Qwen3-Next 80B-A3B Thinking (AWQ 4-bit)
This is an AWQ 4-bit quantized release of Qwen/Qwen3-Next-80B-A3B-Thinking, aimed at making a very large, long-context reasoning model practical to run on a smaller number of GPUs. The model card reports a drop in weight memory from ~151.5 GB down to ~45.9 GB, which can be the difference between “needs a big multi-GPU box” and “fits on a few consumer/workstation cards.”
The underlying Qwen3-Next line is interesting for its scaling-efficiency story: an MoE setup listed as 80B parameters total with ~3B active, plus hybrid attention designed to keep ultra-long context usable (262k native context is called out prominently). This “Thinking” variant is explicitly positioned for more complex reasoning, and the card includes vLLM-based serving examples.
If you try it, start by validating basic throughput and output quality at a normal context length first, then scale up context and max output gradually. Even with quantized weights, long-context inference can get expensive quickly due to KV cache growth and “thinking mode” verbosity.
Quick stats from the listing feed: pipeline: text-generation · 20 likes · 197157 downloads.
Source listing: https://huggingface.co/models?sort=modified