A high-sparsity Mixture-of-Experts 80B model utilizing a hybrid attention architecture.
This model requires a specialized High-VRAM environment. Ensure you have the latest CUDA Drivers or Metal Framework installed.
Minimum VRAM: 26GB VRAM Recommended
The Qwen3 Next model by Alibaba is a 80B (MoE) parameter architecture optimized for chat tasks. It requires approximately 24GB of VRAM to comfortably run locally using a Q4_K_M quantization. Extending the context window up to 131,072 tokens will dynamically allocate further VRAM, meaning high-bandwidth memory hardware is strictly advised.
For running Qwen3 Next 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 26GB 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:next' directly in your command line. Alternatively, you can search for the model via LM Studio's interface.