> ## 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.

# Ascend NPU Ring-SP Performance (Wan2.1-T2V-1.3B)

This page reports Ring-SP performance on Ascend NPU with `torch_npu==2.10.0`.

* Baseline config: `ulysses=1, ring=1` (short: `u1r1`)
* Ring-SP config: `ulysses=1, ring=2` (short: `u1r2`)

## Benchmark Setup

* Model: `Wan2.1-T2V-1.3B-Diffusers`
* Prompt: `"a cat is playing piano"`
* Framework command: `sglang generate`
* Runtime: `torch_npu==2.10.0`

## Generate Commands

### Baseline (`u1r1`)

```bash theme={null}
sglang generate --model-path /nas/disk1/Wan2.1-T2V-1.3B-Diffusers \
    --prompt "a cat is playing piano" --num-gpus 1 --ring-degree 1 \
    --save-output
```

### Ring-SP (`u1r2`)

```bash theme={null}
sglang generate --model-path /nas/disk1/Wan2.1-T2V-1.3B-Diffusers \
    --prompt "a cat is playing piano" --num-gpus 2 --ring-degree 2 \
    --save-output
```

## Benchmarks

Benchmark Disclaimer

These numbers are from one fixed setup and one prompt case. Actual performance may vary by model settings, environment, and workload.

### 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>0.0003</td>
      <td>0.0002</td>
      <td>0.67x</td>
    </tr>

    <tr>
      <td>TextEncoding</td>
      <td>3.5936</td>
      <td>3.5820</td>
      <td>1.00x</td>
    </tr>

    <tr>
      <td>LatentPreparation</td>
      <td>0.0007</td>
      <td>0.0055</td>
      <td>7.86x</td>
    </tr>

    <tr>
      <td>TimestepPreparation</td>
      <td>0.0008</td>
      <td>0.0007</td>
      <td>0.88x</td>
    </tr>

    <tr>
      <td>Denoising</td>
      <td>121.2788</td>
      <td>239.2580</td>
      <td>1.97x</td>
    </tr>

    <tr>
      <td>Decoding</td>
      <td>13.8685</td>
      <td>16.4969</td>
      <td>1.19x</td>
    </tr>

    <tr>
      <td><strong>Total (Pixel data generated)</strong></td>
      <td><strong>141.86</strong></td>
      <td><strong>266.50</strong></td>
      <td><strong>1.88x</strong></td>
    </tr>
  </tbody>
</table>

## Summary

* With `torch_npu==2.10.0`, Ring-SP (`u1r2`) runs successfully on NPU for this case.
* End-to-end generation time improves from `266.50s` to `141.86s` (`1.88x`).
* The main gain comes from `DenoisingStage` (`1.97x`), while decoding also improves (`1.19x`).