[NPU][Doc] add qwen3-30b-a3b low latency example (#22446)
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@@ -41,6 +41,8 @@ you encounter issues or have any questions, please [open an issue](https://githu
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| 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) |
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| 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) |
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| Qwen3-8B | Atlas 800I A3 | 1 | PD Mixed | 6K+1.5K | 9ms | W8A8 INT8 | [Optimal Configuration](#qwen3-8b-6k-1_5k-9ms-on-a3-1-cards-mixed-mode) |
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| 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) |
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| Qwen3-30B-A3B | Atlas 800I A3 | 1 | PD Mixed | 1K+0.3K | 8ms | W8A8 INT8 | [Optimal Configuration](#qwen3-30b-a3b-1k-0_3k-8ms-on-a3-1-cards-mixed-mode) |
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### High Throughput
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@@ -2779,3 +2781,131 @@ We tested it based on the `RANDOM` dataset.
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```shell
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python3 -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 7239 --max-concurrency 120 --random-output-len 2000 --random-input-len 2000 --num-prompts 120 --random-range-ratio 1
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```
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### Qwen3-30B-A3B 6K-1_5K 10ms on A3 1 Cards Mixed Mode
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Model: Qwen3-30B-A3B
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Hardware: Atlas 800I A3 1Card
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DeployMode: PD Mixed
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Dataset: random
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Input Output Length: 6K+1.5K
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TPOT: 10ms
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#### Model Deployment
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```shell
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export SGLANG_SET_CPU_AFFINITY=1
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unset https_proxy
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unset http_proxy
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unset HTTPS_PROXY
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unset HTTP_PROXY
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unset ASCEND_LAUNCH_BLOCKING
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export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
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MODEL_PATH=xxx
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export SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT=600
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LOCAL_HOST1=`hostname -I|awk -F " " '{print$1}'`
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LOCAL_HOST2=`hostname -I|awk -F " " '{print$2}'`
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echo "${LOCAL_HOST1}"
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echo "${LOCAL_HOST2}"
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export HCCL_BUFFSIZE=400
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export HCCL_SOCKET_IFNAME=lo
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export GLOO_SOCKET_IFNAME=lo
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export HCCL_OP_EXPANSION_MODE="AIV"
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export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
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export SGLANG_ENABLE_SPEC_V2=1
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python -m sglang.launch_server --model-path $MODEL_PATH \
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--host 127.0.0.1 --port 7339 --trust-remote-code --nnodes 1 --node-rank 0 \
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--attention-backend ascend --device npu --quantization modelslim \
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--max-running-requests 16 \
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--disable-radix-cache \
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--speculative-draft-model-quantization unquant \
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--speculative-algorithm EAGLE3 --speculative-draft-model-path xxx --speculative-draft-model-quantization unquant \
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--speculative-num-steps 4 --speculative-eagle-topk 1 --speculative-num-draft-tokens 5 \
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--chunked-prefill-size -1 --max-prefill-tokens 35000 \
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--tp-size 2 --mem-fraction-static 0.6 --cuda-graph-bs 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 --dtype bfloat16
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```
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#### Benchmark
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We tested it based on the `RANDOM` dataset.
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```shell
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python3 -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 7339 --random-range-ratio 1 --max-concurrency 16 --random-output-len 1500 --random-input-len 6144 --num-prompts 16
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```
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### Qwen3-30B-A3B 1K-0_3K 8ms on A3 1 Cards Mixed Mode
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Model: Qwen3-30B-A3B
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Hardware: Atlas 800I A3 1Card
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DeployMode: PD Mixed
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Dataset: random
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Input Output Length: 1K+0.3K
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TPOT: 8ms
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#### Model Deployment
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```shell
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export SGLANG_SET_CPU_AFFINITY=1
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unset https_proxy
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unset http_proxy
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unset HTTPS_PROXY
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unset HTTP_PROXY
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unset ASCEND_LAUNCH_BLOCKING
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export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
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MODEL_PATH=xxx
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export SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT=600
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LOCAL_HOST1=`hostname -I|awk -F " " '{print$1}'`
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LOCAL_HOST2=`hostname -I|awk -F " " '{print$2}'`
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echo "${LOCAL_HOST1}"
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echo "${LOCAL_HOST2}"
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export HCCL_BUFFSIZE=400
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export HCCL_SOCKET_IFNAME=lo
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export GLOO_SOCKET_IFNAME=lo
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export HCCL_OP_EXPANSION_MODE="AIV"
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export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
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export SGLANG_ENABLE_SPEC_V2=1
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python -m sglang.launch_server --model-path $MODEL_PATH \
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--host 127.0.0.1 --port 7339 --trust-remote-code --nnodes 1 --node-rank 0 \
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--attention-backend ascend --device npu --quantization modelslim \
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--max-running-requests 8 \
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--disable-radix-cache \
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--speculative-draft-model-quantization unquant \
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--speculative-algorithm EAGLE3 --speculative-draft-model-path xxx --speculative-draft-model-quantization unquant \
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--speculative-num-steps 4 --speculative-eagle-topk 1 --speculative-num-draft-tokens 5 \
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--chunked-prefill-size -1 --max-prefill-tokens 35000 \
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--tp-size 2 --mem-fraction-static 0.7 --cuda-graph-bs 1 2 3 4 5 6 7 8 --dtype bfloat16
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```
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#### Benchmark
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We tested it based on the `RANDOM` dataset.
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```shell
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python3 -m sglang.bench_serving --dataset-name random --backend sglang --host 127.0.0.1 --port 7339 --random-range-ratio 1 --max-concurrency 8 --random-output-len 300 --random-input-len 1024 --num-prompts 8
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```
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