We noticed that Ornith Lab released the Ornith 1.5 series of open-source models this week, including 9B (with vision) and 35B-A3B variants, all under the MIT license (free for commercial use). A community team led by the co-founder of Atomic Chat ran a quantization comparison (compressing the model to run on lower-spec hardware): the 9B model shrunk to 5.6 GB still achieves a 91.93% answer accuracy rate.
What this is
Ornith 1.5 is Ornith Lab's new-generation open-source model series. The 9B version (about 9 billion parameters, roughly the model's "brain cell" count) comes with image recognition capability and is suited for consumer GPUs or high-performance laptops; the 35B-A3B uses a Mixture-of-Experts architecture (the model internally contains multiple "small experts," only activating a subset per inference to balance performance and cost), and quantizes to roughly 20 GB.
Both versions use the MIT license — free to download, modify, and use commercially — with weights and configuration files hosted on Hugging Face. The official notes mention the model employs a "self-generated task" training loop — letting the model create and solve its own problems to reduce human annotation costs. But specific effectiveness awaits independent third-party verification.
Industry view
The local AI community has responded broadly positive. Atomic Chat's comparison tests show their in-house "Atomic Dynamic" quantization scheme retains higher answer accuracy than llama.cpp's (currently the most mainstream open-source model runtime framework) default at every file-size tier — for instance, when the 9B model is compressed to a tiny 4.29 GB, it still hits an 83.44% top-1 hit rate (the rate at which the model's top answer matches the original model's). This means users can get near-original-model experience on cheaper hardware.
But cautious voices exist. Compared with Meta's Llama and Alibaba's Tongyi Qianwen, Ornith Lab's community ecosystem and long-term maintenance capability have not yet been validated. The official mentions of a 397B large-model version exist, but documentation is scarce, and whether the "self-generated task" training loop performs stably at large-model scale remains unknown. One user on the r/LocalLLaMA subreddit noted: "Pretty numbers are one thing — whether it can be reliably reproduced, and whether anyone will still be maintaining it six months from now, is another."
Impact on regular people
For enterprise IT: The MIT license means free commercial use; compliance and licensing costs for SMEs deploying private AI assistants drop further.
For individual professionals: The 9B model already runs on laptops with 16 GB of memory; features in tools like Notion AI or Feishu's Smart Companion may increasingly run locally in the future, without sending data to the cloud.
For consumer market: As small models approach large-model performance, subscription pricing for AI assistant apps will likely stay under pressure, and user preference for "local, privacy-friendly" options will get more product responses.