推理性能
模型性能数据
以下数据来自 SGLang-MUSA 0.5.10+20260630 在 M1000 AImodule 端侧环境的实测结果。均在性能模式 + compute_only 环境下测试,数值供参考。
测试环境
- 端设备:M1000 AImodule,64G 统一内存
- 系统版本:V1.4.1
- MUSA 版本:5.1.1
- 服务参数:
max-total-tokens=16384 - 文本测试并发:1
- 文本测试输入 / 输出:无前缀场景为 128/100、250/100、500/100、1000/100、2000/100;带 prefix 场景为 500/100、1000/100、2000/100、4000/100、8000/100、16000/100,其中
PrefillLen=250
测试说明
测试指标说明如下:
Prefill(tps):Prefill 阶段吞吐,单位 token/s。TTFT(ms):首 token 延迟,单位 ms。Decode(tps):Decode 阶段吞吐,单位 token/s。TPOT(ms):单个输出 token 平均耗时,单位 ms。Profile:测试 profile,格式为prefill_len:input_len。
性能复现流程
解压得到安装包目录:
tar -zxvf SGLang-MUSA-M1000-M1000_1.4.1_sglang.tar.gz
cd 20260718_M1000_1.4.1_sglang
修改 scripts/ci/musa/tilelang_workflow_config.json 中的模型路径为实际路径,比如:
"model_paths": [
"/home/dev/models/Qwen3-30B-A3B-GPTQ-Int4",
"/home/dev/models/Qwen3.5-35B-A3B-GPTQ-Int4"
]
生成工作流配置并加载环境变量:
scripts/ci/musa/generate_tilelang_workflow.sh
source scripts/ci/musa/setup_env.sh
首次运行或更新模型、profile、kernel 配置后,需要预编译 TileLang kernel:
scripts/ci/musa/precompile_tilelang_kernels.sh
分别启动服务并运行 benchmark。每组测试使用两个终端:终端 1 启动并保持服务运行,终端 2 进入同一仓库后运行对应的 benchmark。benchmark 通过 MODEL_KEY 选择对应模型配置。
Qwen3-30B-A3B:
终端 1:
scripts/ci/musa/start_qwen3_server.sh
终端 2:
MODEL_KEY=qwen3 scripts/ci/musa/run_tilelang_benchmark.sh
Qwen3.5-35B-A3B:
终端 1:
scripts/ci/musa/start_qwen35_server.sh
终端 2:
MODEL_KEY=qwen35 scripts/ci/musa/run_tilelang_benchmark.sh
Qwen3.5_FQ-35B-A3B:
终端 1:
bash scripts/ci/musa/start_qwen35_fq_server.sh
终端 2:
MODEL_KEY=qwen35_fq scripts/ci/musa/run_tilelang_benchmark.sh
Qwen3-30B-A3B
| Model | Size | PrefillLen(tokens) | Ctx(K) | Batch | NChip | Input(tokens) | Output(tokens) | Prefill(tps) | TTFT(ms) | Decode(tps) | TPOT(ms) | Profile | Prefix(tokens) | MeasuredOutput(tokens) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Qwen3 | 30b_a3b | 128 | 16 | 1 | 1 | 128 | 100 | 53.21 | 2405.54 | 25.688 | 38.929 | 128:128 | 0 | 100 |
| Qwen3 | 30b_a3b | 250 | 16 | 1 | 1 | 250 | 100 | 69.869 | 3578.129 | 25.225 | 39.643 | 250:250 | 0 | 100 |
| Qwen3 | 30b_a3b | 500 | 16 | 1 | 1 | 500 | 100 | 83.732 | 5971.4 | 24.211 | 41.304 | 500:500 | 0 | 100 |
| Qwen3 | 30b_a3b | 1000 | 16 | 1 | 1 | 1000 | 100 | 91.74 | 10900.316 | 23.622 | 42.333 | 1000:1000 | 0 | 100 |
| Qwen3 | 30b_a3b | 2000 | 16 | 1 | 1 | 2000 | 100 | 89.865 | 22255.656 | 22.904 | 43.66 | 2000:2000 | 0 | 100 |
| Qwen3 | 30b_a3b | 250 | 16 | 1 | 1 | 500 | 100 | 42.006 | 5951.564 | 24.184 | 41.35 | 250:500 | 250 | 100 |
| Qwen3 | 30b_a3b | 250 | 16 | 1 | 1 | 1000 | 100 | 40.789 | 6129.116 | 23.646 | 42.291 | 250:1000 | 750 | 100 |
| Qwen3 | 30b_a3b | 250 | 16 | 1 | 1 | 2000 | 100 | 40.551 | 6165.144 | 22.881 | 43.704 | 250:2000 | 1750 | 100 |
| Qwen3 | 30b_a3b | 250 | 16 | 1 | 1 | 4000 | 100 | 60.451 | 4135.607 | 21.621 | 46.251 | 250:4000 | 3750 | 100 |
| Qwen3 | 30b_a3b | 250 | 16 | 1 | 1 | 8000 | 100 | 54.081 | 4622.7 | 19.48 | 51.334 | 250:8000 | 7750 | 100 |
| Qwen3 | 30b_a3b | 250 | 16 | 1 | 1 | 16000 | 100 | 44.348 | 5637.225 | 16.298 | 61.357 | 250:16000 | 15750 | 100 |
Qwen3.5-35B-A3B
| Model | Size | PrefillLen(tokens) | Ctx(K) | Batch | NChip | Input(tokens) | Output(tokens) | Prefill(tps) | TTFT(ms) | Decode(tps) | TPOT(ms) | Profile | Prefix(tokens) | MeasuredOutput(tokens) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Qwen3.5 | 35b_a3b | 128 | 16 | 1 | 1 | 128 | 100 | 64.743 | 1977.056 | 15.498 | 64.525 | 128:128 | 0 | 100 |
| Qwen3.5 | 35b_a3b | 250 | 16 | 1 | 1 | 250 | 100 | 109.407 | 2285.049 | 15.39 | 64.977 | 250:250 | 0 | 100 |
| Qwen3.5 | 35b_a3b | 500 | 16 | 1 | 1 | 500 | 100 | 168.278 | 2971.28 | 15.034 | 66.514 | 500:500 | 0 | 100 |
| Qwen3.5 | 35b_a3b | 1000 | 16 | 1 | 1 | 1000 | 100 | 228.109 | 4383.87 | 14.878 | 67.211 | 1000:1000 | 0 | 100 |
| Qwen3.5 | 35b_a3b | 2000 | 16 | 1 | 1 | 2000 | 100 | 226.784 | 8818.956 | 14.978 | 66.763 | 2000:2000 | 0 | 100 |
| Qwen3.5 | 35b_a3b | 250 | 16 | 1 | 1 | 500 | 100 | 87.485 | 2857.638 | 15.139 | 66.055 | 250:500 | 250 | 100 |
| Qwen3.5 | 35b_a3b | 250 | 16 | 1 | 1 | 1000 | 100 | 85.979 | 2907.687 | 15.134 | 66.077 | 250:1000 | 750 | 100 |
| Qwen3.5 | 35b_a3b | 250 | 16 | 1 | 1 | 2000 | 100 | 85.768 | 2914.834 | 15.028 | 66.542 | 250:2000 | 1750 | 100 |
| Qwen3.5 | 35b_a3b | 250 | 16 | 1 | 1 | 4000 | 100 | 81.367 | 3072.506 | 14.865 | 67.274 | 250:4000 | 3750 | 100 |
| Qwen3.5 | 35b_a3b | 250 | 16 | 1 | 1 | 8000 | 100 | 93.369 | 2677.554 | 14.58 | 68.589 | 250:8000 | 7750 | 100 |
| Qwen3.5 | 35b_a3b | 250 | 16 | 1 | 1 | 16000 | 100 | 80.808 | 3093.741 | 14.02 | 71.325 | 250:16000 | 15750 | 100 |
Qwen3.5_FQ-35B-A3B
| Model | Size | PrefillLen(tokens) | Ctx(K) | Batch | NChip | Input(tokens) | Output(tokens) | Prefill(tps) | TTFT(ms) | Decode(tps) | TPOT(ms) | Profile | Prefix(tokens) | MeasuredOutput(tokens) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Qwen3.5_FQ | 35b_a3b | 128 | 16 | 1 | 1 | 128 | 100 | 79.267 | 1614.799 | 21.747 | 45.984 | 128:128 | 0 | 100 |
| Qwen3.5_FQ | 35b_a3b | 250 | 16 | 1 | 1 | 250 | 100 | 90.277 | 2769.244 | 21.466 | 46.586 | 250:250 | 0 | 100 |
| Qwen3.5_FQ | 35b_a3b | 500 | 16 | 1 | 1 | 500 | 100 | 95.952 | 5210.931 | 21.047 | 47.512 | 500:500 | 0 | 100 |
| Qwen3.5_FQ | 35b_a3b | 1000 | 16 | 1 | 1 | 1000 | 100 | 98.129 | 10190.623 | 20.883 | 47.885 | 1000:1000 | 0 | 100 |
| Qwen3.5_FQ | 35b_a3b | 2000 | 16 | 1 | 1 | 2000 | 100 | 96.036 | 20825.607 | 20.37 | 49.092 | 2000:2000 | 0 | 100 |
| Qwen3.5_FQ | 35b_a3b | 250 | 16 | 1 | 1 | 500 | 100 | 72.803 | 3433.938 | 20.934 | 47.77 | 250:500 | 250 | 100 |
| Qwen3.5_FQ | 35b_a3b | 250 | 16 | 1 | 1 | 1000 | 100 | 74.391 | 3360.609 | 20.743 | 48.209 | 250:1000 | 750 | 100 |
| Qwen3.5_FQ | 35b_a3b | 250 | 16 | 1 | 1 | 2000 | 100 | 77.626 | 3220.563 | 20.365 | 49.104 | 250:2000 | 1750 | 99 |
| Qwen3.5_FQ | 35b_a3b | 250 | 16 | 1 | 1 | 4000 | 100 | 70.845 | 3528.835 | 20.211 | 49.477 | 250:4000 | 3750 | 100 |
| Qwen3.5_FQ | 35b_a3b | 250 | 16 | 1 | 1 | 8000 | 100 | 73.805 | 3387.314 | 19.748 | 50.637 | 250:8000 | 7750 | 100 |
| Qwen3.5_FQ | 35b_a3b | 250 | 16 | 1 | 1 | 16000 | 100 | 64.087 | 3900.927 | 18.583 | 53.811 | 250:16000 | 15750 | 100 |

