# Running DeepSeek-R1 Disaggregated with WideEP on GB200s Dynamo supports SGLang's GB200 implementation of wide expert parallelism and large scale P/D for DeepSeek-R1! You can read their blog post [here](https://lmsys.org/blog/2025-06-16-gb200-part-1/) for more details. We provide a Dockerfile for this in `container/Dockerfile.sglang-wideep` and a sample configuration that demonstrates WideEP and P/D disaggregation. To run the exact configuration shown in the blog post, you can view the commands created by the SGLang team [here](https://github.com/sgl-project/sglang/issues/7227). In this example, we will run 1 prefill worker on 2 GB200 nodes (4 GPUs each) and 1 decode worker on 2 GB200 nodes (total 8 GPUs). ## Instructions 1. Build the Dynamo container using the latest published dynamo version and stable sglang version. If you want to build from a local dynamo repo, you can add `--build-arg BRANCH_TYPE=local` to the build command. If you want to build from a remote dynamo repo, you can add `--build-arg BRANCH_TYPE=remote` to the build command. If you want to use a specific tag for the default sglang version, you can add `--build-arg SGLANG_IMAGE_TAG=` to the build command. > [!Note] > Please ensure that you are building this on an ARM64 machine. The correct SGLang image will be selected automatically via the multi-arch manifest. > [!Note] > Please use `--build-arg SGLANG_IMAGE_TAG=nightly-dev-20251019-fda0cb2a` to build the container due to a bug that we found with the DeepEP version being installed. This was fixed in [PR 11773](https://github.com/sgl-project/sglang/pull/11773). When SGLang releases a version > `0.5.3.post3` we will update these instructions. ```bash cd $DYNAMO_ROOT docker build \ -f container/Dockerfile.sglang-wideep \ -t dynamo-wideep-gb200 \ --build-arg SGLANG_IMAGE_TAG=nightly-dev-20251019-fda0cb2a \ --no-cache \ . ``` 2. You can run this container on each 4xGB200 node using the following command. > [!IMPORTANT] > We recommend downloading DeepSeek-R1 and then mounting it to the container. You can find the model [here](https://huggingface.co/deepseek-ai/DeepSeek-R1) ```bash docker run \ --gpus all \ -it \ --rm \ --network host \ --volume /PATH_TO_DSR1_MODEL/:/model/ \ --shm-size=10G \ --ulimit memlock=-1 \ --ulimit stack=67108864 \ --ulimit nofile=65536:65536 \ --cap-add CAP_SYS_PTRACE \ --ipc host \ dynamo-wideep-gb200:latest ``` 3. Run the ingress and prefill worker ```bash # run ingress python3 -m dynamo.frontend --http-port=8000 & # run prefill worker DYN_SKIP_SGLANG_LOG_FORMATTING=1 \ MC_TE_METRIC=true \ SGLANG_DISAGGREGATION_HEARTBEAT_MAX_FAILURE=100000 \ SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT=100000 \ SGLANG_DISAGGREGATION_WAITING_TIMEOUT=100000 \ MC_FORCE_MNNVL=1 \ NCCL_MNNVL_ENABLE=1 \ NCCL_CUMEM_ENABLE=1 \ SGLANG_USE_MESSAGE_QUEUE_BROADCASTER=0 \ SGLANG_DISABLE_TP_MEMORY_INBALANCE_CHECK=1 \ PYTHONUNBUFFERED=1 \ python3 -m dynamo.sglang \ --served-model-name deepseek-ai/DeepSeek-R1 \ --model-path /model/ \ --skip-tokenizer-init \ --trust-remote-code \ --disaggregation-mode prefill \ --dist-init-addr ${HEAD_PREFILL_NODE_IP}:29500 \ --disaggregation-bootstrap-port 30001 \ --nnodes 2 \ --node-rank 0 \ --tp-size 8 \ --dp-size 8 \ --enable-dp-attention \ --host 0.0.0.0 \ --decode-log-interval 1 \ --max-running-requests 6144 \ --context-length 2716 \ --disable-radix-cache \ --moe-a2a-backend deepep \ --load-balance-method round_robin \ --deepep-mode normal \ --moe-dense-tp-size 1 \ --enable-dp-lm-head \ --disable-shared-experts-fusion \ --ep-num-redundant-experts 32 \ --ep-dispatch-algorithm static \ --eplb-algorithm deepseek \ --attention-backend cutlass_mla \ --watchdog-timeout 1000000 \ --disable-cuda-graph \ --chunked-prefill-size 16384 \ --max-total-tokens 32768 \ --mem-fraction-static 0.82 \ --log-level debug \ --disaggregation-transfer-backend nixl ``` On the other prefill nodes (this example has 2 total prefill nodes), run the same command but change `--node-rank` to 1 4. Run the decode worker on the head decode node ```bash SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=768 \ MC_TE_METRIC=true \ SGLANG_DISAGGREGATION_HEARTBEAT_MAX_FAILURE=100000 \ SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT=100000 \ SGLANG_DISAGGREGATION_WAITING_TIMEOUT=100000 \ SGLANG_HACK_SEQ_BOOTSTRAP_ROOM=1 \ SGLANG_MOONCAKE_CUSTOM_MEM_POOL=True \ NCCL_MNNVL_ENABLE=1 \ MC_FORCE_MNNVL=1 \ NCCL_CUMEM_ENABLE=1 \ SGLANG_USE_MESSAGE_QUEUE_BROADCASTER=0 \ SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK=1 \ PYTHONUNBUFFERED=1 \ python3 -m dynamo.sglang \ --served-model-name deepseek-ai/DeepSeek-R1 \ --model-path /model/ \ --skip-tokenizer-init \ --trust-remote-code \ --disaggregation-mode decode \ --dist-init-addr ${HEAD_DECODE_NODE_IP}:29500 \ --disaggregation-bootstrap-port 30001 \ --nnodes 2 \ --node-rank 0 \ --tp-size 8 \ --dp-size 8 \ --enable-dp-attention \ --host 0.0.0.0 \ --decode-log-interval 1 \ --max-running-requests 36864 \ --context-length 2716 \ --disable-radix-cache \ --moe-a2a-backend deepep \ --prefill-round-robin-balance \ --deepep-mode low_latency \ --moe-dense-tp-size 1 \ --enable-dp-lm-head \ --cuda-graph-max-bs 256 \ --disable-shared-experts-fusion \ --ep-num-redundant-experts 32 \ --ep-dispatch-algorithm static \ --eplb-algorithm deepseek \ --attention-backend cutlass_mla \ --watchdog-timeout 1000000 \ --chunked-prefill-size 36864 \ --mem-fraction-static 0.82 \ --log-level debug \ --disaggregation-transfer-backend nixl ``` On the other decode nodes (this example has 2 total decode nodes), run the same command but change `--node-rank` to 1.