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Kimi-K3 Technical Report [pdf]

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Re: Kimi-K3 Technical Report [pdf]

#71

What would it take to get an American open model to compete with this?

Latest "big" release from any of the bigger American lab must have been GPT-OSS-120b I think? Released ~summer 2025, so pretty much one years ago. Doesn't seem like it'll happen by itself, so something either forcing their hand figuratively, or something forcing their hand literally. Personally I was wishing/hoping for one of the recent Gemma releases to be in the ~100B class at least, but sadly Google is keeping tha…

NVIDIA-Nemotron-3-Ultra-550B-A55B was released in June 4th 2026 and I think it was the largest open US model until thinking machines' Inkling (975B) was released a couple of weeks ago.

Re: Kimi-K3 Technical Report [pdf]

#72

Back of the envelope calculation (could be off, correct me if I am) If you are a large enough company that spends million+ on inference a month, it makes sense to buy a GB300 rack ($6M on top range from what I could find) which has 20.7 TB. Since the model is mixed trained (MXFP4), you would need less than 10% of the rack's memory to serve the full model. Aggregate HBM bandwidth: 576 TB/s. You can run over 6000 paral…

And hire 2 or 3 dev ops to keep it running ? That another 400 to 700k. It becomes your problem and not someone else’s. However, I don’t trust hosted LLMs for anything that needs to be private.

Just let it manage itself, what could go wrong! :)

Re: Kimi-K3 Technical Report [pdf]

#73

Back of the envelope calculation (could be off, correct me if I am) If you are a large enough company that spends million+ on inference a month, it makes sense to buy a GB300 rack ($6M on top range from what I could find) which has 20.7 TB. Since the model is mixed trained (MXFP4), you would need less than 10% of the rack's memory to serve the full model. Aggregate HBM bandwidth: 576 TB/s. You can run over 6000 paral…

And hire 2 or 3 dev ops to keep it running ? That another 400 to 700k. It becomes your problem and not someone else’s. However, I don’t trust hosted LLMs for anything that needs to be private.

> However, I don’t trust hosted LLMs for anything that needs to be private.

Why not? Do you trust AWS with things that need to be private?

Re: Kimi-K3 Technical Report [pdf]

#74

This is amazing to witness. Moonshot open sourcing Kimi K3, a frontier AI and other components really means we are getting abundant AI for all of humanity. Kudos to Moonshot for truly being what OpenAI should have been. Fable-level and frontier AI should be open source and available to everyone for free.

> Kudos to Moonshot for truly being what OpenAI should have been. Kudos to Moonshot for making these weights available for download. Lets not fool ourselves and claim these are "open source" by any understanding of the concept though, there are usage restrictions (even if you download them) and also training data isn't clearly broken down either, nor it it actually using a FOSS license.

> Kudos to Moonshot for making these weights available for download. Lets not fool ourselves and claim these are "open source" by any understanding of the concept though, there are usage restrictions (even if you download them) and also training data isn't clearly broken down either, nor it it actually using a FOSS license.

They will probably never release the training data because that represents a large part of their competitive moat. The same is true of US companies (Google, OpenAI, Meta etc) none of which has released the full training data for its open models.

They use private datasets that cost a lot to acquire, synthetic datasets and a lot copyrighted material for which they don't have licensing.

What matters most is that, with the necessary hardware, I can download a near frontier model, run it and modify it however I want. The other concerns you mentioned are just noise. And if my company is generating $20 million in revenue or serving 100 million users, it can probably afford a relatively inexpensive commercial license.

Re: Kimi-K3 Technical Report [pdf]

#75
post #61
post #33

Earlier quoted context omitted.

Kimi 2.6 had the same janky license: https://huggingface.co/moonshotai/Kimi-K2.6/blob/main/LICENS... - looks like they've been doing that at least as far back as K2. The models they released in 2025 - https://huggingface.co/moonshotai/models - were clean MIT. They started doing the "modified MIT" thing in January 2026 with moonshotai/Kimi-K2-Thinking

In what way was the license "janky"? Moonshot isn't a trillion dollar US behemoth. If these terms allow it to release near frontier models with open weights, so startups and anyone with enough hardware can use them, while charging only companies with more than $20 million in revenue or 100 million users, that seems like a reasonable trade off.

Reasonable for people at the bottom of the tech pyramid. Certainly for startup guys this is heaven sent.

For people at the top of the tech pyramid, I could totally see why they'd want to put an end to all this "Open Source LLM" stuff.

Re: Kimi-K3 Technical Report [pdf]

#76

Back of the envelope calculation (could be off, correct me if I am) If you are a large enough company that spends million+ on inference a month, it makes sense to buy a GB300 rack ($6M on top range from what I could find) which has 20.7 TB. Since the model is mixed trained (MXFP4), you would need less than 10% of the rack's memory to serve the full model. Aggregate HBM bandwidth: 576 TB/s. You can run over 6000 paral…

And hire 2 or 3 dev ops to keep it running ? That another 400 to 700k. It becomes your problem and not someone else’s. However, I don’t trust hosted LLMs for anything that needs to be private.

Where do I sign up to get 200k/yr to keep one rack running? Sounds like an incredibly chill job

Re: Kimi-K3 Technical Report [pdf]

#77

What would it take to get an American open model to compete with this?

Latest "big" release from any of the bigger American lab must have been GPT-OSS-120b I think? Released ~summer 2025, so pretty much one years ago. Doesn't seem like it'll happen by itself, so something either forcing their hand figuratively, or something forcing their hand literally. Personally I was wishing/hoping for one of the recent Gemma releases to be in the ~100B class at least, but sadly Google is keeping tha…

the longer i read this comment the wronger it gets

Re: Kimi-K3 Technical Report [pdf]

#78
post #67
post #5

Also open sourced a bunch of infra to go with it. Anyone who claims open source and open weights models are "decel" needs to get their head checked https://github.com/MoonshotAI/MoonEP https://github.com/kvcache-ai/AgentEnv https://github.com/MoonshotAI/FlashKDA

To me its clear that it is decel the only reason other labs can catch up is because the frontier labs can be distilled, and they siphon a % of the labs' revenue to reinvest into the next iteration full accel would mean nationalizing the big 2 labs and locking in manhattan project style until RSI (Edit: some great counterpoints in the replies. my view has definitely been changed!)

only if you only get your news from main stream business press and Big Lab propaganda channels

There's no chance K3 is a distill of Fable, it came out way too soon after the limited fable release to be feasbile.

If you look at all of the top ML conferences, chinese labs contribute way more to advances in ML than "Open"AI and Anthropic: https://www.reddit.com/r/TheMachineGod/comments/1pi4q7f/pape...

This K3 release just helped every other lab on the planet stay in the race by making it possible for them to build on top of it, placing them at the frontier starting line instead of having to spend billions of their own dollars and risking it all to attempt to catch up.

The open source contributions I linked to above will move the whole field forward and reduce the costs of training and inference for everyone.

Open science compounds on it self, every new advancement pushes the field forwards and opens up new grounds for future improvements.

It is impossible for a single closed lab to consistently stay ahead of the rest of the field, especially in a huge growing research area like machine learning. The only only advantage the big labs have is money, but the naive scaling game is not sustainable long term when you have to pay 10-100x more then the fast followers and we start getting more and more open models or use case specific models that can handle 90% of high volume use cases.

Research is a high variance, low expected value activity, meaning that the few large concentrated labs have to be conservative with their bets and double down on proven things when scaling up. The rest of the field is like a diversified portfolio, with thousands of players making smaller riskier bets that only require a few of them to succeed (like K3 did here, and DeepSeek a year ago)

EDIT: also if you look at most of the work from OpenAI, it's mostly taking existing promising open research work and scaling it up. (except for things like CLIP and etc from Alec Radford)

Re: Kimi-K3 Technical Report [pdf]

#79

Back of the envelope calculation (could be off, correct me if I am) If you are a large enough company that spends million+ on inference a month, it makes sense to buy a GB300 rack ($6M on top range from what I could find) which has 20.7 TB. Since the model is mixed trained (MXFP4), you would need less than 10% of the rack's memory to serve the full model. Aggregate HBM bandwidth: 576 TB/s. You can run over 6000 paral…

And hire 2 or 3 dev ops to keep it running ? That another 400 to 700k. It becomes your problem and not someone else’s. However, I don’t trust hosted LLMs for anything that needs to be private.

> And hire 2 or 3 dev ops to keep it running

Not a devops but I'd say one full time is already too many.

Re: Kimi-K3 Technical Report [pdf]

#80

Back of the envelope calculation (could be off, correct me if I am) If you are a large enough company that spends million+ on inference a month, it makes sense to buy a GB300 rack ($6M on top range from what I could find) which has 20.7 TB. Since the model is mixed trained (MXFP4), you would need less than 10% of the rack's memory to serve the full model. Aggregate HBM bandwidth: 576 TB/s. You can run over 6000 paral…

I think the licensing that would likely apply to a company that's able to afford ~$6M rack and the associated infrastructure muddies this somewhat

As far as I can tell, license fees are only applicable if you have more than 20m USD/year revenue from services provided using the model, or serve more than 100m users.
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