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Merge branch 'main' into fix/sparse_mla
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.github/workflows/benchmark/nvidia/nv3.6-qwen3.6/all_perf.py

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SERVER_HOST = "127.0.0.1"
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SERVER_PORT = 8000
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MODEL_NAME = "qwen36"
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TOKENIZER_PATH = "/root/flagrelease/qwen3.6/Qwen3.6-35B-A3B-nomtp"
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TOKENIZER_PATH = "/root/flagrelease/qwen3.6/Qwen3.6-27B"
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# =============================================================================
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# 默认测试参数

.github/workflows/benchmark/nvidia/nv3.6-qwen3.6/perf.sh

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nvidia-smi -i "$GPU_DEVICE_LIST" -lgc 1830,1830
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nvidia-smi --query-gpu=index,name,clocks.gr,clocks.mem,utilization.gpu --format=csv
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#numactl --cpunodebind=1 --membind=1 \
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#python3 ${SCRIPT_DIR}/all_perf.py --input-len=32768 --output-len=1024 --concurrency=64
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# numactl --cpunodebind=1 --membind=1 \
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# python3 ${SCRIPT_DIR}/all_perf.py --input-len=32768 --output-len=1024 --concurrency=16
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numactl --cpunodebind=1 --membind=1 \
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python3 ${SCRIPT_DIR}/all_perf.py --input-len=16384 --output-len=1024 --concurrency=64
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python3 ${SCRIPT_DIR}/all_perf.py --input-len=16384 --output-len=1024 --concurrency=16
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numactl --cpunodebind=1 --membind=1 \
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python3 ${SCRIPT_DIR}/all_perf.py --input-len=4096 --output-len=1024 --concurrency=64
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python3 ${SCRIPT_DIR}/all_perf.py --input-len=4096 --output-len=1024 --concurrency=16
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nvidia-smi -i "$GPU_DEVICE_LIST" -rgc
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nvidia-smi --query-gpu=index,name,clocks.gr,clocks.mem,utilization.gpu --format=csv
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H100 * 4
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vllm==0.16.0rc2.dev221+ga5ccc85c8
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torch==2.10.0+cu128
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cuda==13.1
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[BaseLine] triton==3.6.0
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# 服务冷启动时间: 1分43秒
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# 输入长度: 32K, 输出长度: 1K, 并发数: 64
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Output token throughput (tok/s): 1218.17
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Total token throughput (tok/s): 40199.48
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Median TTFT (ms): 17179.61
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Median TPOT (ms): 33.82
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Median ITL (ms): 34.69
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# 输入长度: 16K, 输出长度: 1K, 并发数: 64
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Output token throughput (tok/s): 1846.06
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Total token throughput (tok/s): 31383.00
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Median TTFT (ms): 8332.61
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Median TPOT (ms): 25.63
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Median ITL (ms): 31.13
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# 输入长度: 4K, 输出长度: 1K, 并发数: 64
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Output token throughput (tok/s): 3129.82
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Total token throughput (tok/s): 15649.09
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Median TTFT (ms): 2077.27
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Median TPOT (ms): 18.29
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Median ITL (ms): 19.39
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[2026/07/11] flag_gems==5.3.0rc2 (2026/05), flagtree===0.6.0 (2026/06/26)
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# 服务冷启动时间: 7分13秒, 服务热启动时间: 2分3秒
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# 输入长度: 32K, 输出长度: 1K, 并发数: 64
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Output token throughput (tok/s): 827.43
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Total token throughput (tok/s): 27305.25
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Median TTFT (ms): 29391.30
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Median TPOT (ms): 46.98
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Median ITL (ms): 32.13
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# 输入长度: 16K, 输出长度: 1K, 并发数: 64
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Output token throughput (tok/s): 1355.01
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Total token throughput (tok/s): 23035.15
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Median TTFT (ms): 14824.90
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Median TPOT (ms): 32.08
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Median ITL (ms): 23.82
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# 输入长度: 4K, 输出长度: 1K, 并发数: 64
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Output token throughput (tok/s): 2345.05
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Total token throughput (tok/s): 11725.28
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Median TTFT (ms): 4025.38
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Median TPOT (ms): 23.45
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Median ITL (ms): 23.40
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H100 * 4
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H100 * 2
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vllm==0.16.0rc2.dev221+ga5ccc85c8
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torch==2.10.0+cu128
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cuda==13.1
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[BaseLine] triton==3.6.0
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# 服务冷启动时间: 1分43秒
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[BaseLine] USE_FLAGGEMS=0, triton==3.6.0
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# 服务冷启动时间: 2分23秒, 服务热启动时间: 2分4秒
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# 输入长度: 32K, 输出长度: 1K, 并发数: 64
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Output token throughput (tok/s): 1218.17
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Total token throughput (tok/s): 40199.48
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Median TTFT (ms): 17179.61
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Median TPOT (ms): 33.82
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Median ITL (ms): 34.69
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Output token throughput (tok/s): 319.46
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Total token throughput (tok/s): 10542.09
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Median TTFT (ms): 15798.41
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Median TPOT (ms): 34.20
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Median ITL (ms): 22.15
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# 输入长度: 16K, 输出长度: 1K, 并发数: 64
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Output token throughput (tok/s): 1846.06
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Total token throughput (tok/s): 31383.00
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Median TTFT (ms): 8332.61
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Median TPOT (ms): 25.63
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Median ITL (ms): 31.13
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Output token throughput (tok/s): 492.25
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Total token throughput (tok/s): 8368.25
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Median TTFT (ms): 7761.14
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Median TPOT (ms): 24.72
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Median ITL (ms): 20.41
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# 输入长度: 4K, 输出长度: 1K, 并发数: 64
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Output token throughput (tok/s): 3129.82
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Total token throughput (tok/s): 15649.09
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Median TTFT (ms): 2077.27
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Median TPOT (ms): 18.29
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Median ITL (ms): 19.39
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Output token throughput (tok/s): 734.11
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Total token throughput (tok/s): 3670.52
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Median TTFT (ms): 2117.86
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Median TPOT (ms): 19.74
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Median ITL (ms): 19.32
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[2026/07/11] flag_gems==5.3.0rc2 (2026/05), flagtree===0.6.0 (2026/06/26)
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# 服务冷启动时间: 7分13秒, 服务热启动时间: 2分3秒
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[2026/08/11] flag_gems==5.3.0rc2 (2026/05), triton==3.6.0
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# 服务冷启动时间: 9分13秒, 服务热启动时间: 2分2秒
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# 输入长度: 32K, 输出长度: 1K, 并发数: 64
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Output token throughput (tok/s): 827.43
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Total token throughput (tok/s): 27305.25
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Median TTFT (ms): 29391.30
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Median TPOT (ms): 46.98
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Median ITL (ms): 32.13
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Output token throughput (tok/s): 264.37
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Total token throughput (tok/s): 8724.08
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Median TTFT (ms): 19197.68
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Median TPOT (ms): 41.21
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Median ITL (ms): 26.92
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# 输入长度: 16K, 输出长度: 1K, 并发数: 64
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Output token throughput (tok/s): 1355.01
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Total token throughput (tok/s): 23035.15
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Median TTFT (ms): 14824.90
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Median TPOT (ms): 32.08
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Median ITL (ms): 23.82
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Output token throughput (tok/s): 402.67
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Total token throughput (tok/s): 6845.39
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Median TTFT (ms): 9507.34
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Median TPOT (ms): 30.22
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Median ITL (ms): 22.38
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# 输入长度: 4K, 输出长度: 1K, 并发数: 64
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Successful requests: 64.00
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Output token throughput (tok/s): 2345.05
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Total token throughput (tok/s): 11725.28
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Median TTFT (ms): 4025.38
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Median TPOT (ms): 23.45
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Median ITL (ms): 23.40
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Output token throughput (tok/s): 610.43
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Total token throughput (tok/s): 3052.17
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Median TTFT (ms): 2613.72
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Median TPOT (ms): 23.68
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Median ITL (ms): 20.30
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[2026/08/11] flag_gems==5.3.0rc2 (2026/05), flagtree===0.6.0 (2026/06/26)
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# 服务冷启动时间: 8分20秒, 服务热启动时间: 2分2秒
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# 输入长度: 32K, 输出长度: 1K, 并发数: 16
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Output token throughput (tok/s): 262.29
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Total token throughput (tok/s): 8655.47
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Median TTFT (ms): 20106.92
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Median TPOT (ms): 40.85
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Median ITL (ms): 24.00
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# 输入长度: 16K, 输出长度: 1K, 并发数: 16
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Output token throughput (tok/s): 398.28
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Total token throughput (tok/s): 6770.73
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Median TTFT (ms): 9918.11
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Median TPOT (ms): 30.24
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Median ITL (ms): 22.97
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# 输入长度: 4K, 输出长度: 1K, 并发数: 16
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Output token throughput (tok/s): 600.23
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Total token throughput (tok/s): 3001.12
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Median TTFT (ms): 2707.72
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Median TPOT (ms): 24.01
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Median ITL (ms): 25.02
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[2026/08/11] flag_gems==5.3.0rc2 (2026/05), flagtree===0.6.1 (2026/08/07)
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# 服务冷启动时间: 8分30秒, 服务热启动时间: 2分2秒
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# 输入长度: 32K, 输出长度: 1K, 并发数: 16
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Output token throughput (tok/s): 261.04
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Total token throughput (tok/s): 8614.48
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Median TTFT (ms): 18658.96
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Median TPOT (ms): 42.50
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Median ITL (ms): 26.28
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# 输入长度: 16K, 输出长度: 1K, 并发数: 16
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Output token throughput (tok/s): 418.64
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Total token throughput (tok/s): 7116.86
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Median TTFT (ms): 9186.20
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Median TPOT (ms): 28.97
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Median ITL (ms): 22.67
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# 输入长度: 4K, 输出长度: 1K, 并发数: 16
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Output token throughput (tok/s): 624.05
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Total token throughput (tok/s): 3120.26
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Median TTFT (ms): 2507.47
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Median TPOT (ms): 23.17
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Median ITL (ms): 21.83

.github/workflows/benchmark/nvidia/nv3.6-qwen3.6/start.sh

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export USE_RESHAPE_AND_CACHE_FLASH=1
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numactl --cpunodebind=1 --membind=1 \
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nohup vllm serve ./Qwen3.6-35B-A3B-nomtp/ \
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--tensor-parallel-size 4 \
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nohup vllm serve ./Qwen3.6-27B/ \
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--tensor-parallel-size 2 \
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--port 8000 \
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--served-model-name qwen36 \
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--mm-encoder-tp-mode data \
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--mm-processor-cache-type shm \
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--block-size 256 \
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--gpu-memory-utilization 0.7 \
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--gpu-memory-utilization 0.8 \
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--dtype bfloat16 2>&1 >vllm.log &
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echo "$!" >pid.txt
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