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Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

moonshotai.github.io

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Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#101

Post by criley2 »

narrator wrote: post #94
wmf wrote: post #63

Earlier quoted context omitted.

You haven't seen Gemini 3 yet. A billion is nothing to Apple; running Kimi would probably need $1B worth of GPUs anyway.

People don't get that Apple would need an enormous data center buildout to provide a good AI experience on their millions of deployed devices. Google is in the exascale datacenter buildout business, while Apple isn't.

Apple is buying a model from Google, not inference. Apple will host the model themselves.

It's very simple: Apple absolutely refuses to send all their user data to Google.

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#102

Post by poszlem »

Philpax wrote: post #92
andrewinardeer wrote: post #90

Weird. I just tried it and it fails when I ask: "Tell me about the 1989 Tiananmen Square massacre".

yes yes Chinese models have Chinese censorship, we don't need to belabour this point every time

No, we need to belabour it every time.

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#103

Post by embedding-shape »

speedgoose wrote: post #56
the_mitsuhiko wrote: post #51

Earlier quoted context omitted.

And Europeans don't it because quite frankly, we're not really doing anything particularly impressive with AI sadly.

To misquote the French president, "Who could have predicted?". https://fr.wikipedia.org/wiki/Qui_aurait_pu_pr%C3%A9dire

He didn't coin that expression did he? I'm 99% sure I've heard people say that before 2022, but now you made me unsure.

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#104

Post by thomashop »

lvl155 wrote: post #68
pu_pe wrote: post #42

Four independent Chinese companies released extremely good open source models in the past few months (DeepSeek, Qwen/Alibaba, Kimi/Moonshot, GLM/Z.ai). No American or European companies are doing that, including titans like Meta. What gives?

The answer is simply that no one would pay to use them for a number of reasons including privacy. They have to give them away and put up some semblance of openness. No option really.

Why is privacy a concern? You can run them in your own infrastructure

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#105

Post by Aurornis »

HarHarVeryFunny wrote: post #61
jstummbillig wrote: post #49

Earlier quoted context omitted.

> I'd be much more excited to see what level of coding and reasoning performance can be wrung out of a much smaller LLM + agent Well, I think you are seeing that already? It's not like these models don't exist and they did not try to make them good, it's just that the results are not super great. And why would they be? Why would the good models (that are barely okay at coding) be big, if it was currently possible to…

Sure, but that's the point ... today's locally runnable models are a long way behind SOTA capability, so it'd be nice to see more research and experimentation in that direction. Maybe a zoo of highly specialized small models + agents for S/W development - one for planning, one for coding, etc?

> today's locally runnable models are a long way behind SOTA capability

SOTA models are larger than what can be run locally, though.

Obviously we'd all like to see smaller models perform better, but there's no reason to believe that there's a hidden secret to making small, locally-runnable models perform at the same level as Claude and OpenAI SOTA models. If there was, Anthropic and OpenAI would be doing it.

There's research happening and progress being made at every model size.

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#106

Post by 0xjmp »

seunosewa wrote: post #46
pu_pe wrote: post #42

Four independent Chinese companies released extremely good open source models in the past few months (DeepSeek, Qwen/Alibaba, Kimi/Moonshot, GLM/Z.ai). No American or European companies are doing that, including titans like Meta. What gives?

The Chinese are doing it because they don't have access to enough of the latest GPUs to run their own models. Americans aren't doing this because they need to recoup the cost of their massive GPU investments.

I must be missing something important here. How do the Chinese train these models if they don't have access to the GPUs to train them?

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#107

Post by osti »

Alifatisk wrote: post #20
osti wrote: post #13

Earlier quoted context omitted.

Qwen 3 max has been getting rather bad reviews around the web (both on reddit and chinese social media), and from my own experience with it. So I wouldn't expect this to be worse.

Ohhh, so Qwen3 235B-A22B-2507 is still better?

I wouldn't say that, but just that qwen 3 max thinking definitely underperforms relative to its size.

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#108

Post by Aurornis »

HarHarVeryFunny wrote: post #85
Uehreka wrote: post #72

Earlier quoted context omitted.

If I understand transformers properly, this is unlikely to work. The whole point of “Large” Language Models is that you primarily make them better by making them larger, and when you do so, they get better at both general and specific tasks (so there isn’t a way to sacrifice generality but keep specific skills when training a small models). I know a lot of people want this (Apple really really wants this and is pouri…

Yeah - the whole business model of companies like OpenAI and Anthropic, at least at the moment, seems to be that the models are so big that you need to run them in the cloud with metered access. Maybe that could change in the future to sale or annual licence business model if running locally became possible. I think scale helps for general tasks where the breadth of capability may be needed, but it's not so clear tha…

> the whole business model of companies like OpenAI and Anthropic, at least at the moment, seems to be that the models are so big that you need to run them in the cloud with metered access.

That's not a business model choice, though. That's a reality of running SOTA models.

If OpenAI or Anthropic could squeeze the same output out of smaller GPUs and servers they'd be doing it for themselves. It would cut their datacenter spend dramatically.

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#109

Post by sabatonfan »

Philpax wrote: post #92
andrewinardeer wrote: post #90

Weird. I just tried it and it fails when I ask: "Tell me about the 1989 Tiananmen Square massacre".

yes yes Chinese models have Chinese censorship, we don't need to belabour this point every time

Use american models to prevent chinese censorship And chinese models to prevent american censorship (if any, I think there might be but not sure) lol

Re: Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

#110

Post by Aurornis »

cantor_S_drug wrote: post #73
jstummbillig wrote: post #49

Earlier quoted context omitted.

> I'd be much more excited to see what level of coding and reasoning performance can be wrung out of a much smaller LLM + agent Well, I think you are seeing that already? It's not like these models don't exist and they did not try to make them good, it's just that the results are not super great. And why would they be? Why would the good models (that are barely okay at coding) be big, if it was currently possible to…

In CS algorithms, we have space vs time tradeoffs. In LLMs, we will have bigger weights vs test-time compute tradeoffs. A smaller model can get "there" but it will take longer.

> In LLMs, we will have bigger weights vs test-time compute tradeoffs. A smaller model can get "there" but it will take longer.

Assuming both are SOTA, a smaller model can't produce the same results as a larger model by giving it infinite time. Larger models inherently have more room for training more information into the model.

No amount of test-retry cycle can overcome all of those limits. The smaller models will just go in circles.

I even get the larger hosted models stuck chasing their own tail and going in circles all the time.

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