Beats GPT-5.2 on IFBench (76.5 vs 75.4) and scores 72.2 on BFCL-V4 tool use. A massive MoE that activates only 17B parameters per token for high efficiency.
This model requires a specialized High-VRAM environment. Ensure you have the latest CUDA Drivers or Metal Framework installed.
Minimum VRAM: 130GB VRAM Recommended
The Qwen3.5-397B-A17B model by Alibaba is a 397B (MoE) parameter architecture optimized for chat tasks. It requires approximately 128GB of VRAM to comfortably run locally using a Q4_K_M quantization. Extending the context window up to 262,144 tokens will dynamically allocate further VRAM, meaning high-bandwidth memory hardware is strictly advised.
For running Qwen3.5-397B-A17B at maximum tokens-per-second, we recommend using LM Studio or Ollama with a GGUF quantization (Q4_K_M or Q6_K). If you are multi-GPU, use vLLM to distribute the layers across your VRAM pool for optimal throughput.
You will need a GPU with at least 130GB of VRAM to run the Q4_K_M quantized version smoothly with a moderate context window.
The simplest method is utilizing Ollama by executing 'ollama run qwen3.5:397b' directly in your command line. Alternatively, you can search for the model via LM Studio's interface.