MiMo-V2.5-Pro - SGLang Documentation

This page focuses on the deployment of MiMo-V2.5-Pro (FP4) with DFlash speculative decoding in PD disaggregation mode on the Ascend NPU.On the A3 Series, each card has 2 dies, so --tp-size is twice the card count; see Ascend NPU Reference for details.

Model Deployment

MiMo-V2.5-Pro-FP4-DFlash is an MXFP4-quantized checkpoint with a built-in DFlash draft model (located in the dflash/ subdirectory of the weights). The following example deploys it in 1P1D mode (1 prefill node + 1 decode node, TP8 + DP2 per node).

Common environment setup (both nodes)

Command

# ============================================================
# Before running, update the following variables:
#   ASCEND_MF_STORE_URL: prefill node IP with port
#   HCCL_SOCKET_IFNAME / GLOO_SOCKET_IFNAME: network interface name
# ============================================================

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 HCCL_BUFFSIZE=300
export HCCL_OP_EXPANSION_MODE=AIV
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export STREAMS_PER_DEVICE=32
export SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT=600

# Use the AscendC flash attention
export ASCEND_USE_FIA=1

# PD disaggregation transfer config
export ASCEND_MF_STORE_URL="tcp://<your prefill ip>:24669"
export ASCEND_MF_TRANSFER_PROTOCOL="device_urma"

# DeepEP
export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=32
export HCCL_SOCKET_IFNAME=<network-interface>
export GLOO_SOCKET_IFNAME=<network-interface>
export HCCL_HOST_SOCKET_PORT_RANGE=auto

MODEL_PATH=/path/to/MiMo-V2.5-Pro-FP4-DFlash

Prefill node

Command

export DEEPEP_HCCL_BUFFSIZE=2500
# Enable chunked dispatch for long sequences
export DEEPEP_NORMAL_LONG_SEQ_ROUND=10
export DEEPEP_NORMAL_LONG_SEQ_PER_ROUND_TOKENS=4096
export DEEPEP_NORMAL_COMBINE_ENABLE_LONG_SEQ=0

python3 -m sglang.launch_server \
    --model-path $MODEL_PATH \
    --attention-backend ascend \
    --device npu \
    --tp-size 8 --nnodes 1 --node-rank 0 \
    --chunked-prefill-size 8192 \
    --trust-remote-code --port 10001 \
    --host <your prefill ip> --max-running-requests 32 \
    --mem-fraction-static 0.90 \
    --swa-full-tokens-ratio 0.3 \
    --disaggregation-mode prefill --disaggregation-transfer-backend ascend \
    --disaggregation-bootstrap-port 8996 \
    --disable-piecewise-cuda-graph \
    --dp-size 2 --enable-dp-attention --enable-dp-lm-head \
    --moe-a2a-backend deepep --deepep-mode normal

Decode node

Command

# Use eagle_worker_v2 and overlap plan stream to hide the draft/target preparation
export SGLANG_ENABLE_SPEC_V2=1
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
# DFlash draft model has a longer context length than the derived value
export SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1

export DEEPEP_HCCL_BUFFSIZE=1200

python3 -m sglang.launch_server \
    --model-path $MODEL_PATH \
    --speculative-draft-model-path $MODEL_PATH/dflash \
    --attention-backend ascend \
    --device npu \
    --tp-size 8 --nnodes 1 --node-rank 0 \
    --trust-remote-code --port 20001 \
    --host <your decode ip> --max-running-requests 32 \
    --mem-fraction-static 0.88 \
    --swa-full-tokens-ratio 0.3 \
    --cuda-graph-bs 1 2 4 8 12 16 \
    --disaggregation-mode decode --disaggregation-transfer-backend ascend \
    --disaggregation-bootstrap-port 8996 \
    --moe-a2a-backend deepep --deepep-mode low_latency \
    --dp-size 2 --enable-dp-attention --enable-dp-lm-head \
    --speculative-algorithm DFLASH \
    --speculative-num-draft-tokens 8

Router

Command

python -m sglang_router.launch_router \
    --pd-disaggregation \
    --policy cache_aware \
    --prefill http://<your prefill ip>:10001 \
    --decode http://<your decode ip>:20001 \
    --host 127.0.0.1 \
    --port 6688 \
    --health-check-interval-secs 3600 --mini-lb

Benchmark

We tested it based on the RANDOM dataset.

Benchmark Prefill Node (TTFT)

Command

python3 -m sglang.bench_serving \
    --backend sglang \
    --host 127.0.0.1 \
    --port 6688 \
    --model /path/to/MiMo-V2.5-Pro-FP4-DFlash \
    --dataset-name random \
    --tokenize-prompt \
    --random-input-len 16000 \
    --random-output-len 1 \
    --request-rate 0.4 \
    --random-range-ratio 1 \
    --num-prompts 128 \
    --max-concurrency 32

Benchmark Decode Node (TPOT)

Command

python3 -m sglang.bench_serving \
    --backend sglang \
    --host 127.0.0.1 \
    --port 6688 \
    --model /path/to/MiMo-V2.5-Pro-FP4-DFlash \
    --dataset-name random \
    --tokenize-prompt \
    --random-input-len 16000 \
    --random-output-len 1000 \
    --request-rate inf \
    --random-range-ratio 1 \
    --num-prompts 128 \
    --max-concurrency 32

​Model Deployment

​Common environment setup (both nodes)

​Prefill node

​Decode node

​Router

​Benchmark

​Benchmark Prefill Node (TTFT)

​Benchmark Decode Node (TPOT)

Model Deployment

Common environment setup (both nodes)

Prefill node

Decode node

Router

Benchmark

Benchmark Prefill Node (TTFT)

Benchmark Decode Node (TPOT)