How to run Qwen 3.5 locally (unsloth.ai)

Article summary

The article provides a guide on how to run Qwen 3.5 locally, including the required hardware and software setup. Qwen 3.5 is a multimodal hybrid reasoning language model that can be run on various devices, including Macs and PCs with sufficient RAM and VRAM. The model comes in different sizes, including 0.8B, 2B, 4B, 9B, 27B, 35B-A3B, 122B-A10B, and 397B-A17B, each with its own hardware requirements. The article also provides tutorials and recommendations for running the model using llama.cpp and Unsloth Studio.

Main themes

• Qwen 3.5 local setup
• Model size and quantization
• Hardware requirements
• Coding tasks and performance
• Comparison to other models
• GPU offloading and optimization
• Model quality and capabilities
• Troubleshooting and limitations

What commenters say

• Running Qwen 3.5 locally can be practical and effective for coding tasks, even on relatively low-end hardware.
• The 35B-A3B model is considered faster and more capable than the 27B model, but the 27B model may be more accurate in certain tasks.
• The quality of Qwen 3.5 models is comparable to other state-of-the-art models, such as Sonnet 4.0, despite having fewer parameters.
• Some users have reported issues with the model getting stuck in loops or ignoring instructions, while others have found it to be highly capable and useful.
• The choice of model size and quantization depends on the specific use case and available hardware, with some users preferring the 9B model for its balance of speed and quality.
• The use of GPU offloading can significantly improve performance, but may require additional setup and troubleshooting.
• The article's claims about the model's quality and capabilities are not universally accepted, with some users finding it to be overhyped or disappointing in certain tasks.