Move all experiments under hardware-specific folders: - experiments/h200/ : H200 GPU experiments (15 dirs) - experiments/h20/ : H20 GPU experiments (2 dirs) - experiments/p800/ : Kunlun P800 experiments (3 dirs) - experiments/pro6000/ : RTX 6000D experiments (2 dirs) This improves discoverability and keeps hardware-specific configs isolated from each other.
DSV4 / DeepSeek-V4-Flash 推理测速项目
记录 vllm-dspark、SGLang、vLLM 等后端在 DeepSeek-V4-Flash 上的 benchmark 脚本与最终结果。 H200 快速上手指南见
docs/H200_QUICKSTART.md。
目录说明
| 目录/文件 | 说明 |
|---|---|
platforms/ |
芯片/加速器平台配置(*.env),如 P800、H200、RTX 6000D |
scripts/common/ |
跨实验复用的 orchestration 组件(server 启停、health check、元数据生成、结果解析) |
scripts/ |
仅保留仍在使用的 legacy DSpark 脚本与 SLO 标准 |
experiments/ |
以实验为单位的自包含目录(脚本 + 配置 + 结果) |
experiments/legacy_bench_results/ |
历史归档的实验产物(只读),从原 bench_results/ 迁移而来 |
BENCHMARK_WORKFLOW.md |
benchmark 目录与命名规范 |
docs/SLO_STANDARDS.md |
推理服务 SLO 标准(TTFT/TPOT) |
注:
.gitignore已排除envs/、deps/、tmp/、datasets/、logs/、*.zip、原始raw_outputs/*.jsonl等大文件/中间文件。
实验索引
新结构(experiments/)
| 实验 | 脚本 | 说明 |
|---|---|---|
| DSV4 P800 SGLang | experiments/dsv4_p800_sglang/run_bench.sh |
Kunlun P800 + SGLang + DeepSeek-V4-Flash-INT8 |
| DSV4 H200 DSpark | experiments/dsv4_h200_dspark/run_bench.sh |
NVIDIA H200 + vllm-dspark + DeepSeek-V4-Flash-DSpark |
| DSV4 H200 vLLM baseline | experiments/dsv4_h200_vllm/run_bench.sh |
NVIDIA H200 + vLLM + DeepSeek-V4-Flash |
| DSV4 H200 SGLang baseline | experiments/dsv4_h200_sglang/run_bench.sh |
NVIDIA H200 + native SGLang + DeepSeek-V4-Flash |
| DSV4 H200 SGLang vs vLLM | experiments/dsv4_h200_sglang_vs_vllm/run_bench.sh |
NVIDIA H200 上 SGLang 与 vLLM 控制变量对比(TP=8,最长 200k 上下文) |
| DSV4 H200 vLLM DSpark vs default | experiments/dsv4_h200_vllm_dspark_vs_default/run_bench.sh |
vLLM 开启 DSpark 投机解码 vs 默认配置,验证 TTFT 差异 |
| DSV4 H200 max context length | experiments/dsv4_h200_max_context_length/run_bench.sh |
探索 SGLang / vLLM 在 H200 上能支持的最大输入长度 |
| DSV4 H200 SGLang TP/DP Matrix | experiments/dsv4_h200_sglang_tp_dp_matrix/run_bench.sh |
H200 上 SGLang 不同 TP×DP 配置的固定并发矩阵测试 |
| DSV4 H200 SGLang TP/DP Adaptive | experiments/dsv4_h200_sglang_tp_dp_matrix/run_adaptive_concurrency.sh |
H200 上 SGLang 不同 TP×DP 配置的自适应并发饱和点搜索 |
| DSV4 H200 vLLM TP/DP Matrix | experiments/dsv4_h200_vllm_tp_dp_matrix/run_bench.sh |
H200 上 vLLM 不同 TP×DP 配置的固定并发矩阵测试 |
| DSV4 H200 vLLM TP/DP Adaptive | experiments/dsv4_h200_vllm_tp_dp_matrix/run_adaptive_concurrency.sh |
H200 上 vLLM 不同 TP×DP 配置的自适应并发饱和点搜索 |
| DSV4 H200 64k SGLang vs vLLM | experiments/dsv4_h200_64k_sglang_vs_vllm/run_bench.sh |
H200 上 64k 上下文 SGLang 与 vLLM 对比 |
| DSV4 H200 256k 4k Probe | experiments/dsv4_h200_256k_4k_probe/run_bench.sh |
H200 上 256k 输入/4k 输出长上下文探测 |
| DSV4 H200 vLLM TP2 Custom Bench | experiments/dsv4_h200_vllm_tp2_custom_bench/run_bench.sh |
H200 上 vLLM TP=2 自定义压测客户端(多服务负载均衡) |
| DSV4 H200 vLLM TP4 Custom Bench | experiments/dsv4_h200_vllm_tp4_custom_bench/run_bench.sh |
H200 上 vLLM TP=4 自定义压测客户端 |
| DSV4 H200 vLLM TP8 Custom Bench | experiments/dsv4_h200_vllm_tp8_custom_bench/run_bench.sh |
H200 上 vLLM TP=8 自定义压测客户端 |
| DSV4 H200 vLLM MTP vs Default | experiments/dsv4_h200_vllm_mtp_vs_default/run_bench.sh |
H200 上 vLLM MTP(Multi-Token Prediction)vs 默认配置对比 |
| DSV4 H200 Long Context Matrix | experiments/dsv4_h200_long_context_matrix/run_bench.sh |
H200 上长上下文矩阵测试(多后端对比) |
| DSV4 RTX 6000D SGLang TP/DP Adaptive | experiments/dsv4_pro6000_sglang_tp_dp_matrix/run_adaptive_concurrency.sh |
RTX 6000D 上 SGLang 自适应并发饱和点搜索 |
| DSV4 RTX 6000D vLLM TP/DP Adaptive | experiments/dsv4_pro6000_vllm_tp_dp_matrix/run_adaptive_concurrency.sh |
RTX 6000D 上 vLLM 自适应并发饱和点搜索 |
旧结构(scripts/ + bench_results/)
仅保留还在被 experiments/dsv4_h200_dspark/ 引用的 legacy DSpark 脚本,其余旧脚本已删除。
| 实验 | 脚本 | 最终报告 | 说明 |
|---|---|---|---|
| DSpark grid benchmark | scripts/benchmark_dspark_0707/run_dspark_benchmark_grid.sh |
experiments/legacy_bench_results/dspark_grid_20260707-132641/report.md |
P1/P2/P3 全量网格 |
| DSpark spec-tokens 对比 | scripts/benchmark_dspark_0707/run_dspark_st_comparison.sh |
experiments/legacy_bench_results/dspark_st_comparison_20260707-150649/comparison_report.md |
--spec-tokens 3 vs 5 |
快速复现
P800 SGLang
bash experiments/dsv4_p800_sglang/run_bench.sh
H200 DSpark
bash experiments/dsv4_h200_dspark/run_bench.sh
H200 SGLang baseline
bash experiments/dsv4_h200_sglang/run_bench.sh
H200 SGLang vs vLLM 对比
bash experiments/dsv4_h200_sglang_vs_vllm/run_bench.sh
H200 vLLM DSpark vs default
bash experiments/dsv4_h200_vllm_dspark_vs_default/run_bench.sh
H200 SGLang TP/DP Matrix
bash experiments/dsv4_h200_sglang_tp_dp_matrix/run_bench.sh
H200 SGLang TP/DP 自适应并发搜索
bash experiments/dsv4_h200_sglang_tp_dp_matrix/run_adaptive_concurrency.sh
H200 vLLM TP/DP Matrix
bash experiments/dsv4_h200_vllm_tp_dp_matrix/run_bench.sh
H200 vLLM TP/DP 自适应并发搜索
bash experiments/dsv4_h200_vllm_tp_dp_matrix/run_adaptive_concurrency.sh
H200 64k SGLang vs vLLM
bash experiments/dsv4_h200_64k_sglang_vs_vllm/run_bench.sh
H200 256k 4k Probe
bash experiments/dsv4_h200_256k_4k_probe/run_bench.sh
H200 vLLM TP2/TP4/TP8 Custom Bench
bash experiments/dsv4_h200_vllm_tp2_custom_bench/run_bench.sh
bash experiments/dsv4_h200_vllm_tp4_custom_bench/run_bench.sh
bash experiments/dsv4_h200_vllm_tp8_custom_bench/run_bench.sh
H200 vLLM MTP vs Default
bash experiments/dsv4_h200_vllm_mtp_vs_default/run_bench.sh
H200 Long Context Matrix
bash experiments/dsv4_h200_long_context_matrix/run_bench.sh
RTX 6000D SGLang TP/DP 自适应并发搜索
bash experiments/dsv4_pro6000_sglang_tp_dp_matrix/run_adaptive_concurrency.sh
RTX 6000D vLLM TP/DP 自适应并发搜索
bash experiments/dsv4_pro6000_vllm_tp_dp_matrix/run_adaptive_concurrency.sh
DSpark grid / spec-tokens(旧结构)
bash scripts/benchmark_dspark_0707/run_dspark_benchmark_grid.sh
bash scripts/benchmark_dspark_0707/run_dspark_st_comparison.sh
解析已有结果
# 新结构
python3 experiments/dsv4_h200_dspark/parse_results.py \
experiments/dsv4_h200_dspark/results/<run_id>
# 旧结构
/data/user1/yy/envs/sglang/bin/python scripts/benchmark_dspark_0707/parse_results.py \
/data/user1/yy/experiments/legacy_bench_results/dspark_grid_20260707-132641
SLO 参考
详见 docs/SLO_STANDARDS.md。
主要关注指标:
- TTFT P95:S2(DeepSeek-V4-Flash 所在层)目标
< 3s - TPOT:S2 目标
< 50ms
环境要求
- NVIDIA H200:Python env
/data/user1/yy/envs/vllm-dspark(服务端)、/data/user1/yy/envs/sglang(压测客户端);模型/data/models/DeepSeek-V4-Flash、/data/models/DeepSeek-V4-Flash-DSpark。 - NVIDIA RTX 6000D:Python env
/root/.miniconda3/envs/sglang(压测客户端);模型/data/hf_models/DeepSeek-V4-Flash;Docker 镜像lmsysorg/sglang:latest、vllm/vllm-openai:latest。 - Kunlun P800:Docker 镜像
iregistry.baidu-int.com/xpu/sglang-p800-pd-disagg-0510:20260511_4202;模型/data1/models/DeepSeek-V4-Flash-INT8;压测客户端在容器内运行。
平台相关常量见 platforms/*.env。
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