# Llama4 Usage

[Llama 4](https://github.com/meta-llama/llama-models/blob/main/models/llama4/MODEL_CARD.md) 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
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.

- **Chat Template**: Add `--chat-template llama-4` for chat completion tasks.
- **Enable Multi-Modal**: Add `--enable-multimodal` for multi-modal capabilities.
- **Enable Hybrid-KVCache**: Add `--hybrid-kvcache-ratio` for hybrid kv cache. Details can be seen in [this PR](https://github.com/sgl-project/sglang/pull/6563)


### EAGLE Speculative Decoding
**Description**: SGLang has supported Llama 4 Maverick (400B) with [EAGLE speculative decoding](https://docs.sglang.ai/backend/speculative_decoding.html#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:
```
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:
|                    | Llama-4-Scout-17B-16E-Instruct | Llama-4-Maverick-17B-128E-Instruct  |
|--------------------|--------------------------------|-------------------------------------|
| Official Benchmark | 74.3                           | 80.5                                |
| SGLang             | 75.2                           | 80.7                                |

Commands:

```bash
# 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).
