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+---
+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
+
+
+
+
+
+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.
+
+
+
+
+
+```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 "${SGLANG_WHL_INDEX}" "sglang[all]"
+```
+
+Then run the **Python** output of the command panel below in that environment.
+
+
+
+
+
+```bash Command
+# Choose the rolling nightly image for your hardware.
+SGLANG_DOCKER_IMAGE=lmsysorg/sglang:dev-cu13 # B300 / CUDA 13
+# SGLANG_DOCKER_IMAGE=lmsysorg/sglang:dev # Other supported hardware
+
+docker pull "${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.
+
+
+
+
+
+
+
+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";
+
+
+
+
+ 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.
+
+
+## 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";
+
+
+
+## 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.
+
+
+
+**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 `--nsa-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
+
+
+
+```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}
+ }'
+```
+
+
+
+
+
+```text Output
+15
+```
+
+
+
+
+
+```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)
+```
+
+
+
+
+
+```text Output
+15
+```
+
+
+
+## 4. Validation
+
+The B300 recipe was validated with `meituan-longcat/LongCat-2.0-FP8` on 8x B300 using the command generated above.
+
+