Qwen
Qwen is a family of large language models (LLMs) developed by Alibaba Cloud, ranging from 0.5B to 110B parameters. Operators encounter Qwen models as open-weight releases on Hugging Face, supporting both dense and mixture-of-experts (MoE) architectures. The models are designed for multilingual tasks, with strong performance in Chinese and English. For local AI, Qwen models are available in quantized formats (GGUF, AWQ, GPTQ) that fit consumer GPUs; for example, Qwen2.5 7B at Q4_K_M uses ~5 GB VRAM, while Qwen2.5 72B at Q4 requires ~40 GB, often needing offloading or larger hardware.
Deeper dive
The Qwen family includes several series: Qwen1, Qwen2, Qwen2.5, and QwQ (reasoning-focused). Qwen2.5 introduced extended context lengths (up to 128K tokens) and improved multilingual capabilities. The MoE variants (e.g., Qwen2.5-32B-A14B) activate only a subset of parameters per token, reducing compute cost while maintaining quality. For local deployment, Qwen models are commonly run via llama.cpp or Ollama, with GGUF quantizations available from community providers like TheBloke. Operators should note that larger Qwen models (72B, 110B) require substantial VRAM or system-RAM offloading, which can reduce tokens/sec significantly. The QwQ series is optimized for chain-of-thought reasoning, similar to OpenAI's o1, but requires careful prompt formatting.
Practical example
An operator with an RTX 4090 (24 GB VRAM) can run Qwen2.5 7B at Q4_K_M (5 GB) with a 32K context, achieving ~40 tok/s. The same card cannot fit Qwen2.5 72B at Q4 (40 GB) without offloading to system RAM, dropping speed to ~5 tok/s. For the MoE Qwen2.5-32B-A14B, the 14B active parameters fit in 24 GB at Q4, offering a balance of quality and speed.
Workflow example
In Ollama, an operator runs ollama pull qwen2.5:7b to download the model. The runtime loads it into VRAM; if VRAM is insufficient, Ollama offloads layers to system RAM, visible in the logs as 'offloading 10/28 layers to CPU'. In llama.cpp, the command ./main -m qwen2.5-7b-q4_k_m.gguf -n 256 -p "Hello" runs inference. For Hugging Face Transformers, from transformers import AutoModelForCausalLM; model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B", device_map="auto") loads the model with automatic offloading.
Reviewed by Fredoline Eruo. See our editorial policy.