sglang/docs_new/docs/basic_usage/llama4.mdx

---
title: "Llama4 Usage"
metatags:
    description: "Deploy Llama 4 Scout (109B) and Maverick (400B) with SGLang: up to 10M context, hybrid KV cache, vision support. Optimized for H100/H200 GPUs."
---
[Llama 4](https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD) is Meta's latest generation of open-source LLM model with industry-leading performance.

SGLang has supported Llama 4 Scout (109B) and Llama 4 Maverick (400B) since [v0.4.5](https://github.com/sgl-project/sglang/releases/tag/v0.4.5).

Ongoing optimizations are tracked in the [Roadmap](https://github.com/sgl-project/sglang/issues/5118).

## Launch Llama 4 with SGLang

To serve Llama 4 models on 8xH100/H200 GPUs:

```bash Command
python3 -m sglang.launch_server \
  --model-path meta-llama/Llama-4-Scout-17B-16E-Instruct \
  --tp 8 \
  --context-length 1000000
```

### Configuration Tips

- **OOM Mitigation**: Adjust `--context-length` to avoid a GPU out-of-memory issue. For the Scout model, we recommend setting this value up to 1M on 8\*H100 and up to 2.5M on 8\*H200. For the Maverick model, we don't need to set context length on 8\*H200. When hybrid kv cache is enabled, `--context-length` can be set up to 5M on 8\*H100 and up to 10M on 8\*H200 for the Scout model.

- **Attention Backend Auto-Selection**: SGLang automatically selects the optimal attention backend for Llama 4 based on your hardware. You typically don't need to specify `--attention-backend` manually:
  - **Blackwell GPUs (B200/GB200)**: `trtllm_mha`
  - **Hopper GPUs (H100/H200)**: `fa3`
  - **AMD GPUs**: `aiter`
  - **Intel XPU**: `intel_xpu`
  - **Other platforms**: `triton` (fallback)

  To override the auto-selection, explicitly specify `--attention-backend` with one of the supported backends: `fa3`, `aiter`, `triton`, `trtllm_mha`, or `intel_xpu`.

- **Chat Template**: Add `--chat-template llama-4` for chat completion tasks.
- **Enable Multi-Modal**: Add `--enable-multimodal` for multi-modal capabilities.
- **Enable Hybrid-KVCache**: Set `--swa-full-tokens-ratio` to adjust the ratio of SWA layer (for Llama4, it's local attention layer) KV tokens / full layer KV tokens. (default: 0.8, range: 0-1)


### EAGLE Speculative Decoding
**Description**: SGLang has supported Llama 4 Maverick (400B) with [EAGLE speculative decoding](../advanced_features/speculative_decoding#EAGLE-Decoding).

**Usage**:
Add arguments `--speculative-draft-model-path`, `--speculative-algorithm`, `--speculative-num-steps`, `--speculative-eagle-topk` and `--speculative-num-draft-tokens` to enable this feature. For example:
```text Output
python3 -m sglang.launch_server \
  --model-path meta-llama/Llama-4-Maverick-17B-128E-Instruct \
  --speculative-algorithm EAGLE3 \
  --speculative-draft-model-path nvidia/Llama-4-Maverick-17B-128E-Eagle3 \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --trust-remote-code \
  --tp 8 \
  --context-length 1000000
```

- **Note** The Llama 4 draft model *nvidia/Llama-4-Maverick-17B-128E-Eagle3* can only recognize conversations in chat mode.

## Benchmarking Results

### Accuracy Test with `lm_eval`

The accuracy on SGLang for both Llama4 Scout and Llama4 Maverick can match the [official benchmark numbers](https://ai.meta.com/blog/llama-4-multimodal-intelligence/).

Benchmark results on MMLU Pro dataset with 8*H100:
<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "34%"}} />
    <col style={{width: "33%"}} />
    <col style={{width: "33%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}></th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Llama-4-Scout-17B-16E-Instruct</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Llama-4-Maverick-17B-128E-Instruct</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Official Benchmark</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>74.3</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>80.5</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>SGLang</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>75.2</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>80.7</td>
    </tr>
  </tbody>
</table>

Commands:

```bash Command
# Llama-4-Scout-17B-16E-Instruct model
python -m sglang.launch_server \
  --model-path meta-llama/Llama-4-Scout-17B-16E-Instruct \
  --port 30000 \
  --tp 8 \
  --mem-fraction-static 0.8 \
  --context-length 65536
lm_eval --model local-chat-completions --model_args model=meta-llama/Llama-4-Scout-17B-16E-Instruct,base_url=http://localhost:30000/v1/chat/completions,num_concurrent=128,timeout=999999,max_gen_toks=2048 --tasks mmlu_pro --batch_size 128 --apply_chat_template --num_fewshot 0

# Llama-4-Maverick-17B-128E-Instruct
python -m sglang.launch_server \
  --model-path meta-llama/Llama-4-Maverick-17B-128E-Instruct \
  --port 30000 \
  --tp 8 \
  --mem-fraction-static 0.8 \
  --context-length 65536
lm_eval --model local-chat-completions --model_args model=meta-llama/Llama-4-Maverick-17B-128E-Instruct,base_url=http://localhost:30000/v1/chat/completions,num_concurrent=128,timeout=999999,max_gen_toks=2048 --tasks mmlu_pro --batch_size 128 --apply_chat_template --num_fewshot 0
```

Details can be seen in [this PR](https://github.com/sgl-project/sglang/pull/5092).