# Qwen 3.5 Usage

Qwen 3.5 is Alibaba's latest generation LLM featuring a hybrid attention architecture, advanced MoE with shared experts, and native multimodal capabilities.

Key architecture features:
- **Hybrid Attention**: Gated Delta Networks (linear, O(n) complexity) combined with full attention every 4th layer for high associative recall
- **MoE with Shared Experts**: Top-8 active out of 64 routed experts plus a dedicated shared expert for universal features
- **Multimodal**: DeepStack Vision Transformer with Conv3d for native image and video understanding

## Launch Qwen 3.5 with SGLang

### Dense Model

To serve `Qwen/Qwen3.5-397B-A17B` on 8 GPUs:

```bash
python3 -m sglang.launch_server \
    --model-path Qwen/Qwen3.5-397B-A17B \
    --tp 8 \
    --trust-remote-code
```

### AMD GPU (MI300X / MI325X / MI35X)

On AMD Instinct GPUs, use the `triton` attention backend. Both the full attention layers and the Gated Delta Net (linear attention) layers use Triton-based kernels on ROCm:

```bash
SGLANG_USE_AITER=1 python3 -m sglang.launch_server \
    --model-path Qwen/Qwen3.5-397B-A17B \
    --tp 8 \
    --attention-backend triton \
    --trust-remote-code
```

```{tip}
Set `SGLANG_USE_AITER=1` to enable AMD's optimized aiter kernels for MoE and GEMM operations.
```

### Configuration Tips

- `--attention-backend`: Use `triton` on AMD GPUs for Qwen 3.5. The hybrid attention architecture (Gated Delta Networks + full attention) works best with the Triton backend on ROCm. The linear attention (GDN) layers always use Triton kernels internally via the `GDNAttnBackend`.
- `--watchdog-timeout`: Increase to `1200` or higher for this large model, as weight loading takes significant time.
- `--model-loader-extra-config '{"enable_multithread_load": true}'`: Enables parallel weight loading for faster startup.

### Reasoning and Tool Calling

Qwen 3.5 supports reasoning and tool calling via the Qwen3 parsers:

```bash
python3 -m sglang.launch_server \
    --model-path Qwen/Qwen3.5-397B-A17B \
    --tp 8 \
    --trust-remote-code \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder
```

## Accuracy Evaluation

You can evaluate the model accuracy using `lm-eval`:

```bash
pip install lm-eval[api]

lm_eval --model local-completions \
    --model_args '{"base_url": "http://localhost:8000/v1/completions", "model": "Qwen/Qwen3.5-397B-A17B", "num_concurrent": 256, "max_retries": 10, "max_gen_toks": 2048}' \
    --tasks gsm8k \
    --batch_size auto \
    --num_fewshot 5 \
    --trust_remote_code
```

## Additional Resources

- [AMD Day 0 Support for Qwen 3.5 on AMD Instinct GPUs](https://www.amd.com/en/developer/resources/technical-articles/2026/day-0-support-for-qwen-3-5-on-amd-instinct-gpus.html)
- [HuggingFace Model Card](https://huggingface.co/Qwen/Qwen3.5-397B-A17B)