I brought hot news today.
It's so exciting and a little scary to see how much progress is being made every day^^
They say a model that compresses Qwen 3.8 - 27B to 1.76 bits has been released. The capacity is a whopping 5.9 gigabytes ㅋㅋㅋ
Below is the detailed information written by Gemini.
Hello. There's breaking news this morning that will completely shake up the open-source AI world, so I'll quickly summarize it.
This is great news not only for Mac users but also for those who use low-spec laptops or general PCs.
To get to the point, a medium-sized model with 27 billion parametersQwen 3.8-27B has been released, which has been drastically compressed to just '1.76 bits' without any loss of brain function.
Here are the key takeaways:
1. Amazing capacity diet (5.9GB)
The original model's size has been drastically compressed, resulting in a total file size of just 5.9GB.
Now, even low-spec hardware environments that previously could only handle lightweight 8B (8 billion) models due to insufficient VRAM or integrated memory can now easily load a 27B class giant model into memory.
2. Intelligence preservation rate 98.2% (overcoming the inference cliff)
Normally, if compressed to less than 4 bits, the 'inference cliff' phenomenon becomes severe, where AI becomes foolish. However, thanks to the Ternary compression technology applied this time, the original intelligence was maintained at 98.2% in precision benchmark tests.
This means that we have both the speed of a lightweight small model and the deep logical reasoning ability of a large model.
3. Recommended for these people:
Those who use Macs with limited integrated memory (8GB, 16GB, etc.) or older graphics cards and couldn't even consider large local models.
Those who want to run a smart AI comparable to commercial APIs perfectly offline on smartphones, tablets, and other on-device environments.
The speed of technological development in the local LLM field is truly frightening. Since the capacity burden has been completely eliminated, I strongly recommend that you test it right away in Hugging Face or Ollama environments!
