sglang/docs/cookbook/autoregressive/Meituan/LongCat-2.0.mdx

---
title: LongCat-2.0
description: "Deploy LongCat-2.0-FP8 with SGLang - config-driven recipes for Meituan's 1.6T sparse MoE model on B300, B200, H200, and H20 GPUs."
tag: NEW
---

## Deployment

<a id="install" />

<Accordion title="Install SGLang">

For all methods and hardware platforms, see the [official SGLang installation guide](../../../docs/get-started/install). LongCat-2.0 support is on SGLang `main`; use a nightly wheel or rolling nightly Docker image until the next tagged release includes it. The two paths below match the **Python / Docker** toggle in the command panel.

<Tabs>

<Tab title="Python (pip / uv)">

```bash Command
pip install --upgrade pip
pip install uv

# Choose the nightly wheel index for your CUDA runtime.
SGLANG_WHL_INDEX=https://docs.sglang.ai/whl/cu130  # B300 / CUDA 13
# SGLANG_WHL_INDEX=https://docs.sglang.ai/whl/cu129  # CUDA 12.9

uv pip install --prerelease=allow --extra-index-url "${SGLANG_WHL_INDEX}" "sglang[all]"
```

Then run the **Python** output of the command panel below in that environment.

</Tab>

<Tab title="Docker">

```bash Command
# Choose the rolling nightly image for your hardware.
SGLANG_DOCKER_IMAGE=lmsysorg/sglang:dev-cu13  # B300 / CUDA 13
# SGLANG_DOCKER_IMAGE=lmsysorg/sglang:dev  # Other supported hardware

docker pull "${SGLANG_DOCKER_IMAGE}"
```

For how to launch the image, see [Install -> Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker). Substitute the inner `sglang serve ...` with what the command generator below produces.

</Tab>

</Tabs>

</Accordion>

Pick your hardware + recipe to generate the launch command. LongCat-2.0 currently exposes one model-card-aligned serving strategy:

- **Balanced** - the validated B300 recipe and the 2-node H200/B200/H20 topology use TP/EP parallelism with LongCat sparse attention prefill.

import { Deployment } from "/src/snippets/_deployment.jsx";
import { config }     from "/src/snippets/configs/meituan-longcat/longcat-2.0.jsx";
import { benchmarks } from "/src/snippets/configs/meituan-longcat/longcat-2.0-benchmarks.jsx";

<Deployment config={config} benchmarks={benchmarks} />

<Warning>
  All recipes here run the LongCat sparse-attention indexer top-k on the default `--dsa-topk-backend sgl-kernel`. Other top-k backend choices have not been fully validated on LongCat-2.0.
</Warning>

<Note>
  The B300 single-node recipe was validated end-to-end with CUDA graph capture enabled. H200, B200, and H20 are shown as 2-node recipes because LongCat-2.0-FP8 needs 16 ranks for those GPU memory profiles.
</Note>

## Playground

The Playground is where you experiment with **SGLang features beyond the verified matrix**. The Deploy panel above only emits combinations the SGLang team has signed off on; the Playground lets you turn on additional knobs on top of whichever cell the Deploy panel is currently showing.

import { Playground } from "/src/snippets/_playground.jsx";

<Playground config={config} />

## 1. Model Introduction

[LongCat-2.0-FP8](https://huggingface.co/meituan-longcat/LongCat-2.0-FP8) is the FP8 checkpoint of Meituan LongCat-2.0, a large sparse Mixture-of-Experts language model with 1.6T total parameters and about 48B activated parameters per token. It combines LongCat Sparse Attention (LSA), expert parallel MoE layers, and an n-gram/token-table embedding path for serving long-context workloads efficiently.

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Model</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Architecture</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Serving precision</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px"}}><strong><a href="https://huggingface.co/meituan-longcat/LongCat-2.0-FP8">LongCat-2.0-FP8</a></strong></td>
      <td style={{padding: "9px 12px"}}>Sparse MoE · LongCat Sparse Attention · n-gram embedding</td>
      <td style={{padding: "9px 12px"}}>FP8 weights, BF16 KV cache</td>
    </tr>
  </tbody>
</table>

**Resources:** [LongCat-2.0-FP8](https://huggingface.co/meituan-longcat/LongCat-2.0-FP8).

## 2. Configuration Tips

- **Remote code.** Use `--trust-remote-code` for the Hugging Face checkpoint.
- **Topology.** The 8x B300 recipe uses TP=8 and EP=8. H200, B200, and H20 use a 2-node 16 GPU layout with TP=16 and EP=16; the command panel injects the multi-node rank flags for you.
- **LongCat sparse attention.** Keep `--dsa-prefill-backend fa3` with `--chunked-prefill-size 2048` for the model-card-aligned prefill path.
- **Memory.** The recipe uses `--kv-cache-dtype bfloat16` and starts at `--mem-fraction-static 0.92`. Tune memory only after the generated command launches cleanly on your cluster.
- **Weight loading.** `--model-loader-extra-config '{"enable_multithread_load":true,"num_threads":12}'` loads checkpoint shards in parallel and reduces startup time.
- **FP8 backend selection.** Do not pass `--fp8-gemm-runner-backend` manually. SGLang selects the correct backend for the LongCat FP8 scale layout.
- **Host, port, and ranks.** Use the command panel environment fields for `HOST_IP`, `PORT`, `NODE0_IP`, and `NODE_RANK` instead of hardcoding them in the recipe.

## 3. Advanced Usage

### 3.1 Test the deployment

<Accordion title="Chat completion example (cURL)">

```bash Command
curl http://localhost:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meituan-longcat/LongCat-2.0-FP8",
    "messages": [
      {"role": "user", "content": "A shop has 17 apples and sells 8. Then it buys 6 more. How many apples are there? Answer with only the final number."}
    ],
    "max_tokens": 32,
    "chat_template_kwargs": {"enable_thinking": false}
  }'
```

</Accordion>

<Accordion title="Expected output">

```text Output
15
```

</Accordion>

<Accordion title="OpenAI-compatible client (Python)">

```python Example
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY",
)

response = client.chat.completions.create(
    model="meituan-longcat/LongCat-2.0-FP8",
    messages=[
        {
            "role": "user",
            "content": "Solve: A shop has 17 apples and sells 8, then buys 6 more. Answer with only the final number.",
        }
    ],
    max_tokens=32,
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)

print(response.choices[0].message.content)
```

</Accordion>

<Accordion title="Example output">

```text Output
15
```

</Accordion>

## 4. Validation

The B300 recipe was validated with `meituan-longcat/LongCat-2.0-FP8` on 8x B300 using the command generated above.

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Evaluation</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Examples</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Accuracy</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px"}}>GSM8K</td>
      <td style={{padding: "9px 12px"}}>200</td>
      <td style={{padding: "9px 12px"}}>98.0%</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px"}}>GSM8K</td>
      <td style={{padding: "9px 12px"}}>1314</td>
      <td style={{padding: "9px 12px"}}>95.8904109589041%</td>
    </tr>
  </tbody>
</table>

CUDA graph was enabled, and decode CUDA graph capture completed successfully during serving validation.