DeepSeek-V3.2 - SGLang Documentation

This page focuses on optimal configuration and benchmark results for DeepSeek-V3.2 on the Ascend NPU. For environment setup, model weight download, feature configuration, and deployment instructions, etc., see the DeepSeek-V3.2 Model Tutorial.On the A3 Series, each card has 2 dies, so --tp-size is twice the card count; see Ascend NPU Reference for details.

The optimal configurations on this page are validated with the image quay.io/ascend/sglang:v0.5.18-cann9.0.0-a3. Use exactly this version; later versions are not covered by this page and may introduce breaking changes.

Low Latency

Model	Hardware	Cards	Deploy Mode	Dataset	TPOT	Quantization	Configuration
DeepSeek-V3.2	Ascend A3 Series Products	32	PD Disaggregation	128k+1k	26ms	W8A8 INT8	Optimal Configuration
DeepSeek-V3.2	Ascend A3 Series Products	32	PD Disaggregation	128k+1k	26ms	W8A8 INT8	Optimal Configuration

High Throughput

Model	Hardware	Cards	Deploy Mode	Dataset	TPOT	Quantization	Configuration
DeepSeek-V3.2	Ascend A3 Series Products	32	PD Disaggregation	128k+1k	107ms	W8A8 INT8	Optimal Configuration

Optimal Configuration

DeepSeek-V3.2 W8A8 1P1D 32P IN128K OUT1K 26ms

Model: DeepSeek-V3.2 Hardware: Ascend A3 Series Products Cards: 32 Deploy Mode: PD Disaggregation Quantization: W8A8 INT8 Dataset: 128k+1k TPOT: 26ms

Model Deployment

Command

# ============================================================
# Before running, update the following variables:
#   P_IP: prefill node IP address
#   D_IP: decode node IP address
#   ASCEND_MF_STORE_URL: prefill node IP with port
#   MODEL_PATH: path to the model weights directory
#   HCCL_SOCKET_IFNAME: network interface name for HCCL
#   GLOO_SOCKET_IFNAME: network interface name for Gloo
# ============================================================


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

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 PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export SGLANG_SET_CPU_AFFINITY=1
export STREAMS_PER_DEVICE=32

P_IP=('<your prefill ip1>' '<your prefill ip2>')
D_IP=('<your decode ip1>' '<your decode ip2>')

export ASCEND_MF_STORE_URL="tcp://<your prefill ip1>:24670"

MODEL_PATH=/path/to/model-weights

LOCAL_HOST1=`hostname -I|awk -F " " '{print$1}'`
LOCAL_HOST2=`hostname -I|awk -F " " '{print$2}'`
echo "${LOCAL_HOST1}"
echo "${LOCAL_HOST2}"
# prefill
for i in "${!P_IP[@]}";
do
    if [[ "$LOCAL_HOST1" == "${P_IP[$i]}" || "$LOCAL_HOST2" == "${P_IP[$i]}" ]];
    then
        echo "${P_IP[$i]}"
        export DEEPEP_HCCL_BUFFSIZE=1200
        export DEEP_NORMAL_MODE_USE_INT8_QUANT=1
        export GLOO_SOCKET_IFNAME=<network-interface>
        export HCCL_SOCKET_IFNAME=<network-interface>
        export TASK_QUEUE_ENABLE=2

        python3 -m sglang.launch_server \
        --model-path ${MODEL_PATH} \
        --disaggregation-mode prefill \
        --host ${P_IP[$i]} \
        --port 8000 \
        --dist-init-addr ${P_IP[0]}:5000 \
        --disaggregation-bootstrap-port 8998 \
        --node-rank $i \
        --nnodes 2 \
        --tp 32 \
        --watchdog-timeout 9000 \
        --mem-fraction-static 0.73 \
        --disable-radix-cache \
        --chunked-prefill-size -1 \
        --max-prefill-tokens 68000 \
        --max-running-requests 1 \
        --moe-a2a-backend deepep \
        --deepep-mode normal \
        --quantization modelslim \
        --disaggregation-transfer-backend ascend \
        --disable-cuda-graph \
        --moe-dense-tp-size 1 \
        --enable-prefill-cp \
        --cp-strategy zigzag \
        --attn-cp-size 32 \
        --speculative-algorithm NEXTN \
        --speculative-num-steps 1 \
        --speculative-eagle-topk 1 \
        --speculative-num-draft-tokens 2 \
        --reasoning-parser deepseek-v3 \
        --tool-call-parser deepseekv32 \
        --trust-remote-code \
        --attention-backend ascend \
        --device npu
        break
    fi
done

# decode
for i in "${!D_IP[@]}";
do
    if [[ "$LOCAL_HOST1" == "${D_IP[$i]}" || "$LOCAL_HOST2" == "${D_IP[$i]}" ]];
    then
        echo "${D_IP[$i]}"
        export DEEPEP_HCCL_BUFFSIZE=400
        export GLOO_SOCKET_IFNAME=<network-interface>
        export HCCL_SOCKET_IFNAME=<network-interface>
        export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=8
        export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
        export SGLANG_SCHEDULER_SKIP_ALL_GATHER=1
        export TASK_QUEUE_ENABLE=0

        python3 -m sglang.launch_server \
        --model-path ${MODEL_PATH} \
        --disaggregation-mode decode \
        --host ${D_IP[$i]} \
        --port 8001 \
        --dist-init-addr ${D_IP[0]}:5000 \
        --node-rank $i \
        --nnodes 2 \
        --tp 32 \
        --enable-dp-attention --dp-size 8 \
        --ep 32 \
        --moe-dense-tp-size 1 \
        --enable-dp-lm-head \
        --watchdog-timeout 9000 \
        --mem-fraction-static 0.79 \
        --disable-radix-cache \
        --chunked-prefill-size -1 \
        --max-prefill-tokens 68000 \
        --max-running-requests 32 \
        --cuda-graph-max-bs-decode 4 \
        --moe-a2a-backend deepep \
        --deepep-mode low_latency \
        --quantization modelslim \
        --speculative-algorithm NEXTN \
        --speculative-num-steps 3 \
        --speculative-eagle-topk 1 \
        --speculative-num-draft-tokens 4 \
        --disaggregation-transfer-backend ascend \
        --reasoning-parser deepseek-v3 \
        --tool-call-parser deepseekv32 \
        --trust-remote-code \
        --attention-backend ascend \
        --device npu
        break
    fi
done

Command

# ============================================================
# Before running, replace the following placeholders:
#   <your prefill ip>: prefill node IP address
#   <your decode ip1>: first decode node IP address (decode may have distributed nodes)
# ============================================================

python -m sglang_router.launch_router \
    --pd-disaggregation \
    --policy cache_aware \
    --prefill http://<your prefill ip>:8000 8998 \
    --decode http://<your decode ip1>:8001 \
    --host 127.0.0.1 \
    --port 6688 \
    --mini-lb

Benchmark

We tested it based on the RANDOM dataset.

Command

python -m sglang.bench_serving \
    --dataset-name random \
    --backend sglang \
    --host 127.0.0.1 \
    --port 6688 \
    --max-concurrency 8 \
    --num-prompts 8 \
    --random-input-len 131072 \
    --random-output-len 1024 \
    --random-range-ratio 1 \
    --seed 1

DeepSeek-V3.2 W8A8 1P1D 32P IN128K OUT1K BS16

Model: DeepSeek-V3.2 Hardware: Ascend A3 Series Products Cards: 32 Deploy Mode: PD Disaggregation Quantization: W8A8 INT8 Dataset: 128k+1k TPOT: 107ms

Model Deployment

Command

# ============================================================
# Before running, update the following variables:
#   P_IP: prefill node IP address
#   D_IP: decode node IP address
#   ASCEND_MF_STORE_URL: prefill node IP with port
#   MODEL_PATH: path to the model weights directory
#   HCCL_SOCKET_IFNAME: network interface name for HCCL
#   GLOO_SOCKET_IFNAME: network interface name for Gloo
# ============================================================


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

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 PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export SGLANG_SET_CPU_AFFINITY=1
export STREAMS_PER_DEVICE=32

P_IP=('<your prefill ip1>' '<your prefill ip2>')
D_IP=('<your decode ip1>' '<your decode ip2>')

export ASCEND_MF_STORE_URL="tcp://<your prefill ip1>:24670"

MODEL_PATH=/path/to/model-weights

LOCAL_HOST1=`hostname -I|awk -F " " '{print$1}'`
LOCAL_HOST2=`hostname -I|awk -F " " '{print$2}'`
echo "${LOCAL_HOST1}"
echo "${LOCAL_HOST2}"
# prefill
for i in "${!P_IP[@]}";
do
    if [[ "$LOCAL_HOST1" == "${P_IP[$i]}" || "$LOCAL_HOST2" == "${P_IP[$i]}" ]];
    then
        echo "${P_IP[$i]}"
        export DEEPEP_HCCL_BUFFSIZE=1200
        export DEEP_NORMAL_MODE_USE_INT8_QUANT=1
        export GLOO_SOCKET_IFNAME=<network-interface>
        export HCCL_SOCKET_IFNAME=<network-interface>
        export TASK_QUEUE_ENABLE=2

        python3 -m sglang.launch_server \
        --model-path ${MODEL_PATH} \
        --disaggregation-mode prefill \
        --host ${P_IP[$i]} \
        --port 8000 \
        --dist-init-addr ${P_IP[0]}:5000 \
        --disaggregation-bootstrap-port 8998 \
        --node-rank $i \
        --nnodes 2 \
        --tp 32 \
        --watchdog-timeout 9000 \
        --mem-fraction-static 0.73 \
        --disable-radix-cache \
        --chunked-prefill-size -1 \
        --max-prefill-tokens 68000 \
        --max-running-requests 1 \
        --moe-a2a-backend deepep \
        --deepep-mode normal \
        --quantization modelslim \
        --disaggregation-transfer-backend ascend \
        --disable-cuda-graph \
        --moe-dense-tp-size 1 \
        --enable-prefill-cp \
        --cp-strategy zigzag \
        --attn-cp-size 32 \
        --speculative-algorithm NEXTN \
        --speculative-num-steps 1 \
        --speculative-eagle-topk 1 \
        --speculative-num-draft-tokens 2 \
        --reasoning-parser deepseek-v3 \
        --tool-call-parser deepseekv32 \
        --trust-remote-code \
        --attention-backend ascend \
        --device npu
        break
    fi
done

# decode
for i in "${!D_IP[@]}";
do
    if [[ "$LOCAL_HOST1" == "${D_IP[$i]}" || "$LOCAL_HOST2" == "${D_IP[$i]}" ]];
    then
        echo "${D_IP[$i]}"
        export DEEPEP_HCCL_BUFFSIZE=400
        export GLOO_SOCKET_IFNAME=<network-interface>
        export HCCL_SOCKET_IFNAME=<network-interface>
        export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=8
        export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
        export SGLANG_SCHEDULER_SKIP_ALL_GATHER=1
        export TASK_QUEUE_ENABLE=0

        python3 -m sglang.launch_server \
        --model-path ${MODEL_PATH} \
        --disaggregation-mode decode \
        --host ${D_IP[$i]} \
        --port 8001 \
        --dist-init-addr ${D_IP[0]}:5000 \
        --node-rank $i \
        --nnodes 2 \
        --tp 32 \
        --enable-dp-attention --dp-size 8 \
        --ep 32 \
        --moe-dense-tp-size 1 \
        --enable-dp-lm-head \
        --watchdog-timeout 9000 \
        --mem-fraction-static 0.79 \
        --disable-radix-cache \
        --chunked-prefill-size -1 \
        --max-prefill-tokens 68000 \
        --max-running-requests 32 \
        --cuda-graph-max-bs-decode 4 \
        --moe-a2a-backend deepep \
        --deepep-mode low_latency \
        --quantization modelslim \
        --speculative-algorithm NEXTN \
        --speculative-num-steps 3 \
        --speculative-eagle-topk 1 \
        --speculative-num-draft-tokens 4 \
        --disaggregation-transfer-backend ascend \
        --reasoning-parser deepseek-v3 \
        --tool-call-parser deepseekv32 \
        --trust-remote-code \
        --attention-backend ascend \
        --device npu
        break
    fi
done

Command

# ============================================================
# Before running, replace the following placeholders:
#   <your prefill ip>: prefill node IP address
#   <your decode ip1>: first decode node IP address (decode may have distributed nodes)
# ============================================================

python -m sglang_router.launch_router \
    --pd-disaggregation \
    --policy cache_aware \
    --prefill http://<your prefill ip>:8000 8998 \
    --decode http://<your decode ip1>:8001 \
    --host 127.0.0.1 \
    --port 6688 \
    --mini-lb

Benchmark

We tested it based on the RANDOM dataset.

Command

python -m sglang.bench_serving \
    --dataset-name random \
    --backend sglang \
    --host 127.0.0.1 \
    --port 6688 \
    --max-concurrency 16 \
    --num-prompts 16 \
    --random-input-len 131072 \
    --random-output-len 1024 \
    --random-range-ratio 1

DeepSeek-V3.2 W8A8 1P1D 32P IN128K OUT1K BS8

Model: DeepSeek-V3.2 Hardware: Ascend A3 Series Products Cards: 32 Deploy Mode: PD Disaggregation Quantization: W8A8 INT8 Dataset: 128k+1k TPOT: 26ms

Model Deployment

Command

# ============================================================
# Before running, update the following variables:
#   P_IP: prefill node IP address
#   D_IP: decode node IP address
#   ASCEND_MF_STORE_URL: prefill node IP with port
#   MODEL_PATH: path to the model weights directory
#   HCCL_SOCKET_IFNAME: network interface name for HCCL
#   GLOO_SOCKET_IFNAME: network interface name for Gloo
# ============================================================


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

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 PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export SGLANG_SET_CPU_AFFINITY=1
export STREAMS_PER_DEVICE=32

P_IP=('<your prefill ip1>' '<your prefill ip2>')
D_IP=('<your decode ip1>' '<your decode ip2>')

export ASCEND_MF_STORE_URL="tcp://<your prefill ip1>:24670"

MODEL_PATH=/path/to/model-weights

LOCAL_HOST1=`hostname -I|awk -F " " '{print$1}'`
LOCAL_HOST2=`hostname -I|awk -F " " '{print$2}'`
echo "${LOCAL_HOST1}"
echo "${LOCAL_HOST2}"
# prefill
for i in "${!P_IP[@]}";
do
    if [[ "$LOCAL_HOST1" == "${P_IP[$i]}" || "$LOCAL_HOST2" == "${P_IP[$i]}" ]];
    then
        echo "${P_IP[$i]}"
        export DEEPEP_HCCL_BUFFSIZE=1200
        export DEEP_NORMAL_MODE_USE_INT8_QUANT=1
        export GLOO_SOCKET_IFNAME=<network-interface>
        export HCCL_SOCKET_IFNAME=<network-interface>
        export TASK_QUEUE_ENABLE=2

        python3 -m sglang.launch_server \
        --model-path ${MODEL_PATH} \
        --disaggregation-mode prefill \
        --host ${P_IP[$i]} \
        --port 8000 \
        --dist-init-addr ${P_IP[0]}:5000 \
        --disaggregation-bootstrap-port 8998 \
        --node-rank $i \
        --nnodes 2 \
        --tp 32 \
        --watchdog-timeout 9000 \
        --mem-fraction-static 0.73 \
        --disable-radix-cache \
        --chunked-prefill-size -1 \
        --max-prefill-tokens 68000 \
        --max-running-requests 1 \
        --moe-a2a-backend deepep \
        --deepep-mode normal \
        --quantization modelslim \
        --disaggregation-transfer-backend ascend \
        --disable-cuda-graph \
        --moe-dense-tp-size 1 \
        --enable-prefill-cp \
        --cp-strategy zigzag \
        --attn-cp-size 32 \
        --speculative-algorithm NEXTN \
        --speculative-num-steps 1 \
        --speculative-eagle-topk 1 \
        --speculative-num-draft-tokens 2 \
        --reasoning-parser deepseek-v3 \
        --tool-call-parser deepseekv32 \
        --trust-remote-code \
        --attention-backend ascend \
        --device npu
        break
    fi
done

# decode
for i in "${!D_IP[@]}";
do
    if [[ "$LOCAL_HOST1" == "${D_IP[$i]}" || "$LOCAL_HOST2" == "${D_IP[$i]}" ]];
    then
        echo "${D_IP[$i]}"
        export DEEPEP_HCCL_BUFFSIZE=400
        export GLOO_SOCKET_IFNAME=<network-interface>
        export HCCL_SOCKET_IFNAME=<network-interface>
        export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=8
        export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
        export SGLANG_SCHEDULER_SKIP_ALL_GATHER=1
        export TASK_QUEUE_ENABLE=0

        python3 -m sglang.launch_server \
        --model-path ${MODEL_PATH} \
        --disaggregation-mode decode \
        --host ${D_IP[$i]} \
        --port 8001 \
        --dist-init-addr ${D_IP[0]}:5000 \
        --node-rank $i \
        --nnodes 2 \
        --tp 32 \
        --enable-dp-attention --dp-size 8 \
        --ep 32 \
        --moe-dense-tp-size 1 \
        --enable-dp-lm-head \
        --watchdog-timeout 9000 \
        --mem-fraction-static 0.79 \
        --disable-radix-cache \
        --chunked-prefill-size -1 \
        --max-prefill-tokens 68000 \
        --max-running-requests 32 \
        --cuda-graph-max-bs-decode 4 \
        --moe-a2a-backend deepep \
        --deepep-mode low_latency \
        --quantization modelslim \
        --speculative-algorithm NEXTN \
        --speculative-num-steps 3 \
        --speculative-eagle-topk 1 \
        --speculative-num-draft-tokens 4 \
        --disaggregation-transfer-backend ascend \
        --reasoning-parser deepseek-v3 \
        --tool-call-parser deepseekv32 \
        --trust-remote-code \
        --attention-backend ascend \
        --device npu
        break
    fi
done

Command

# ============================================================
# Before running, replace the following placeholders:
#   <your prefill ip>: prefill node IP address
#   <your decode ip1>: first decode node IP address (decode may have distributed nodes)
# ============================================================

python -m sglang_router.launch_router \
    --pd-disaggregation \
    --policy cache_aware \
    --prefill http://<your prefill ip>:8000 8998 \
    --decode http://<your decode ip1>:8001 \
    --host 127.0.0.1 \
    --port 6688 \
    --mini-lb

Benchmark

We tested it based on the RANDOM dataset.

Command

python -m sglang.bench_serving \
    --dataset-name random \
    --backend sglang \
    --host 127.0.0.1 \
    --port 6688 \
    --max-concurrency 8 \
    --num-prompts 8 \
    --random-input-len 131072 \
    --random-output-len 1024 \
    --random-range-ratio 1

​Low Latency

​High Throughput

​Optimal Configuration

​DeepSeek-V3.2 W8A8 1P1D 32P IN128K OUT1K 26ms

​Model Deployment

​Benchmark

​DeepSeek-V3.2 W8A8 1P1D 32P IN128K OUT1K BS16

​Model Deployment

​Benchmark

​DeepSeek-V3.2 W8A8 1P1D 32P IN128K OUT1K BS8

​Model Deployment

​Benchmark

Low Latency

High Throughput

Optimal Configuration

DeepSeek-V3.2 W8A8 1P1D 32P IN128K OUT1K 26ms

Model Deployment

Benchmark

DeepSeek-V3.2 W8A8 1P1D 32P IN128K OUT1K BS16

Model Deployment

Benchmark

DeepSeek-V3.2 W8A8 1P1D 32P IN128K OUT1K BS8

Model Deployment

Benchmark