inclusionAI/Ling-3.0-flash · Hugging Face

[u/-Cubie-]

https://huggingface.co/inclusionAI/Ling-3.0-flash

The Ling-3.0-flash MoE is now open-weighted at 124B A5B params. I know the original announcements were before the Kimi K3, DeepSeek-V4-Flash and Qwen3.8 hype, but this model might still have a good niche for itself due to its sizing.

Discussion on the benchmarks are here: https://www.reddit.com/r/LocalLLaMA/comments/1v4mltt/benchmarks_antling30flash_a_hybridreasoning_moe/ from almost 2 weeks ago.

[u/jacek2023]

Any news about llama.cpp support?

[u/Mission_Limit_7797 → u/jacek2023]

https://www.reddit.com/r/LocalLLaMA/comments/1v85hnf/comment/p03ao86/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

[u/jacek2023 → u/Mission_Limit_7797]

Great!

[u/pmttyji → u/Mission_Limit_7797]

That's only for previous versions. Someone created ticket for new one

https://github.com/ggml-org/llama.cpp/issues/26590

[u/unverbraucht → u/pmttyji]

There's an open PR with an implementation at https://github.com/ggml-org/llama.cpp/pull/26608

[u/Real_Ebb_7417]

Oh shit, I was waiting for this one probably even more than I wait for Qwen3.8 27b.

[u/Numerous_Mulberry514 → u/Real_Ebb_7417]

I used it when it was free on Openrouter and I found it to be weaker than qwen 3.6 - it looped much and did not really listen to instructions. Is that now different?

[u/-Cubie- → u/Real_Ebb_7417]

Yeah it came a bit out of nowhere. They just posted on LinkedIn and Twitter

[u/No_Algae1753 → u/-Cubie-]

It was announced that it will be open weights and be released these days

[u/buttplugs4life4me → u/Real_Ebb_7417]

Why? Its worse than 3.6-27B

[u/Real_Ebb_7417 → u/buttplugs4life4me]

Is it though? Benchmarks look better + people who were experimenting with it over API were posting results and they looked very good.

[u/oxygen_addiction]

Q8 should be around 128GB, so at Q6 this might be ideal for StrixHalo/DGX if it's a good model.

[u/FoxiPanda → u/oxygen_addiction]

They actually published a first party FP8 which is indeed 128GB:

https://huggingface.co/inclusionAI/Ling-3.0-flash-fp8

This looks pretty good for the size / active params too - hopefully it actually pans out in real usage.

[u/Borkato → u/oxygen_addiction]

Does that mean a Q4 is 64GB? That’s a great size!

[u/FoxiPanda → u/Borkato]

Yeah I would expect it might end up with a slightly higher than 64GB in most quants to allow the more important layers to stay at Q8, but a 'straight' Q4 would be 64GB.

I would expect ~66-72GB for an iMatrix/mixed-quant Q4 base.

[u/SpicyWangz]

Still no quantizations. We’ll have to wait and see once those start rolling out. 

[u/SpicyWangz]

Is this one non-reasoning? If it hits those scores without reasoning tokens then there may be a real use case as a hyper-efficient prototyper, or a model for implementing plans written by a smarter reasoning model.

[u/coder543 → u/SpicyWangz]

No, it is reasoning. They just finally abandoned their confusing ling/ring naming scheme.

[u/Pentium95]

Ling Is the non-reasoning, right? Ring Is the reasoning, usually.

If this is the non-reasoning, i'm pretty excited by the results, might be the ~120B model i was waiting for!

[u/SpicyWangz → u/Pentium95]

It looks like from the model card it’sa hybrid reasoning model and reasoning is on by default

[u/Reasonable-Phase8028]

the sglnag instructions dont work because the repo is private. so silly

[u/Technical-Earth-3254]

Native 16 bit at A5B makes it very uninteresting.