> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sglang.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Ring SP Benchmark: Wan2.2-TI2V-5B (u1r2 vs Baseline)

This page reports Ring-SP performance for `Wan2.2-TI2V-5B-Diffusers` using:

* Parallel config: `sp=2, ulysses=1, ring=2` (short: `u1r2`)
* Baseline config: `sp=1, ulysses=1, ring=1` (short: `u1r1`)

## Benchmark Setup

* Model: `Wan2.2-TI2V-5B-Diffusers`
* GPU: `48G RTX40 series * 2`

## Online Serving

### Ring SP (`u1r2`)

```bash theme={null}
sglang serve \
  --model-type diffusion \
  --model-path /model/HuggingFace/Wan-AI/Wan2.2-TI2V-5B-Diffusers \
  --num-gpus 2 --sp-degree 2 --ulysses-degree 1 --ring-degree 2 \
  --port 8898
```

### Baseline (`u1r1`)

```bash theme={null}
sglang serve \
  --model-type diffusion \
  --model-path /model/HuggingFace/Wan-AI/Wan2.2-TI2V-5B-Diffusers \
  --num-gpus 1 --sp-degree 1 --ulysses-degree 1 --ring-degree 1 \
  --port 8898
```

## Benchmarks

### Benchmark Disclaimer

These benchmarks are provided for reference under one specific setup and command configuration. Actual performance may vary with model settings, runtime environment, and request patterns.

### Stage Time Breakdown

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "25%"}} />

    <col style={{width: "25%"}} />

    <col style={{width: "25%"}} />

    <col style={{width: "25%"}} />
  </colgroup>

  <thead>
    <tr>
      <th>Stage / Metric</th>
      <th><code>u1r2</code> (s)</th>
      <th><code>u1r1</code> baseline (s)</th>
      <th>Speedup</th>
    </tr>
  </thead>

  <tbody>
    <tr>
      <td>InputValidation</td>
      <td>0.1060</td>
      <td>0.1029</td>
      <td>0.97x</td>
    </tr>

    <tr>
      <td>TextEncoding</td>
      <td>1.3965</td>
      <td>2.2261</td>
      <td>1.59x</td>
    </tr>

    <tr>
      <td>LatentPreparation</td>
      <td>0.0002</td>
      <td>0.0002</td>
      <td>1.00x</td>
    </tr>

    <tr>
      <td>TimestepPreparation</td>
      <td>0.0003</td>
      <td>0.0004</td>
      <td>1.33x</td>
    </tr>

    <tr>
      <td>Denoising</td>
      <td>52.6358</td>
      <td>71.6785</td>
      <td>1.36x</td>
    </tr>

    <tr>
      <td>Decoding</td>
      <td>7.6708</td>
      <td>13.4314</td>
      <td>1.75x</td>
    </tr>

    <tr>
      <td><strong>Total</strong></td>
      <td><strong>63.74</strong></td>
      <td><strong>90.63</strong></td>
      <td><strong>1.42x</strong></td>
    </tr>
  </tbody>
</table>

### Memory Usage

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "25%"}} />

    <col style={{width: "25%"}} />

    <col style={{width: "25%"}} />

    <col style={{width: "25%"}} />
  </colgroup>

  <thead>
    <tr>
      <th>Memory Metric</th>
      <th><code>u1r2</code> (GB)</th>
      <th><code>u1r1</code> baseline (GB)</th>
      <th>Delta</th>
    </tr>
  </thead>

  <tbody>
    <tr>
      <td>Peak GPU Memory</td>
      <td>20.07</td>
      <td>27.40</td>
      <td>-7.33</td>
    </tr>

    <tr>
      <td>Peak Allocated</td>
      <td>13.35</td>
      <td>20.40</td>
      <td>-7.05</td>
    </tr>

    <tr>
      <td>Memory Overhead</td>
      <td>6.72</td>
      <td>7.00</td>
      <td>-0.28</td>
    </tr>

    <tr>
      <td>Overhead Ratio</td>
      <td>33.5%</td>
      <td>25.6%</td>
      <td>+7.9pp</td>
    </tr>
  </tbody>
</table>

## Summary

* End-to-end latency improves from `90.63s` to `63.74s` (`1.42x`).
* Main gains come from `Denoising` (`1.36x`) and `Decoding` (`1.75x`).
* Absolute memory usage drops noticeably on Ring-SP (`Peak GPU Memory -7.33GB`, `Peak Allocated -7.05GB`).
* Overhead ratio rises (`+7.9pp`), so future tuning can focus on reducing communication/runtime overhead while preserving the latency gain.