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[NPU] [DOC] Update npu best practice docs to match latest code (#23077)

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amote-i
2026-04-18 14:17:00 +08:00
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docs/platforms/ascend/ascend_npu_best_practice.md
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@@ -10,9 +10,9 @@ you encounter issues or have any questions, please [open an issue](https://githu
| Model | Hardware | Cards | Deploy Mode | Dataset | TPOT | Quantization | Configuration |
|-------------------|---------------|-------|-------------------|-----------|------|--------------|-------------------------------------------------------------------------------------------|
| Deepseek-R1 | Atlas 800I A3 | 32 | PD Disaggregation | 6K+1.6K | 20ms | W8A8 INT8 | [Optimal Configuration](#deepseek-r1-6k-1_6k-20ms-on-a3-32-cards-disaggregation-mode) |
| Deepseek-R1 | Atlas 800I A3 | 32 | PD Disaggregation | 3.9K+1K | 20ms | W8A8 INT8 | [Optimal Configuration](#deepseek-r1-3_9k-1k-20ms-on-a3-32-cards-disaggregation-mode) |
| Deepseek-R1 | Atlas 800I A3 | 32 | PD Disaggregation | 3.5K+1.5K | 20ms | W8A8 INT8 | [Optimal Configuration](#deepseek-r1-3_5k-1_5k-20ms-on-a3-32-cards-disaggregation-mode) |
| Deepseek-R1 | Atlas 800I A3 | 32 | PD Disaggregation | 3.5K+1K | 20ms | W8A8 INT8 | [Optimal Configuration](#deepseek-r1-3_5k-1k-20ms-on-a3-32-cards-disaggregation-mode) |
| Deepseek-R1 | Atlas 800I A3 | 32 | PD Disaggregation | 3.9K+1K | 19ms | W8A8 INT8 | [Optimal Configuration](#deepseek-r1-3_9k-1k-19ms-on-a3-32-cards-disaggregation-mode) |
| Deepseek-R1 | Atlas 800I A3 | 32 | PD Disaggregation | 3.5K+1.5K | 19ms | W8A8 INT8 | [Optimal Configuration](#deepseek-r1-3_5k-1_5k-19ms-on-a3-32-cards-disaggregation-mode) |
| Deepseek-R1 | Atlas 800I A3 | 32 | PD Disaggregation | 3.5K+1K | 19ms | W8A8 INT8 | [Optimal Configuration](#deepseek-r1-3_5k-1k-19ms-on-a3-32-cards-disaggregation-mode) |
| DeepSeek-V3.2 | Atlas 800I A3 | 32 | PD Disaggregation | 128K+1K | 26ms | W8A8 INT8 | [Optimal Configuration](#deepseek-v32-128k-1k-26ms-on-a3-32-cards-disaggregation-mode) |
### High Throughput
@@ -20,6 +20,7 @@ you encounter issues or have any questions, please [open an issue](https://githu
| Model | Hardware | Cards | Deploy Mode | Dataset | TPOT | Quantization | Configuration |
|-------------|---------------|-------|-------------------|-----------|------|--------------|-----------------------------------------------------------------------------------------|
| Deepseek-R1 | Atlas 800I A3 | 32 | PD Disaggregation | 3.5K+1.5K | 50ms | W8A8 INT8 | [Optimal Configuration](#deepseek-r1-3_5k-1_5k-50ms-on-a3-32-cards-disaggregation-mode) |
| Deepseek-R1 | Atlas 800I A3 | 24 | PD Disaggregation | 2K+2K | 50ms | W8A8 INT8 | [Optimal Configuration](#deepseek-r1-2k-2k-50ms-on-a3-24-cards-disaggregation-mode) |
| Deepseek-R1 | Atlas 800I A3 | 8 | PD Mixed | 2K+2K | 50ms | W4A8 INT8 | [Optimal Configuration](#deepseek-r1-2k-2k-50ms-on-a3-8-cards-mixed-mode) |
| Deepseek-R1 | Atlas 800I A3 | 16 | PD Disaggregation | 2K+2K | 50ms | W4A8 INT8 | [Optimal Configuration](#deepseek-r1-2k-2k-50ms-on-a3-16-cards-disaggregation-mode) |
| Deepseek-R1 | Atlas 800I A3 | 8 | PD Mixed | 3.5K+1.5K | 50ms | W4A8 INT8 | [Optimal Configuration](#deepseek-r1-3_5k-1_5k-50ms-on-a3-8-cards-mixed-mode) |
@@ -41,10 +42,12 @@ you encounter issues or have any questions, please [open an issue](https://githu
| Qwen3-32B | Atlas 800I A3 | 2 | PD Mixed | 6K+1.5K | 17ms | W8A8 INT8 | [Optimal Configuration](#qwen3-32b-6k-1_5k-17ms-on-a3-2-cards-mixed-mode) |
| Qwen3-8B | Atlas 800I A3 | 1 | PD Mixed | 1K+0.3K | 7ms | W8A8 INT8 | [Optimal Configuration](#qwen3-8b-1k-0_3k-7ms-on-a3-1-cards-mixed-mode) |
| Qwen3-8B | Atlas 800I A3 | 1 | PD Mixed | 6K+1.5K | 12ms | W8A8 INT8 | [Optimal Configuration](#qwen3-8b-6k-1_5k-12ms-on-a3-1-cards-mixed-mode) |
| Qwen3-8B | Atlas 800I A3 | 1 | PD Mixed | 3.5K+1.5K | 5ms | W8A8 INT8 | [Optimal Configuration](#qwen3-8b-3_5k-1_5k-5ms-on-a3-1-cards-mixed-mode) |
| Qwen3-30B-A3B | Atlas 800I A3 | 1 | PD Mixed | 6K+1.5K | 10ms | W8A8 INT8 | [Optimal Configuration](#qwen3-30b-a3b-6k-1_5k-10ms-on-a3-1-cards-mixed-mode) |
| Qwen3-30B-A3B | Atlas 800I A3 | 1 | PD Mixed | 1K+0.3K | 7ms | W8A8 INT8 | [Optimal Configuration](#qwen3-30b-a3b-1k-0_3k-7ms-on-a3-1-cards-mixed-mode) |
| Qwen3-Next-A3B-Instruct | Atlas 800I A3 | 2 | PD Mixed | 1K+0.3K | 14.21ms | W8A8 INT8 | [Optimal Configuration](#qwen3-next-1k-0_3k-14_21ms-on-a3-2-cards-mixed-mode) |
| Qwen3-Next-A3B-Instruct | Atlas 800I A3 | 2 | PD Mixed | 6K+1.5K | 15.62ms | W8A8 INT8 | [Optimal Configuration](#qwen3-next-6k-1_5k-15_62ms-on-a3-2-cards-mixed-mode) |
| Qwen3-Next-A3B-Instruct | Atlas 800I A3 | 2 | PD Mixed | 3.5K+1.5K | 20ms | W8A8 INT8 | [Optimal Configuration](#qwen3-next-3_5k-1_5k-20ms-on-a3-2-cards-mixed-mode) |
| Qwen3-14B | Atlas 800I A3 | 1 | PD Mixed | 3.5K+1.5K | 9ms | W8A8 INT8 | [Optimal Configuration](#qwen3-14b-3_5k-1_5k-9ms-on-a3-1-cards-mixed-mode) |
### High Throughput
@@ -66,6 +69,7 @@ you encounter issues or have any questions, please [open an issue](https://githu
| Qwen3-32B | Atlas 800I A2 | 8 | PD Mixed | 3.5K+1.5K | 50ms | W8A8 INT8 | [Optimal Configuration](#qwen3-32b-3_5k-1_5k-50ms-on-a2-8-cards-mixed-mode) |
| Qwen3-32B | Atlas 800I A2 | 8 | PD Mixed | 2K+2K | 50ms | W8A8 INT8 | [Optimal Configuration](#qwen3-32b-2k-2k-50ms-on-a2-8-cards-mixed-mode) |
| Qwen3-14B | Atlas 800I A3 | 1 | PD Mixed | 3.5K+1.5K | 50ms | W8A8 INT8 | [Optimal Configuration](#qwen3-14b-3_5k-1_5k-50ms-on-a3-1-cards-mixed-mode) |
| Qwen3-8B | Atlas 800I A3 | 1 | PD Mixed | 3.5K+1.5K | 50ms | W8A8 INT8 | [Optimal Configuration](#qwen3-8b-3_5k-1_5k-50ms-on-a3-1-cards-mixed-mode) |
## Optimal Configuration
@@ -173,6 +177,7 @@ done
```
```shell
export SGLANG_DP_ROUND_ROBIN=1
python -m sglang_router.launch_router \
--pd-disaggregation \
--policy cache_aware \
@@ -192,6 +197,134 @@ We tested it based on the `RANDOM` dataset.
python -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 6688 --max-concurrency 768 --random-input-len 3500 --random-output-len 1500 --num-prompts 3072 --random-range-ratio 1 --request-rate 16
```
### DeepSeek-R1 2K-2K 50ms on A3 24 Cards Disaggregation Mode
Model: Deepseek R1
Hardware: Atlas 800I A3 24Card
DeployMode: PD Disaggregation
Dataset: random
Input Output Length: 2K+2K
TPOT: 50ms
#### Model Deployment
```shell
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 ASCEND_LAUNCH_BLOCKING
source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh
export PATH=/usr/local/Ascend/8.5.0/compiler/bishengir/bin:$PATH
export SGLANG_SET_CPU_AFFINITY=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export STREAMS_PER_DEVICE=32
export SGLANG_NPU_USE_MLAPO=1
export SGLANG_USE_FIA_NZ=1
export ASCEND_MF_STORE_URL="tcp://your prefill ip1:24669"
P_IP=('your prefill ip1')
D_IP=('your decode ip1' 'your decode ip2')
MODEL_PATH=xxx
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 HCCL_BUFFSIZE=1600
export DEEP_NORMAL_MODE_USE_INT8_QUANT=1
export TASK_QUEUE_ENABLE=2
export SGLANG_USE_AG_AFTER_QLORA=1
export HCCL_SOCKET_IFNAME=lo
export GLOO_SOCKET_IFNAME=lo
python -m sglang.launch_server --model-path ${MODEL_PATH} --disaggregation-mode prefill --host ${P_IP[$i]} \
--port 8000 --disaggregation-bootstrap-port $((8998+$i)) --trust-remote-code --nnodes 1 --node-rank 0 \
--tp-size 16 --mem-fraction-static 0.8 --attention-backend ascend --device npu --quantization modelslim \
--disaggregation-transfer-backend ascend --max-running-requests 20 --context-length 8192 --disable-radix-cache \
--chunked-prefill-size -1 --max-prefill-tokens 28680 --moe-a2a-backend deepep --deepep-mode normal \
--speculative-algorithm NEXTN --speculative-num-steps 1 --speculative-eagle-topk 1 --speculative-num-draft-tokens 2 \
--dp-size 4 --enable-dp-attention --disable-shared-experts-fusion --dtype bfloat16 --enable-attn-tp-input-scattered
NODE_RANK=$i
break
fi
done
# decode
for i in "${!D_IP[@]}";
do
if [[ "$LOCAL_HOST1" == "${D_IP[$i]}" || "$LOCAL_HOST2" == "${D_IP[$i]}" ]];
then
echo "${D_IP[$i]}"
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export SGLANG_ENABLE_SPEC_V2=1
export HCCL_BUFFSIZE=800
export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=102
export TASK_QUEUE_ENABLE=1
export SGLANG_SCHEDULER_SKIP_ALL_GATHER=1
export SGLANG_NPU_FUSED_MOE_MODE=1
export HCCL_SOCKET_IFNAME=xxx
export GLOO_SOCKET_IFNAME=xxx
python -m sglang.launch_server --model-path ${MODEL_PATH} --disaggregation-mode decode --host ${D_IP[$i]} \
--port 8001 --trust-remote-code --dist-init-addr ${D_IP[0]}:5000 --nnodes 2 --node-rank $i --tp-size 32 --dp-size 32 \
--mem-fraction-static 0.81 --max-running-requests 1088 --attention-backend ascend --device npu --quantization modelslim \
--moe-a2a-backend ascend_fuseep --enable-dp-attention --deepep-mode low_latency --enable-dp-lm-head --moe-dense-tp 1 \
--cuda-graph-bs 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 --disaggregation-transfer-backend ascend --watchdog-timeout 9000 --context-length 8192 \
--speculative-algorithm NEXTN --speculative-num-steps 2 --speculative-eagle-topk 1 --speculative-num-draft-tokens 3 \
--tokenizer-worker-num 4 --disable-shared-experts-fusion --dtype bfloat16 \
--load-balance-method round_robin
NODE_RANK=$i
break
fi
done
```
```shell
python -m sglang_router.launch_router \
--pd-disaggregation \
--policy cache_aware \
--prefill http://P_IP:8000 8998 \
--prefill http://P_IP:8000 8999 \
--decode http://D_IP:8001 \
--host 127.0.0.1 \
--port 6688 \
--mini-lb
```
#### Benchmark
We tested it based on the `RANDOM` dataset.
```shell
python -m sglang.bench_serving --dataset-name random --backend sglang \
--host 127.0.0.1 \
--port 6688 \
--max-concurrency 1088 \
--random-input-len 2048 \
--random-output-len 2048 \
--num-prompts 12800 \
--random-range-ratio 1 \
--request-rate 24
```
### DeepSeek-R1 6K-1_6K 20ms on A3 32 Cards Disaggregation Mode
Model: Deepseek R1
@@ -250,7 +383,7 @@ do
python -m sglang.launch_server --model-path ${MODEL_PATH} --disaggregation-mode prefill --host ${P_IP[$i]} \
--port 8000 --disaggregation-bootstrap-port $((8998+$i)) --trust-remote-code --nnodes 1 --node-rank 0 \
--tp-size 16 --mem-fraction-static 0.81 --attention-backend ascend --device npu --quantization modelslim \
--disaggregation-transfer-backend ascend --max-running-requests 4 --context-length 8192 --disable-radix-cache \
--disaggregation-transfer-backend ascend --max-running-requests 4 --disable-radix-cache \
--chunked-prefill-size -1 --max-prefill-tokens 28680 --moe-a2a-backend deepep --deepep-mode normal \
--speculative-algorithm NEXTN --speculative-num-steps 1 --speculative-eagle-topk 1 --speculative-num-draft-tokens 2 \
--dp-size 2 --enable-dp-attention --disable-shared-experts-fusion --dtype bfloat16 --enable-attn-tp-input-scattered
@@ -277,10 +410,10 @@ do
--port 8001 --trust-remote-code --dist-init-addr DIP1:5000 --nnodes 2 --node-rank $i --tp-size 32 --dp-size 8 \
--mem-fraction-static 0.75 --max-running-requests 32 --attention-backend ascend --device npu --quantization modelslim \
--moe-a2a-backend deepep --enable-dp-attention --deepep-mode low_latency --enable-dp-lm-head --moe-dense-tp 1 \
--cuda-graph-bs 2 4 6 --disaggregation-transfer-backend ascend --watchdog-timeout 9000 --context-length 8192 \
--cuda-graph-bs 2 4 6 --disaggregation-transfer-backend ascend --watchdog-timeout 9000 \
--speculative-algorithm NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4 \
--tokenizer-worker-num 4 --disable-shared-experts-fusion --dtype bfloat16 \
--load-balance-method decode_round_robin
--load-balance-method round_robin
NODE_RANK=$i
break
fi
@@ -289,6 +422,7 @@ done
```
```shell
export SGLANG_DP_ROUND_ROBIN=1
python -m sglang_router.launch_router \
--pd-disaggregation \
--policy cache_aware \
@@ -305,10 +439,18 @@ python -m sglang_router.launch_router \
We tested it based on the `RANDOM` dataset.
```shell
python -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 6688 --max-concurrency 32 --random-input-len 6000 --random-output-len 1600 --num-prompts 32 --random-range-ratio 1
python -m sglang.bench_serving --dataset-name random --backend sglang \
--host 127.0.0.1 \
--port 6688 \
--max-concurrency 32 \
--random-input-len 6000 \
--random-output-len 1600 \
--num-prompts 32 \
--random-range-ratio 1 \
--request-rate 16
```
### DeepSeek-R1 3_9K-1K 20ms on A3 32 Cards Disaggregation Mode
### DeepSeek-R1 3_9K-1K 19ms on A3 32 Cards Disaggregation Mode
Model: Deepseek R1
@@ -320,21 +462,120 @@ Dataset: random
Input Output Length: 3.9K+1K
TPOT: 20ms
TPOT: 19ms
#### Model Deployment
Please Turn to [DeepSeek-R1 6K-1_6K 20ms on A3 32 Cards Disaggregation Mode](#deepseek-r1-6k-1_6k-20ms-on-a3-32-cards-disaggregation-mode)
```shell
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 ASCEND_LAUNCH_BLOCKING
source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh
export PATH=/usr/local/Ascend/8.5.0/compiler/bishengir/bin:$PATH
export SGLANG_SET_CPU_AFFINITY=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export STREAMS_PER_DEVICE=32
export SGLANG_NPU_USE_MLAPO=1
export SGLANG_USE_FIA_NZ=1
export ASCEND_MF_STORE_URL="tcp://your prefill ip1:24669"
P_IP=('your prefill ip1' 'your prefill ip2')
D_IP=('your decode ip1' 'your decode ip2')
MODEL_PATH=xxx
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 HCCL_BUFFSIZE=1536
export DEEP_NORMAL_MODE_USE_INT8_QUANT=1
export TASK_QUEUE_ENABLE=2
export HCCL_SOCKET_IFNAME=lo
export GLOO_SOCKET_IFNAME=lo
python -m sglang.launch_server --model-path ${MODEL_PATH} --disaggregation-mode prefill --host ${P_IP[$i]} \
--port 8000 --disaggregation-bootstrap-port $((8998+$i)) --trust-remote-code --nnodes 1 --node-rank 0 \
--tp-size 16 --mem-fraction-static 0.81 --attention-backend ascend --device npu --quantization modelslim \
--disaggregation-transfer-backend ascend --max-running-requests 4 --context-length 8192 --disable-radix-cache \
--chunked-prefill-size -1 --max-prefill-tokens 28680 --moe-a2a-backend deepep --deepep-mode normal \
--speculative-algorithm NEXTN --speculative-num-steps 1 --speculative-eagle-topk 1 --speculative-num-draft-tokens 2 \
--dp-size 2 --enable-dp-attention --disable-shared-experts-fusion --dtype bfloat16 --enable-attn-tp-input-scattered
NODE_RANK=$i
break
fi
done
# decode
for i in "${!D_IP[@]}";
do
if [[ "$LOCAL_HOST1" == "${D_IP[$i]}" || "$LOCAL_HOST2" == "${D_IP[$i]}" ]];
then
echo "${D_IP[$i]}"
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export SGLANG_ENABLE_SPEC_V2=1
export HCCL_BUFFSIZE=650
export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=12
export TASK_QUEUE_ENABLE=1
export SGLANG_SCHEDULER_SKIP_ALL_GATHER=1
export HCCL_SOCKET_IFNAME=xxx
export GLOO_SOCKET_IFNAME=xxx
python -m sglang.launch_server --model-path ${MODEL_PATH} --disaggregation-mode decode --host ${D_IP[$i]} \
--port 8001 --trust-remote-code --dist-init-addr DIP1:5000 --nnodes 2 --node-rank $i --tp-size 32 --dp-size 16 \
--mem-fraction-static 0.75 --max-running-requests 32 --attention-backend ascend --device npu --quantization modelslim \
--moe-a2a-backend deepep --enable-dp-attention --deepep-mode low_latency --enable-dp-lm-head --moe-dense-tp 1 \
--cuda-graph-bs 2 4 6 --disaggregation-transfer-backend ascend --watchdog-timeout 9000 --context-length 8192 \
--speculative-algorithm NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4 \
--tokenizer-worker-num 4 --disable-shared-experts-fusion --dtype bfloat16 \
--load-balance-method round_robin
NODE_RANK=$i
break
fi
done
```
```shell
export SGLANG_DP_ROUND_ROBIN=1
python -m sglang_router.launch_router \
--pd-disaggregation \
--policy cache_aware \
--prefill http://P_IP:8000 8998 \
--prefill http://P_IP:8000 8999 \
--decode http://D_IP:8001 \
--host 127.0.0.1 \
--port 6688 \
--mini-lb
```
#### Benchmark
We tested it based on the `RANDOM` dataset.
```shell
python -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 6688 --max-concurrency 768 --random-input-len 3900 --random-output-len 1000 --num-prompts 768 --random-range-ratio 1 --request-rate 16
python -m sglang.bench_serving --dataset-name random --backend sglang \
--host 127.0.0.1 \
--port 6688 \
--max-concurrency 32 \
--random-input-len 3900 \
--random-output-len 1024 \
--num-prompts 32 \
--random-range-ratio 1 \
--request-rate 16
```
### DeepSeek-R1 3_5K-1_5K 20ms on A3 32 Cards Disaggregation Mode
### DeepSeek-R1 3_5K-1_5K 19ms on A3 32 Cards Disaggregation Mode
Model: Deepseek R1
@@ -346,21 +587,29 @@ Dataset: random
Input Output Length: 3.5K+1.5K
TPOT: 20ms
TPOT: 19ms
#### Model Deployment
Please Turn to [DeepSeek-R1 6K-1_6K 20ms on A3 32 Cards Disaggregation Mode](#deepseek-r1-6k-1_6k-20ms-on-a3-32-cards-disaggregation-mode)
Please Turn to [DeepSeek-R1 3_9K-1K 19ms on A3 32 Cards Disaggregation Mode](#deepseek-r1-3_9k-1k-19ms-on-a3-32-cards-disaggregation-mode)
#### Benchmark
We tested it based on the `RANDOM` dataset.
```bash
python -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 6688 --max-concurrency 768 --random-input-len 3500 --random-output-len 1500 --num-prompts 768 --random-range-ratio 1 --request-rate 16
```shell
python -m sglang.bench_serving --dataset-name random --backend sglang \
--host 127.0.0.1 \
--port 6688 \
--max-concurrency 32 \
--random-input-len 3500 \
--random-output-len 1500 \
--num-prompts 32 \
--random-range-ratio 1 \
--request-rate 16
```
### DeepSeek-R1 3_5K-1K 20ms on A3 32 Cards Disaggregation Mode
### DeepSeek-R1 3_5K-1K 19ms on A3 32 Cards Disaggregation Mode
Model: Deepseek R1
@@ -372,18 +621,26 @@ Dataset: random
Input Output Length: 3.5K+1K
TPOT: 20ms
TPOT: 19ms
#### Model Deployment
Please Turn to [DeepSeek-R1 6K-1_6K 20ms on A3 32 Cards Disaggregation Mode](#deepseek-r1-6k-1_6k-20ms-on-a3-32-cards-disaggregation-mode)
Please Turn to [DeepSeek-R1 3_9K-1K 19ms on A3 32 Cards Disaggregation Mode](#deepseek-r1-3_9k-1k-19ms-on-a3-32-cards-disaggregation-mode)
#### Benchmark
We tested it based on the `RANDOM` dataset.
```shell
python -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 6688 --max-concurrency 768 --random-input-len 3500 --random-output-len 1000 --num-prompts 768 --random-range-ratio 1 --request-rate 16
python -m sglang.bench_serving --dataset-name random --backend sglang \
--host 127.0.0.1 \
--port 6688 \
--max-concurrency 32 \
--random-input-len 3500 \
--random-output-len 1024 \
--num-prompts 32 \
--random-range-ratio 1 \
--request-rate 16
```
### DeepSeek-R1 2K-2K 50ms on A3 8 Cards Mixed Mode
@@ -462,7 +719,7 @@ python3 -m sglang.launch_server --model-path ${MODEL_PATH} \
We tested it based on the `RANDOM` dataset.
```shell
python -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 6699 --max-concurrency 256 --random-input-len 2048 --random-output-len 2048 --num-prompts 1024 --random-range-ratio 1
python -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 6699 --max-concurrency 352 --random-input-len 2048 --random-output-len 2048 --num-prompts 1408 --random-range-ratio 1
```
### DeepSeek-R1 2K-2K 50ms on A3 16 Cards Disaggregation Mode
@@ -523,7 +780,7 @@ do
if [[ "$LOCAL_HOST1" == "${P_IP[$i]}" || "$LOCAL_HOST2" == "${P_IP[$i]}" ]];
then
echo "${P_IP[$i]}"
export HCCL_BUFFSIZE=1536
export HCCL_BUFFSIZE=2600
export DEEP_NORMAL_MODE_USE_INT8_QUANT=1
export TASK_QUEUE_ENABLE=2
@@ -531,11 +788,11 @@ do
export GLOO_SOCKET_IFNAME=lo
python -m sglang.launch_server --model-path ${MODEL_PATH} --disaggregation-mode prefill --host ${P_IP[$i]} \
--port 8000 --disaggregation-bootstrap-port $((8998+$i)) --trust-remote-code --nnodes 1 --node-rank 0 \
--tp-size 16 --mem-fraction-static 0.8 --attention-backend ascend --device npu --quantization modelslim \
--disaggregation-transfer-backend ascend --max-running-requests 8 --context-length 8192 --disable-radix-cache \
--chunked-prefill-size 32768 --max-prefill-tokens 28680 --moe-a2a-backend deepep --deepep-mode normal \
--tp-size 16 --mem-fraction-static 0.7 --attention-backend ascend --device npu --quantization modelslim \
--disaggregation-transfer-backend ascend --max-running-requests 32 --context-length 8192 --disable-radix-cache \
--chunked-prefill-size -1 --max-prefill-tokens 10240 --moe-a2a-backend deepep --deepep-mode normal \
--speculative-algorithm NEXTN --speculative-num-steps 1 --speculative-eagle-topk 1 --speculative-num-draft-tokens 2 \
--dp-size 1 --enable-dp-attention --disable-shared-experts-fusion --dtype bfloat16
--dp-size 8 --enable-dp-attention --disable-shared-experts-fusion --dtype bfloat16
NODE_RANK=$i
break
fi
@@ -549,20 +806,20 @@ do
echo "${D_IP[$i]}"
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export SGLANG_ENABLE_SPEC_V2=1
export HCCL_BUFFSIZE=720
export HCCL_BUFFSIZE=900
export SGLANG_DP_ROUND_ROBIN=1
export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=96
export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=112
export TASK_QUEUE_ENABLE=1
export HCCL_SOCKET_IFNAME=xxx
export GLOO_SOCKET_IFNAME=xxx
python -m sglang.launch_server --model-path ${MODEL_PATH} --disaggregation-mode decode --host ${D_IP[$i]} \
--port 8001 --trust-remote-code --nnodes 1 --node-rank 0 --tp-size 16 --dp-size 4 \
--mem-fraction-static 0.8 --max-running-requests 24 --attention-backend ascend --device npu --quantization modelslim \
--port 8001 --trust-remote-code --nnodes 1 --node-rank 0 --tp-size 16 --dp-size 16 \
--mem-fraction-static 0.8 --max-running-requests 448 --attention-backend ascend --device npu --quantization modelslim \
--moe-a2a-backend deepep --enable-dp-attention --deepep-mode low_latency --enable-dp-lm-head \
--cuda-graph-bs 8 10 12 14 16 18 20 22 24 --disaggregation-transfer-backend ascend --watchdog-timeout 9000 --context-length 8192 \
--cuda-graph-bs 2 4 6 8 10 12 14 16 18 20 22 24 26 28 --disaggregation-transfer-backend ascend --watchdog-timeout 9000 --context-length 8192 \
--speculative-algorithm NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4 \
--disable-shared-experts-fusion --dtype bfloat16 --tokenizer-worker-num 4 \
--load-balance-method round_robin
--load-balance-method round_robin
NODE_RANK=$i
break
fi
@@ -586,7 +843,7 @@ python -m sglang_router.launch_router \
We tested it based on the `RANDOM` dataset.
```shell
python -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 6688 --max-concurrency 400 --random-input-len 2048 --random-output-len 2048 --num-prompts 3200 --random-range-ratio 1 --request-rate 8
python -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 6688 --max-concurrency 448 --random-input-len 2048 --random-output-len 2048 --num-prompts 1792 --random-range-ratio 1 --request-rate 32
```
### DeepSeek-R1 3_5K-1_5K 50ms on A3 8 Cards Mixed Mode
@@ -662,7 +919,7 @@ python3 -m sglang.launch_server --model-path ${MODEL_PATH} \
We tested it based on the `RANDOM` dataset.
```shell
python -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 6699 --max-concurrency 144 --random-input-len 3500 --random-output-len 1500 --num-prompts 576 --random-range-ratio 1
python -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 6699 --max-concurrency 224 --random-input-len 3500 --random-output-len 1500 --num-prompts 896 --random-range-ratio 1
```
### DeepSeek-R1 3_5K-1_5K 50ms on A3 16 Cards Disaggregation Mode
@@ -785,7 +1042,7 @@ python -m sglang_router.launch_router \
We tested it based on the `RANDOM` dataset.
```shell
python -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 6688 --max-concurrency 384 --random-input-len 3500 --random-output-len 1500 --num-prompts 1536 --random-range-ratio 1
python -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 6688 --max-concurrency 416 --random-input-len 3500 --random-output-len 1500 --num-prompts 1664 --random-range-ratio 1
```
### DeepSeek-V3.2 128K-1K 26ms on A3 32 Cards Disaggregation Mode
@@ -1063,7 +1320,8 @@ done
```
```shell
SGLANG_DP_ROUND_ROBIN=1 python -m sglang_router.launch_router \
export SGLANG_DP_ROUND_ROBIN=1
python -m sglang_router.launch_router \
--pd-disaggregation \
--policy cache_aware \
--prefill http://PIP:8000 8995 \
@@ -2986,7 +3244,8 @@ export HCCL_OP_EXPANSION_MODE="AIV"
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export SGLANG_ENABLE_SPEC_V2=1
python3 -m sglang.launch_server --model-path ${MODEL_PATH} \--page-size 128 \
python3 -m sglang.launch_server --model-path ${MODEL_PATH} \
--page-size 128 \
--tp-size 4 \
--trust-remote-code \
--attention-backend ascend \
@@ -3080,7 +3339,8 @@ export HCCL_OP_EXPANSION_MODE="AIV"
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export SGLANG_ENABLE_SPEC_V2=1
python3 -m sglang.launch_server --model-path ${MODEL_PATH} \--page-size 128 \
python3 -m sglang.launch_server --model-path ${MODEL_PATH} \
--page-size 128 \
--tp-size 4 \
--trust-remote-code \
--attention-backend ascend \
@@ -3259,3 +3519,244 @@ We tested it based on the `RANDOM` dataset.
```shell
python3 -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 7239 --max-concurrency 144 --random-output-len 1500 --random-input-len 3500 --num-prompts 576 --random-range-ratio 1
```
### Qwen3-8B 3_5K-1_5K 50ms on A3 1 Cards Mixed Mode
Model: Qwen3-8B
Hardware: Atlas 800I A3 1Card
DeployMode: PD Mixed
Dataset: random
Input Output Length: 3.5K+1.5K
TPOT: 50ms
#### Model Deployment
```shell
echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl -w kernel.sched_migration_cost_ns=50000
unset https_proxy
unset http_proxy
unset HTTPS_PROXY
unset HTTP_PROXY
unset ASCEND_LAUNCH_BLOCKING
source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh
source /usr/local/Ascend/ascend-toolkit/latest/opp/vendors/customize/bin/set_env.bash
export PATH=/usr/local/Ascend/8.5.0/compiler/bishengir/bin:$PATH
MODEL_PATH=xxx
export SGLANG_SET_CPU_AFFINITY=1
export SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT=600
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_OP_EXPANSION_MODE="AIV"
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export SGLANG_ENABLE_SPEC_V2=1
export SGLANG_SCHEDULER_DECREASE_PREFILL_IDLE=1
export SGLANG_PREFILL_DELAYER_MAX_DELAY_PASSES=50
export HCCL_SOCKET_IFNAME=lo
export GLOO_SOCKET_IFNAME=lo
LOCAL_HOST1=`hostname -I|awk -F " " '{print$1}'`
LOCAL_HOST2=`hostname -I|awk -F " " '{print$2}'`
echo "${LOCAL_HOST1}"
echo "${LOCAL_HOST2}"
python -m sglang.launch_server --model-path $MODEL_PATH \
--host 127.0.0.1 --port 7239 --trust-remote-code --nnodes 1 --node-rank 0 \
--attention-backend ascend --device npu --quantization modelslim \
--disable-radix-cache --mem-fraction-static 0.9 \
--tp-size 1 \
--max-running-requests 70 \
--max-prefill-tokens 16384 \
--served-model-name Qwen3-8B \
--chunked-prefill-size 16384 \
--cuda-graph-bs 8 12 24 36 48 51 55 60 63 64 66 68 70 \
--dtype bfloat16 \
--speculative-draft-model-quantization unquant \
--speculative-algorithm EAGLE3 --speculative-draft-model-path xxx \
--speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4
```
#### Benchmark
We tested it based on the `RANDOM` dataset.
```shell
python3 -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 7239 --max-concurrency 64 --random-output-len 1500 --random-input-len 3500 --num-prompts 256 --random-range-ratio 1
```
### Qwen3-8B 3_5K-1_5K 5ms on A3 1 Cards Mixed Mode
Model: Qwen3-8B
Hardware: Atlas 800I A3 1Card
DeployMode: PD Mixed
Dataset: random
Input Output Length: 3.5K+1.5K
TPOT: 5ms
#### Model Deployment
```shell
echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl -w kernel.sched_migration_cost_ns=50000
unset https_proxy
unset http_proxy
unset HTTPS_PROXY
unset HTTP_PROXY
unset ASCEND_LAUNCH_BLOCKING
source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh
source /usr/local/Ascend/ascend-toolkit/latest/opp/vendors/customize/bin/set_env.bash
export PATH=/usr/local/Ascend/8.5.0/compiler/bishengir/bin:$PATH
MODEL_PATH=xxx
export SGLANG_SET_CPU_AFFINITY=1
export SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT=600
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export HCCL_OP_EXPANSION_MODE="AIV"
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export SGLANG_ENABLE_SPEC_V2=1
export HCCL_SOCKET_IFNAME=lo
export GLOO_SOCKET_IFNAME=lo
LOCAL_HOST1=`hostname -I|awk -F " " '{print$1}'`
LOCAL_HOST2=`hostname -I|awk -F " " '{print$2}'`
echo "${LOCAL_HOST1}"
echo "${LOCAL_HOST2}"
python -m sglang.launch_server --model-path $MODEL_PATH \
--host 127.0.0.1 --port 7239 --trust-remote-code --nnodes 1 --node-rank 0 \
--attention-backend ascend --device npu --quantization modelslim \
--disable-radix-cache --mem-fraction-static 0.894 \
--tp-size 2 \
--max-running-requests 1 \
--max-prefill-tokens 16384 \
--served-model-name Qwen3-8B \
--chunked-prefill-size -1 \
--cuda-graph-bs 1 \
--dtype bfloat16 \
--speculative-draft-model-quantization unquant \
--speculative-algorithm EAGLE3 --speculative-draft-model-path xxx \
--speculative-num-steps 4 --speculative-eagle-topk 1 --speculative-num-draft-tokens 5
```
#### Benchmark
We tested it based on the `RANDOM` dataset.
```shell
python3 -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 7239 --max-concurrency 1 --random-output-len 1500 --random-input-len 3500 --num-prompts 4 --random-range-ratio 1
```
### Qwen3-Next 3_5K-1_5K 20ms on A3 2 Cards Mixed Mode
Model: Qwen3-Next-80B-A3B-Instruct
Hardware: Atlas 800I A3 2Card
DeployMode: PD Mixed
Dataset: random
Input Output Length: 3.5K+1.5K
TPOT: 20ms
#### Model Deployment
```shell
echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl -w kernel.sched_migration_cost_ns=50000
unset https_proxy
unset http_proxy
unset HTTPS_PROXY
unset HTTP_PROXY
unset ASCEND_LAUNCH_BLOCKING
source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh
source /usr/local/Ascend/ascend-toolkit/latest/opp/vendors/customize/bin/set_env.bash
export PATH=/usr/local/Ascend/8.5.0/compiler/bishengir/bin:$PATH
export SGLANG_SET_CPU_AFFINITY=1
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export STREAMS_PER_DEVICE=32
export DEEP_NORMAL_MODE_USE_INT8_QUANT=1
export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=400
export DEEPEP_NORMAL_LONG_SEQ_ROUND=10
export DEEPEP_NORMAL_LONG_SEQ_PER_ROUND_TOKENS=2048
export HCCL_OP_EXPANSION_MODE="AIV"
export TASK_QUEUE_ENABLE=1
export ASCEND_USE_FIA=1
export SGLANG_NPU_USE_MULTI_STREAM=0
export SGLANG_WARMUP_TIMEOUT=3600
export SGLANG_ENABLE_SPEC_V2=1
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export FORCE_DRAFT_MODEL_NON_QUANT=1
export HCCL_BUFFSIZE=2000
export ZBCCL_LOCAL_MEM_SIZE=60416
export SGLANG_ENABLE_TP_MEMORY_INBALANCE_CHECK=0
export ZBCCL_BOOTSTRAP_URL=tcp://127.0.0.1:24669
export ZBCCL_NPU_ALLOC_CONF=use_vmm_for_static_memory:True
export ZBCCL_ENABLE_GRAPH=1
export HCCL_SOCKET_IFNAME=lo
export GLOO_SOCKET_IFNAME=lo
MODEL_PATH=xxx
LOCAL_HOST1=`hostname -I|awk -F " " '{print$1}'`
LOCAL_HOST2=`hostname -I|awk -F " " '{print$2}'`
echo "${LOCAL_HOST1}"
echo "${LOCAL_HOST2}"
python3 -m sglang.launch_server --model-path ${MODEL_PATH} \
--page-size 128 \
--tp-size 4 --dp-size 2 \
--trust-remote-code \
--attention-backend ascend \
--device npu \
--quantization modelslim \
--watchdog-timeout 9000 \
--host 127.0.0.1 --port 6699 \
--mem-fraction-static 0.85 \
--disable-radix-cache --max-prefill-tokens 28672 --context-length 26384 --max-total-tokens 122304 \
--enable-dp-attention --enable-dp-lm-head \
--speculative-algorithm NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4 --speculative-draft-model-quantization unquant \
--chunked-prefill-size -1 --max-running-requests 16 \
--cuda-graph-bs 2 4 8 \
--mamba-ssm-dtype bfloat16 \
--speculative-draft-model-path /path/to/Qwen3-Next-80B-A3B-Instruct
```
#### Benchmark
We tested it based on the `RANDOM` dataset.
```shell
python3 -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 6699 --random-range-ratio 1 --max-concurrency 1 --random-output-len 1500 --random-input-len 3500 --num-prompts 1
```