Excellent, Fast Local Model (somewhat of a review)
This model is fast due to its 8B-A1B~ MoE+KDA architecture and very performant for its size. I've found that giving it web search and code execution (e.g. Python or JavaScript sandboxes) can elevate the model's abilities greatly and it feels like a bigger model in some areas while still being lightning fast. Pretty much frontier for its size.
I have been using it for basic research, general search queries, and generating simple snippets of code with great success. It has replaced my 7-9B dense on-device models and works well as a subagent to augment a larger model to speed things up.
Having Ling-3.0-tiny/flash be available under the MIT license makes it all the more usable as well. Though, I agree with #3 that it'd be really nice to have the base model for fine-tuning/plain text completion.
Thank you inclusionAI/Ant Group for this excellent release. I hope to see more models from you in the future!
thx, the pretrain base is uploading
Guys you dropped models very different in size (Ling-3.0-flash - 124B MoE, Ling-3.0-tiny - 8B MoE).
Thank you for them.
Can you please also drop something in between- like 30B Moe?
To pipe in, I think around a 30B~ MoE is good for more advanced local usage such as basic agentic coding or more advanced research since it balances intelligence with speed (and fits on local devices). Example models that work well on local devices in this range are gemma-4-26B-A4B-it (creative writing, general assistant) and Qwen3.6-35B-A3B (coding and agentic).
The main reason is that consumer devices (e.g. an "average" laptop or desktop upgraded with 16, 32, or 64 GB of RAM) can run such a MoE at speeds of 15-40 tokens/second on CPU/CPU+GPU/iGPU whereas they'd have a much harder time running a dense model - it'd work, yes, but would be very slow.
Medium MoE models like 100-300B are excellent for server deployments, but are tougher to run locally. Sure, you can get maybe a few tokens/second streaming from an NVMe SSD, but prefill will be slow and the whole thing will be too sluggish for serious long horizon work.
Bottom line is, a 30B MoE is just a nice size to have available in a model family. It'll run on a lot of modern home computers, be more powerful than < 20B options, and even though a few models of this size exist, it's good to have options. It doesn't need to be 30B, but around that ballpark would be nice (25-40B). In my opinion, I think Ling might be a good candidate for a 30B-ish model as it uses KDA, meaning context doesn't take up as much memory as other models while still being good, making it perfect for long horizon tasks.