# DSpark vs SGLang EAGLE 投机解码对比报告

- DSpark 结果：`/data/user1/yy/bench_results/dspark_st_comparison_20260707-150649`
- EAGLE 结果：`/data/user1/yy/bench_results/eagle_grid/focused`
- DSpark 模型：`/data/models/DeepSeek-V4-Flash-DSpark`
- EAGLE 模型：`/data/models/DeepSeek-V4-Flash`
- DSpark 后端：vllm-dspark (TP=8, FP8 KV cache, spec-method=dspark)
- EAGLE 后端：SGLang (TP=8, FP8 KV cache, speculative-algorithm=EAGLE, num_steps=3, topk=1, draft_tokens=4)
- 压测客户端：`sglang.bench_serving`
- Warmup：100 条

## 核心指标对比

| Scenario | Concurrency | Method | Total tok/s | Out tok/s | Req/s | Mean E2E(ms) | P95 E2E(ms) | P99 E2E(ms) | Mean TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P99 TPOT(ms) |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| chat_short | 1 | DSpark(st=5) | 1094.51 | 397.37 | 2.73 | 365.29 | 743.89 | 782.98 | 64.42 | 174.36 | 2.06 | 3.40 |
| chat_short | 1 | EAGLE | 272.82 | 99.05 | 0.68 | 1467.67 | 3775.96 | 5175.09 | 223.86 | 405.20 | 7.47 | 19.74 |
| chat_short | 8 | DSpark(st=3) | 4705.85 | 1525.06 | 11.55 | 672.66 | 1269.82 | 1552.65 | 76.20 | 221.18 | 4.67 | 8.43 |
| chat_short | 8 | EAGLE | 1059.99 | 343.52 | 2.60 | 3043.16 | 7143.57 | 8345.08 | 245.91 | 528.86 | 21.41 | 36.02 |
| chat_short | 16 | DSpark(st=5) | 6282.94 | 2171.58 | 15.59 | 997.37 | 2147.86 | 2496.56 | 107.56 | 240.46 | 7.09 | 21.93 |
| chat_short | 16 | EAGLE | 1506.42 | 520.67 | 3.74 | 4240.11 | 8031.57 | 10192.48 | 236.19 | 438.56 | 29.12 | 54.02 |
| chat_short | 32 | DSpark(st=3) | 8275.55 | 2754.96 | 20.79 | 1497.40 | 2944.48 | 3658.90 | 144.47 | 453.29 | 10.72 | 23.97 |
| chat_short | 32 | EAGLE | 2461.23 | 819.35 | 6.18 | 4731.11 | 9926.81 | 11388.85 | 243.84 | 571.89 | 34.22 | 54.92 |
| chat_short | 64 | DSpark(st=5) | 9586.66 | 3191.43 | 24.09 | 2555.12 | 5387.59 | 6559.69 | 274.71 | 610.40 | 18.47 | 42.08 |
| chat_short | 64 | EAGLE | 4474.88 | 1489.70 | 11.24 | 5239.80 | 10296.02 | 11922.06 | 268.27 | 452.80 | 38.17 | 61.24 |
| chat_standard | 64 | DSpark(st=3) | 15680.46 | 3288.58 | 24.70 | 2483.46 | 4798.63 | 5693.88 | 271.41 | 947.15 | 17.93 | 46.05 |
| chat_standard | 64 | EAGLE | 6746.24 | 1414.85 | 10.63 | 5637.63 | 10693.92 | 12907.15 | 296.86 | 597.18 | 41.12 | 69.08 |
| decode_heavy | 1 | DSpark(st=5) | 673.37 | 531.76 | 0.55 | 1802.00 | 2957.04 | 3676.66 | 59.06 | 114.78 | 1.92 | 3.03 |
| decode_heavy | 1 | EAGLE | 261.35 | 206.39 | 0.22 | 4643.62 | 10452.02 | 18810.32 | 162.91 | 314.71 | 4.84 | 11.69 |
| decode_heavy | 32 | DSpark(st=5) | 7203.28 | 5737.73 | 5.52 | 5595.02 | 10412.11 | 12522.00 | 107.12 | 331.08 | 5.43 | 9.15 |
| decode_heavy | 32 | EAGLE | 2981.50 | 2374.90 | 2.28 | 13707.03 | 27159.67 | 30638.81 | 192.78 | 442.78 | 13.23 | 19.26 |
| generation_standard | 64 | DSpark(st=3) | 13363.77 | 6705.88 | 13.27 | 4542.22 | 8273.38 | 9355.10 | 190.34 | 813.58 | 9.00 | 15.36 |
| generation_standard | 64 | EAGLE | 5609.33 | 2814.73 | 5.57 | 11057.53 | 22577.31 | 26713.41 | 275.48 | 944.52 | 21.88 | 34.80 |
| long_context_probe | 1 | DSpark(st=5) | 11835.70 | 392.31 | 1.44 | 693.30 | 1172.84 | 1404.17 | 267.61 | 561.65 | 1.53 | 1.88 |
| long_context_probe | 1 | EAGLE | 5556.95 | 184.19 | 0.68 | 1477.16 | 2792.68 | 3836.17 | 238.26 | 618.74 | 4.43 | 8.62 |
| long_context_probe | 4 | DSpark(st=5) | 39893.35 | 1322.32 | 4.85 | 806.55 | 1390.87 | 1500.22 | 143.42 | 236.41 | 2.41 | 3.54 |
| long_context_probe | 4 | EAGLE | 6660.56 | 220.77 | 0.81 | 4777.51 | 9442.45 | 10071.75 | 433.60 | 1230.20 | 14.85 | 21.65 |
| long_context_probe | 8 | DSpark(st=5) | 29361.32 | 900.89 | 3.56 | 2223.29 | 4693.10 | 5345.72 | 425.03 | 1241.57 | 7.08 | 19.83 |
| long_context_probe | 8 | EAGLE | 9890.47 | 303.47 | 1.20 | 6232.13 | 16324.27 | 20329.21 | 606.76 | 2671.06 | 22.79 | 54.38 |
| rag_medium | 1 | DSpark(st=5) | 4075.47 | 496.09 | 1.76 | 566.58 | 968.90 | 1134.15 | 110.34 | 156.35 | 1.64 | 2.34 |
| rag_medium | 1 | EAGLE | 1326.02 | 161.41 | 0.57 | 1742.43 | 3804.17 | 3936.47 | 195.07 | 337.55 | 5.83 | 12.84 |
| rag_medium | 8 | DSpark(st=5) | 15838.39 | 1692.13 | 6.69 | 1175.26 | 2263.59 | 3106.42 | 154.24 | 352.19 | 4.14 | 8.36 |
| rag_medium | 8 | EAGLE | 3676.32 | 392.77 | 1.55 | 4908.89 | 11659.24 | 12566.81 | 254.65 | 678.44 | 18.53 | 35.36 |
| rag_medium | 32 | DSpark(st=5) | 27420.79 | 3062.92 | 11.91 | 2626.21 | 5182.78 | 6873.72 | 253.09 | 522.80 | 9.17 | 18.55 |
| rag_medium | 32 | EAGLE | 13268.07 | 1482.05 | 5.76 | 5444.98 | 10471.13 | 11990.37 | 337.62 | 2204.45 | 20.09 | 39.98 |
| stress_standard | 64 | DSpark(st=3) | 15225.66 | 3193.19 | 23.99 | 2569.66 | 5006.16 | 5831.81 | 261.98 | 710.57 | 18.02 | 36.01 |
| stress_standard | 64 | EAGLE | 5399.35 | 1132.38 | 8.51 | 6678.22 | 13086.21 | 14811.42 | 314.38 | 584.25 | 48.19 | 81.06 |
| stress_standard | 96 | DSpark(st=3) | 24069.73 | 4756.72 | 37.72 | 2433.73 | 4827.07 | 6001.29 | 272.61 | 749.99 | 18.04 | 38.52 |
| stress_standard | 96 | EAGLE | 8833.26 | 1745.65 | 13.84 | 6792.34 | 14000.22 | 16722.55 | 360.14 | 959.71 | 52.90 | 96.70 |
| stress_standard | 128 | DSpark(st=3) | 29828.36 | 5942.48 | 47.45 | 2581.42 | 5162.16 | 6222.01 | 298.69 | 970.61 | 19.09 | 40.93 |
| stress_standard | 128 | EAGLE | 13189.80 | 2627.70 | 20.98 | 5960.86 | 12256.06 | 14219.75 | 416.12 | 2153.71 | 47.20 | 81.35 |

## 吞吐 Winner 统计（按 Total tok/s）

| Scenario | Concurrency | Winner | DSpark Total tok/s | EAGLE Total tok/s | 提升 |
|---|---:|---:|---:|---:|---:|
| chat_short | 1 | DSpark(st=5) | 1094.51 | 272.82 | 301.18% |
| chat_short | 8 | DSpark(st=3) | 4705.85 | 1059.99 | 343.95% |
| chat_short | 16 | DSpark(st=5) | 6282.94 | 1506.42 | 317.08% |
| chat_short | 32 | DSpark(st=3) | 8275.55 | 2461.23 | 236.24% |
| chat_short | 64 | DSpark(st=5) | 9586.66 | 4474.88 | 114.23% |
| chat_standard | 64 | DSpark(st=3) | 15680.46 | 6746.24 | 132.43% |
| decode_heavy | 1 | DSpark(st=5) | 673.37 | 261.35 | 157.65% |
| decode_heavy | 32 | DSpark(st=5) | 7203.28 | 2981.50 | 141.60% |
| generation_standard | 64 | DSpark(st=3) | 13363.77 | 5609.33 | 138.24% |
| long_context_probe | 1 | DSpark(st=5) | 11835.70 | 5556.95 | 112.99% |
| long_context_probe | 4 | DSpark(st=5) | 39893.35 | 6660.56 | 498.95% |
| long_context_probe | 8 | DSpark(st=5) | 29361.32 | 9890.47 | 196.86% |
| rag_medium | 1 | DSpark(st=5) | 4075.47 | 1326.02 | 207.35% |
| rag_medium | 8 | DSpark(st=5) | 15838.39 | 3676.32 | 330.82% |
| rag_medium | 32 | DSpark(st=5) | 27420.79 | 13268.07 | 106.67% |
| stress_standard | 64 | DSpark(st=3) | 15225.66 | 5399.35 | 181.99% |
| stress_standard | 96 | DSpark(st=3) | 24069.73 | 8833.26 | 172.49% |
| stress_standard | 128 | DSpark(st=3) | 29828.36 | 13189.80 | 126.15% |

## 关键发现

- 对比点数：18
- EAGLE 胜出：0 个
- DSpark 胜出：18 个

**结论：在相同负载下，vllm-dspark 的综合吞吐优于 SGLang EAGLE。**

### 延迟与稳定性观察

- 平均 Mean E2E 比值（EAGLE / DSpark）：3.09
- 平均 Mean TPOT 比值（EAGLE / DSpark）：3.21
- 平均 Mean TTFT 比值（EAGLE / DSpark）：1.81

> 比值 < 1 表示 EAGLE 更快；> 1 表示 DSpark 更快。

## 优化建议

1. 如果以吞吐为首要目标，当前配置下 **vllm-dspark 更优**，建议继续使用 `--spec-tokens` 并根据并发选择 3（高并发）或 5（低并发/长上下文）。
2. SGLang EAGLE 本次表现不佳，平均延迟和 TPOT 均显著高于 DSpark；如需进一步评估 EAGLE，可尝试调整 `--speculative-num-steps`、`--speculative-eagle-topk`、`--speculative-num-draft-tokens` 或更换 MOE runner backend，并确认是否已完成 `sglang.compile_deep_gemm` 预编译。
3. 注意两套后端的实现差异（CUDA graph、KV cache 管理、调度器、draft 模型架构）会显著影响不同并发和输入长度下的表现，建议按实际业务负载做最终选型。