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:

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:

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

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:

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:

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
• HuggingFace Model Card