-running-requests` to a larger number. The default value is `32` for MTP. For larger batch sizes, you should increase this value beyond the default value.
+ - Adjust `--max-running-requests` to a larger number. The default value is `48` for MTP. For larger batch sizes, you should increase this value beyond the default value.
- Set `--cuda-graph-bs`. It's a list of batch sizes for cuda graph capture. The default captured batch sizes for speculative decoding is set [here](https://github.com/sgl-project/sglang/blob/49420741746c8f3e80e0eb17e7d012bfaf25793a/python/sglang/srt/model_executor/cuda_graph_runner.py#L126). You can include more batch sizes into it.
diff --git a/docs/basic_usage/deepseek_v32.md b/docs/basic_usage/deepseek_v32.md
new file mode 100644
index 000000000000..bac87498b31a
--- /dev/null
+++ b/docs/basic_usage/deepseek_v32.md
@@ -0,0 +1,150 @@
+# DeepSeek V3.2 Usage
+
+[DeepSeek-V3.2-Exp](https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp) equips DeepSeek-V3.1-Terminus with DeepSeek Sparse Attention (DSA) through continued training. With DSA, a fine-grained sparse attention mechanism powered by a lightning indexer, DeepSeek-V3.2 achieves efficiency improvements in long-context scenarios.
+
+For reporting issues or tracking upcoming features, please refer to this [Roadmap](https://github.com/sgl-project/sglang/issues/11060).
+
+## Installation
+
+### Docker
+
+```bash
+# H200/B200
+docker pull lmsysorg/sglang:latest
+
+# MI350/MI355
+docker pull lmsysorg/sglang:dsv32-rocm
+
+# NPUs
+docker pull lmsysorg/sglang:dsv32-a2
+docker pull lmsysorg/sglang:dsv32-a3
+```
+
+### Build From Source
+
+```bash
+# Install SGLang
+git clone https://github.com/sgl-project/sglang
+cd sglang
+pip3 install pip --upgrade
+pip3 install -e "python[all]"
+
+# Install flash_mla
+git clone https://github.com/deepseek-ai/FlashMLA.git flash-mla
+cd flash-mla
+git submodule update --init --recursive
+pip install -v .
+```
+## Launch DeepSeek V3.2 with SGLang
+
+To serve DeepSeek-V3.2-Exp on 8xH200/B200 GPUs:
+
+```bash
+# Launch with TP + DP
+python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --dp 8 --enable-dp-attention
+
+# Launch with EP + DP
+python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --ep 8 --dp 8 --enable-dp-attention
+```
+
+### Configuration Tips
+- **DP Attention**: For DeepSeek V3.2 model, the kernels are customized for the use case of `dp_size=8`. So
+- **Choices of Attention Kernels**: The attention backend is automatically set to `nsa` attention backend for DeepSeek V3.2 model. In this backend, different kernels for sparse prefilling/decoding are implemented, which can be specified by `--nsa-prefill-backend` and `--nsa-decode-backend` server arguments. The choices of nsa prefill/decode attention kernels include:
+ - `flashmla_sparse`: `flash_mla_sparse_fwd` kernel from `flash_mla` library. Can run on both Hopper and Blackwell GPUs.
+ - `flashmla_kv`: `flash_mla_with_kvcache` kernel from `flash_mla` library. Can run on both Hopper and Blackwell GPUs.
+ - `fa3`: `flash_attn_with_kvcache` kernel from `flash_attn` library. Can only run on Hopper GPUs.
+ - `tilelang`: `tilelang` implementation that can run on GPU, HPU and NPU.
+ - `alter`: Alter kernel on AMD HPUs. Can only be used as decode kernel.
+- On the basis of performance benchmarks, the default configuration on H200 and B200 are set as follows :
+ - H200: `flashmla_sparse` prefill attention, `fa3` decode attention, `bf16` kv cache dtype.
+ - B200: `flashmla_kv` prefill attention, `flashmla_kv` decode attention, `fp8_e4m3` kv cache dtype.
+ - Currently we don't enable `prefill=flashmla_sparse` with `decode=flashmla_kv` due to latency caused by kv cache quantization operations. In the future we might shift to this setting after attention/quantization kernels are optimized.
+
+### Multi-token Prediction
+SGLang implements Multi-Token Prediction (MTP) for DeepSeek V3.2 based on [EAGLE speculative decoding](https://docs.sglang.ai/advanced_features/speculative_decoding.html#EAGLE-Decoding). With this optimization, the decoding speed can be improved significantly on small batch sizes. Please look at [this PR](https://github.com/sgl-project/sglang/pull/11652) for more information.
+
+Example usage:
+```bash
+python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --dp 8 --enable-dp-attention --speculative-algorithm EAGLE --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4
+```
+- The best configuration for `--speculative-num-steps`, `--speculative-eagle-topk` and `--speculative-num-draft-tokens` can be searched with [bench_speculative.py](https://github.com/sgl-project/sglang/blob/main/scripts/playground/bench_speculative.py) script for given batch size. The minimum configuration is `--speculative-num-steps 1 --speculative-eagle-topk 1 --speculative-num-draft-tokens 2`, which can achieve speedup for larger batch sizes.
+- The default value of `--max-running-requests` is set to `48` for MTP. For larger batch sizes, this value should be increased beyond the default value.
+
+
+# Function Calling and Reasoning Parser
+The usage of function calling and reasoning parser is the same as DeepSeek V3.1. Please refer to [Reasoning Parser](https://docs.sglang.ai/advanced_features/separate_reasoning.html) and [Tool Parser](https://docs.sglang.ai/advanced_features/tool_parser.html) documents.
+
+# PD Disaggregation
+
+Prefill Command:
+```bash
+python -m sglang.launch_server \
+ --model-path deepseek-ai/DeepSeek-V3.2-Exp \
+ --disaggregation-mode prefill \
+ --host $LOCAL_IP \
+ --port $PORT \
+ --tp 8 \
+ --dp 8 \
+ --enable-dp-attention \
+ --dist-init-addr ${HOST}:${DIST_PORT} \
+ --trust-remote-code \
+ --disaggregation-bootstrap-port 8998 \
+ --mem-fraction-static 0.9 \
+```
+
+Decode command:
+```bash
+python -m sglang.launch_server \
+ --model-path deepseek-ai/DeepSeek-V3.2-Exp \
+ --disaggregation-mode decode \
+ --host $LOCAL_IP \
+ --port $PORT \
+ --tp 8 \
+ --dp 8 \
+ --enable-dp-attention \
+ --dist-init-addr ${HOST}:${DIST_PORT} \
+ --trust-remote-code \
+ --mem-fraction-static 0.9 \
+```
+
+Router command:
+```bash
+python -m sglang_router.launch_router --pd-disaggregation \
+ --prefill $PREFILL_ADDR 8998 \
+ --decode $DECODE_ADDR \
+ --host 127.0.0.1 \
+ --port 8000 \
+```
+
+If you need more advanced deployment methods or production-ready deployment methods, such as RBG or LWS-based deployment, please refer to [references/multi_node_deployment/rbg_pd/deepseekv32_pd.md](../references/multi_node_deployment/rbg_pd/deepseekv32_pd.md). Additionally, you can also find startup commands for DeepEP-based EP parallelism in the aforementioned documentation.
+
+
+## Benchmarking Results
+
+### Accuracy Test with `gsm8k`
+A simple accuracy benchmark can be tested with `gsm8k` dataset:
+```bash
+python3 benchmark/gsm8k/bench_sglang.py --num-shots 8 --num-questions 1319 --parallel 1319
+```
+
+The result is 0.956, which matches our expectation:
+```bash
+Accuracy: 0.956
+Invalid: 0.000
+Latency: 25.109 s
+Output throughput: 5226.235 token/s
+```
+
+
+### Accuracy Test with `gpqa-diamond`
+
+Accuracy benchmark on long context can be tested on GPQA-diamond dataset with long output tokens and thinking enabled:
+```bash
+python3 -m sglang.test.run_eval --port 30000 --eval-name gpqa --num-examples 198 --max-tokens 120000 --repeat 8 --thinking-mode deepseek-v3
+```
+
+The mean accuracy over 8 runs shows 0.797, which matches the number 79.9 in official tech report.
+```bash
+Repeat: 8, mean: 0.797
+Scores: ['0.808', '0.798', '0.808', '0.798', '0.783', '0.788', '0.803', '0.793']
+```
diff --git a/docs/references/multi_node_deployment/rbg_pd/deepseekv32_pd.md b/docs/references/multi_node_deployment/rbg_pd/deepseekv32_pd.md
new file mode 100644
index 000000000000..30ff16ebcba7
--- /dev/null
+++ b/docs/references/multi_node_deployment/rbg_pd/deepseekv32_pd.md
@@ -0,0 +1,570 @@
+# DeepSeekV32-Exp RBG Based PD Deploy
+
+## 0. Prerequisites
+
+1. k8s >=1.26
+2. lws installed on k8s.
+3. rbg installed on k8s.
+
+For RBG installation, please refer to: https://github.com/sgl-project/rbg
+
+## 1. Image Preparation
+
+`lmsysorg/sglang:latest`
+
+
+### 2. All In One manifest file
+
+*Note: The NodeSelector section, model location section, and taint toleration section can be adjusted according to your actual deployment environment*
+
+rbg-dsv32.yml
+
+```yaml
+apiVersion: workloads.x-k8s.io/v1alpha1
+kind: RoleBasedGroup
+metadata:
+ name: deepseek-rbg-32exp
+ namespace: default
+spec:
+ roles:
+ - name: prefill
+ replicas: 1
+ workload:
+ apiVersion: leaderworkerset.x-k8s.io/v1
+ kind: LeaderWorkerSet
+ restartPolicy: None
+ leaderWorkerSet:
+ size: 1
+ patchLeaderTemplate:
+ metadata:
+ labels:
+ role: leader
+ pd_role: prefill
+ spec:
+ containers:
+ - command:
+ - python3
+ - -m
+ - sglang.launch_server
+ - --model-path
+ - /work/models
+ - --port
+ - "30000"
+ - --trust-remote
+ - --host
+ - 0.0.0.0
+ - --disable-radix-cache
+ - --disaggregation-ib-device
+ - mlx5_0,mlx5_1,mlx5_2,mlx5_3,mlx5_4,mlx5_5,mlx5_6,mlx5_7
+ - --disable-radix-cache
+ - --chunked-prefill-size
+ - "131072"
+ - --page-size
+ - "64"
+ # - --enable-eplb
+ - --ep-dispatch-algorithm
+ - dynamic
+ - --eplb-algorithm
+ - deepseek
+ - --enable-dp-lm-head
+ - --enable-dp-attention
+ - --dp-size
+ - "8"
+ - --moe-a2a-backend
+ - deepep
+ - --deepep-mode
+ - normal
+ - --disaggregation-mode
+ - prefill
+ - --mem-fraction-static
+ - "0.8"
+ - --max-prefill-tokens
+ - "32768"
+ - --context-length
+ - "32768"
+ - --tp
+ - "8"
+ - --dist-init-addr
+ - $(LWS_LEADER_ADDRESS):20102
+ - --nnodes
+ - $(LWS_GROUP_SIZE)
+ - --node-rank
+ - $(LWS_WORKER_INDEX)
+ - --trust-remote-code
+ - --ep-num-redundant-experts
+ - "32"
+ - --moe-dense-tp-size
+ - "1"
+ - --max-running-requests
+ - "1024"
+ env:
+ - name: LWS_WORKER_INDEX
+ valueFrom:
+ fieldRef:
+ fieldPath: metadata.labels['leaderworkerset.sigs.k8s.io/worker-index']
+ livenessProbe:
+ failureThreshold: 3000
+ httpGet:
+ path: /health
+ port: 30000
+ initialDelaySeconds: 300
+ periodSeconds: 60
+ successThreshold: 1
+ timeoutSeconds: 10
+ readinessProbe:
+ failureThreshold: 20
+ httpGet:
+ path: /health
+ port: 30000
+ periodSeconds: 30
+ successThreshold: 1
+ timeoutSeconds: 10
+ name: sglang
+ ports:
+ - containerPort: 30000
+ name: sglang-http
+ protocol: TCP
+
+ patchWorkerTemplate: {}
+ template:
+ metadata:
+ labels:
+ inference-framework: sglang
+ inference-stack.io/monitoring: "enabled"
+ spec:
+ containers:
+ - name: sglang
+ image: lmsysorg/sglang:latest
+ env:
+ - name: SGLANG_SKIP_SGL_KERNEL_VERSION_CHECK
+ value: "1"
+ - name: CUDA_LAUNCH_BLOCKING
+ value: "0"
+ - name: SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT
+ value: "1000000000"
+ - name: NVSHMEM_IB_TRAFFIC_CLASS
+ value: "16"
+ - name: NVSHMEM_DISABLE_P2P
+ value: "0"
+ - name: ENABLE_METRICS
+ value: "true"
+ - name: NVSHMEM_IB_GID_INDEX
+ value: "3"
+ - name: NVSHMEM_IB_SL
+ value: "5"
+ - name: SGLANG_SET_CPU_AFFINITY
+ value: "true"
+ - name: SGL_ENABLE_JIT_DEEPGEMM
+ value: "1"
+ - name: NCCL_IB_QPS_PER_CONNECTION
+ value: "8"
+ - name: NCCL_IB_SPLIT_DATA_ON_QPS
+ value: "1"
+ - name: NCCL_NET_PLUGIN
+ value: "none"
+ - name: NCCL_IB_TC
+ value: "136"
+ - name: NCCL_IB_SL
+ value: "5"
+ - name: NCCL_IB_TIMEOUT
+ value: "22"
+ - name: NCCL_IB_GID_INDEX
+ value: "3"
+ - name: NCCL_MIN_NCHANNELS
+ value: "4"
+ - name: NCCL_SOCKET_IFNAME
+ value: bond0
+ - name: GLOO_SOCKET_IFNAME
+ value: bond0
+ - name: NCCL_IB_HCA
+ value: ^=mlx5_0,mlx5_5,mlx5_6
+ - name: NVSHMEM_BOOTSTRAP_UID_SOCK_IFNAME
+ value: "bond0"
+ - name: MC_TE_METRIC
+ value: "false"
+ resources:
+ limits:
+ nvidia.com/gpu: "8"
+ securityContext:
+ capabilities:
+ add:
+ - IPC_LOCK
+ privileged: true
+ volumeMounts:
+ - mountPath: /root/.cache
+ name: sgl-cache
+ - mountPath: /dev/shm
+ name: dshm
+ - mountPath: /work/models
+ name: model
+ - mountPath: /dev/infiniband
+ name: ib
+ - mountPath: /sgl-workspace/sglang
+ name: src
+
+ dnsPolicy: ClusterFirstWithHostNet
+ hostIPC: true
+ hostNetwork: true
+ nodeSelector:
+ pd: "yes"
+ tolerations:
+ - key: pd
+ operator: Exists
+ volumes:
+ - hostPath:
+ path: /var/run/sys-topology
+ name: topo
+ - hostPath:
+ path: /data1/sgl_cache4
+ type: DirectoryOrCreate
+ name: sgl-cache
+ - emptyDir:
+ medium: Memory
+ name: dshm
+ - hostPath:
+ path: /data/DeepSeek-V3.2-Exp
+ name: model
+ - hostPath:
+ path: /dev/infiniband
+ name: ib
+ - hostPath:
+ path: /data/src/sglang
+ type: DirectoryOrCreate
+ name: src
+
+ - name: decode
+ replicas: 1
+ workload:
+ apiVersion: leaderworkerset.x-k8s.io/v1
+ kind: LeaderWorkerSet
+ leaderWorkerSet:
+ size: 1
+ patchLeaderTemplate:
+ metadata:
+ labels:
+ role: leader
+ pd_role: decode
+ spec:
+ containers:
+ - command:
+ - python3
+ - -m
+ - sglang.launch_server
+ - --model-path
+ - /work/models
+ - --port
+ - "30000"
+ - --trust-remote
+ - --host
+ - 0.0.0.0
+ - --disaggregation-ib-device
+ - mlx5_0,mlx5_1,mlx5_2,mlx5_3,mlx5_4,mlx5_5,mlx5_6,mlx5_7
+ - --chunked-prefill-size
+ - "131072"
+ - --prefill-round-robin-balance
+ - --eplb-rebalance-layers-per-chunk
+ - "29"
+ - --page-size
+ - "64"
+ - --enable-dp-attention
+ - --enable-dp-lm-head
+ - --dp-size
+ - "8"
+ - --moe-a2a-backend
+ - deepep
+ - --deepep-mode
+ - low_latency
+ - --disaggregation-mode
+ - decode
+ - --mem-fraction-static
+ - "0.8"
+ - --context-length
+ - "32768"
+ - --max-running-requests
+ - "2048"
+ - --tp-size
+ - "8" # Size of Tensor Parallelism
+ - --cuda-graph-max-bs
+ - "16"
+ - --dist-init-addr
+ - $(LWS_LEADER_ADDRESS):20102
+ - --nnodes
+ - $(LWS_GROUP_SIZE)
+ - --node-rank
+ - $(LWS_WORKER_INDEX)
+ - --trust-remote-code
+ - --ep-num-redundant-experts
+ - "32"
+ - --moe-dense-tp-size
+ - "1"
+ env:
+ - name: LWS_WORKER_INDEX
+ valueFrom:
+ fieldRef:
+ fieldPath: metadata.labels['leaderworkerset.sigs.k8s.io/worker-index']
+ livenessProbe:
+ failureThreshold: 30000
+ httpGet:
+ path: /health
+ port: 30000
+ initialDelaySeconds: 300
+ periodSeconds: 60
+ successThreshold: 1
+ timeoutSeconds: 10
+ name: sglang
+ readinessProbe:
+ failureThreshold: 20
+ httpGet:
+ path: /health
+ port: 30000
+ periodSeconds: 30
+ successThreshold: 1
+ timeoutSeconds: 10
+ patchWorkerTemplate:
+ spec:
+ containers:
+ - command:
+ - python3
+ - -m
+ - sglang.launch_server
+ - --model-path
+ - /work/models
+ - --crash-dump-folder
+ - /log
+ - --chunked-prefill-size
+ - "262144"
+ - --prefill-round-robin-balance
+ - --eplb-rebalance-layers-per-chunk
+ - "29"
+ - --page-size
+ - "64"
+ - --enable-dp-attention
+ - --enable-dp-lm-head
+ - --dp-size
+ - "32"
+ - --moe-a2a-backend
+ - "deepep"
+ - --deepep-mode
+ - low_latency
+ - --disaggregation-mode
+ - decode
+ - --mem-fraction-static
+ - "0.849"
+ - --context-length
+ - "32768"
+ - --disaggregation-ib-device
+ - mlx5_0,mlx5_1,mlx5_2,mlx5_3,mlx5_4,mlx5_5,mlx5_6,mlx5_7
+ - --max-running-requests
+ - "4096"
+ - --cuda-graph-max-bs
+ - "16"
+ - --tp-size
+ - "8" # Size of Tensor Parallelism
+ - --dist-init-addr
+ - $(LWS_LEADER_ADDRESS):20102
+ - --nnodes
+ - $(LWS_GROUP_SIZE)
+ - --node-rank
+ - $(LWS_WORKER_INDEX)
+ - --trust-remote-code
+ - --ep-num-redundant-experts
+ - "32"
+ - --moe-dense-tp-size
+ - "1"
+ env:
+ - name: LWS_WORKER_INDEX
+ valueFrom:
+ fieldRef:
+ fieldPath: metadata.labels['leaderworkerset.sigs.k8s.io/worker-index']
+ name: sglang
+ template:
+ metadata:
+ labels:
+ inference-framework: sglang-unuse
+ inference-stack.io/monitoring: "enabled"
+ spec:
+ containers:
+ - image: lmsysorg/sglang:latest
+ name: sglang
+ resources:
+ limits:
+ nvidia.com/gpu: "8"
+ securityContext:
+ capabilities:
+ add:
+ - IPC_LOCK
+ privileged: true
+ volumeMounts:
+ - mountPath: /root/.cache
+ name: sgl-cache
+ - mountPath: /dev/shm
+ name: dshm
+ - mountPath: /work/models
+ name: model
+ - mountPath: /dev/infiniband
+ name: ib
+ - mountPath: /sgl-workspace/sglang
+ name: src
+ env:
+ - name: SGLANG_SKIP_SGL_KERNEL_VERSION_CHECK
+ value: "1"
+ - name: SGLANG_DISAGGREGATION_WAITING_TIMEOUT
+ value: "100000000"
+ - name: NVSHMEM_DISABLE_P2P
+ value: "0"
+ - name: NVSHMEM_IB_TRAFFIC_CLASS
+ value: "16"
+ - name: NVSHMEM_IB_SL
+ value: "5"
+ - name: ENABLE_METRICS
+ value: "true"
+ - name: CUDA_LAUNCH_BLOCKING
+ value: "0"
+ - name: NVSHMEM_IB_GID_INDEX
+ value: "3"
+ - name: NCCL_IB_QPS_PER_CONNECTION
+ value: "8"
+ - name: NCCL_IB_SPLIT_DATA_ON_QPS
+ value: "1"
+ - name: NCCL_NET_PLUGIN
+ value: "none"
+ - name: NCCL_IB_TC
+ value: "136"
+ - name: NCCL_IB_SL
+ value: "5"
+ - name: NCCL_IB_TIMEOUT
+ value: "22"
+ - name: NCCL_IB_GID_INDEX
+ value: "3"
+ - name: NCCL_MIN_NCHANNELS
+ value: "4"
+ - name: NCCL_SOCKET_IFNAME
+ value: bond0
+ - name: GLOO_SOCKET_IFNAME
+ value: bond0
+ - name: NVSHMEM_BOOTSTRAP_UID_SOCK_IFNAME
+ value: "bond0"
+ - name: NCCL_IB_HCA
+ value: ^=mlx5_0,mlx5_5,mlx5_6
+ - name: MC_TE_METRIC
+ value: "false"
+ - name: SGL_ENABLE_JIT_DEEPGEMM
+ value: "1"
+ dnsPolicy: ClusterFirstWithHostNet
+ hostIPC: true
+ hostNetwork: true
+ nodeSelector:
+ pd: "yes"
+ tolerations:
+ - key: pd
+ operator: Exists
+ volumes:
+ - hostPath:
+ path: /var/run/sys-topology
+ name: topo
+ - hostPath:
+ path: /data1/sgl_cache4
+ type: DirectoryOrCreate
+ name: sgl-cache
+ - hostPath:
+ path: /data/src/sglang
+ type: DirectoryOrCreate
+ name: src
+ - emptyDir:
+ medium: Memory
+ name: dshm
+ - hostPath:
+ path: /data/DeepSeek-V3.2-Exp
+ name: model
+ - hostPath:
+ path: /dev/infiniband
+ name: ib
+ - name: router
+ replicas: 1
+ dependencies: [ "decode", "prefill" ]
+ template:
+ spec:
+ containers:
+ - name: scheduler
+ image: lmsysorg/sglang:latest
+ command:
+ - sh
+ - -c
+ - >
+ python3 -m sglang_router.launch_router
+ --host 0.0.0.0
+ --port 8080
+ --pd-disaggregation
+ --policy random
+ --service-discovery
+ --service-discovery-namespace ${NAMESPACE}
+ --service-discovery-port 30000
+ --prefill-selector pd_role=prefill
+ --decode-selector pd_role=decode
+ --max-payload-size 2147483648
+ --worker-startup-timeout-secs 1200
+ env:
+ - name: NAMESPACE
+ valueFrom:
+ fieldRef:
+ apiVersion: v1
+ fieldPath: metadata.namespace
+---
+apiVersion: v1
+kind: Service
+metadata:
+ labels:
+ app: deepseek-rbg-32exp
+ name: deepseek-rbg-32exp
+ namespace: default
+spec:
+ ports:
+ - name: http
+ port: 8080
+ protocol: TCP
+ targetPort: 8080
+ nodePort: 30080
+
+ selector:
+ rolebasedgroup.workloads.x-k8s.io/name: deepseek-rbg-32exp
+ rolebasedgroup.workloads.x-k8s.io/role: router
+ type: NodePort
+
+```
+
+```bash
+[root@ecs-001]# kubectl get po -n default
+deepseek-rbg-32exp-decode-main-0 1/1 Running 0 74m
+deepseek-rbg-32exp-decode-0-1 1/1 Running 0 74m
+deepseek-rbg-32exp-router-9c5dbfc57 1/1 Running 0 22m
+deepseek-rbg-32exp-prefill-0 1/1 Running 0 74m
+
+[root@ecs-cbm-x1-pd-cpu-001 main_doc]# kubectl get svc |grep dee
+deepseek-rbg-32exp-decode ClusterIP None 97m
+deepseek-rbg-32exp-router-service NodePort 172.16.242.169 8000:30800/TCP 22m
+deepseek-rbg-32exp-prefill ClusterIP None 97m
+```
+
+At this point, select a nodePort:30800 to access:
+
+```bash
+[root@ecs-001]# curl -X POST "http://{nodePort}:30800/v1/chat/completions" \
+> -H "Content-Type: application/json" \
+> -H "Authorization: Bearer None" \
+> -d '{
+> "rid":"ccccdd",
+> "model": "dsv32",
+> "messages": [
+> {"role": "system", "content": "0: You are a helpful AI assistant"},
+> {"role": "user", "content": "你是谁？."}
+> ],
+> "max_tokens":221
+> }'
+{"id":"ccccdd","object":"chat.completion","created":1750252498,"model":"qwen2","choices":[{"index":0,"message":{"role":"assistant","content":"\n嗯，用户问了一个很基础的自我介绍问题"你是谁？"。这可能是第一次互动时的常规开场白，也可能是想确认我的身份和功能范围。\n\n用户没有提供任何背景信息，语气简洁中性。这种场景下新用户的可能性较高，需要给出清晰友好的自我介绍，同时突出实用价值来降低陌生感。\n\n考虑到中文用户，应该用简体中文回复。重点要说明三点：身份归属（深度求索）、功能定位（AI助手）、服务范围（学习/工作/生活）。结尾用开放性问题引导对话很关键——既能了解需求，又能避免让用户面对空白输入框时不知所措。\n\n用波浪线结尾可以软化语气，那个笑脸表情😊刚好能中和AI的机械感。不过要控制表情符号数量，避免显得轻浮。\n\n你好呀！我是你的AI助手，由深度求索公司（DeepSeek）开发的语言模型，名字叫 **DeepSeek-V32**。你可以把我当成一个知识丰富、随叫随到的小帮手～😊\n\n我的任务就是陪你聊天、解答问题、","reasoning_content":null,"tool_calls":null},"logprobs":null,"finish_reason":"length","matched_stop":null}],"usage":{"prompt_tokens":14,"total_tokens":235,"completion_tokens":221,"prompt_tokens_details":null}}
+
+```
+## FAQ
+
+1. The current deployment startup parameters may not be fully compatible with all RDMA scenarios. Different RDMA NCCL-related environment configurations may be needed in different network environments.
+
+2. Please ensure that the sglang code in the image has incorporated the changes from [PR #10912](https://github.com/sgl-project/sglang/pull/10912).