Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model
(twitter.com)285 points by c4pt0r 2 days ago
285 points by c4pt0r 2 days ago
Reasonable speeds are possible with 4bit quants on 2 512GB Mac Studios (MLX TB4 Ring - see https://x.com/awnihannun/status/1943723599971443134) or even a single socket Epyc system with >1TB of RAM (about the same real world memory throughput as the M Ultra). So $20k-ish to play with it.
For real-world speeds though yeah, you'd need serious hardware. This is more of a "deploy your own stamp" model, less a "local" model.
Reasonable speeds are possible if you pay someone else to run it. Right now both NovitaAI and Parasail are running it, both available through Openrouter and both promising not to store any data. I'm sure the other big model hosters will follow if there's demand.
I may not be able to reasonably run it myself, but at least I can choose who I trust to run it and can have inference pricing determined by a competitive market. According to their benchmarks the model is about in a class with Claude 4 Sonet, yet already costs less than one third of Sonet's inference pricing
I’m actually finding Claude 4 Sonnet’s thinking model to be too slow to meet my needs. It literally takes several minutes per query on Cursor.
So running it locally is the exact opposite of what I’m looking for.
Rather, I’m willing to pay more, to have it be run on a faster than normal cloud inference machine.
Anthropic is already too slow.
Since this model is open source, maybe someone could offer it at a “premium” pay per use price, where the response rate / inference is done a lot faster, with more resources thrown at it.
I write a local LLM client, but sometimes, I hate that local models have enough knobs to turn that people can advocate they're reasonable in any scenario - in yesterday's post re: Kimi k2, multiple people spoke up that you can "just" stream the active expert weights out of 64 GB of RAM, and use the lowest GGUF quant, and then you get something that rounds to 1 token/s, and that is reasonable for use.
Good on you for not exaggerating.
I am very curious what exactly they see in that, 2-3 people hopped in to handwave that you just have it do agent stuff overnight and it's well worth it. I can't even begin to imagine unless you have a metric **-ton of easily solved problems that aren't coding. Even a 90% success rate gets you into "useless" territory quick when one step depends on the other, and you're running it autonomoously for hours
I do deepseek at 5tk/sec at home and I'm happy with it. I don't need to do agent stuff to gain from it, I was saving to eventually build out enough to run it at 10tk/sec, but with kimi k2, plan has changed and the savings continue with a goal to run it at 5 tk/sec at home.
This is fairly affordable if you’re a business honestly
The first question I gave it (a sort of pretty simple recreational math question I asked it to code up for me) and it was outrageously wrong. In fairness, and to my surprise, OpenAI's model also failed with this task, although with some prompting, sort of got it.
Still pretty good, someone with enough resources could distil it down to a more manageable size for the rest of us.
I asked it to give me its opinion on a mail I'm writing. 95% of its content is quotes from famous authors, and the 5% I wrote is actually minimal glue in-between.
All the models I tested, which includes Sonnet 4, DeepSeekR1, 4o and Gemini 2.5 understand this isn't your normal email and what I ask is literary/philosophical criticism, not remarks about conventions, formatting or how to convey my message in a more impactful way.
Quick fix:
- Replace half the commas with periods.
- Let one metaphor bloom and then stop.
- Read it aloud; wherever you inhale, insert a line break.
Before:
>And if the collective imagination is so seized by it, it's because
we are already—not in some other world—but in this very life, in a
state of socio-, photo-, and videosynthesis.
After:
>The collective imagination is seized.
>We are not in another world.
>We are in this life, in a state of videosynthesis.
Gemini, second sentence: It is less a simple email than an intellectual
and personal exploration still in the making.
**Gemini is polite and pedagogical; I’m going to be blunt.**
1. **This isn’t an email, it’s a trip.**
...
2. **The content is good, the format is not.**
...
3. **Gemini is right about one thing:**
...
* cut 80% of the material,
* ask **one clear question**
* put the rest in a separate Google Doc or PDF for posterity.
4. **Brutal summary:**
Keep your draft for yourself — it’s valuable. But for the email, write a new one in **three sentences**:
* thank them,
* recall the context in one line,
* ask the question.
You’ve already got Gemini’s answer to stroke your ego — now give me the short version you’re actually going to send.
This is a very impressive general purpose LLM (GPT 4o, DeepSeek-V3 family). It’s also open source.
I think it hasn’t received much attention because the frontier shifted to reasoning and multi-modal AI models. In accuracy benchmarks, all the top models are reasoning ones:
https://artificialanalysis.ai/
If someone took Kimi k2 and trained a reasoning model with it, I’d be curious how that model performs.
Pelican on a bicycle result: https://simonwillison.net/2025/Jul/11/kimi-k2/
"I'm glad we are looking to build nuclear reactors so we can do more of this..."
Does this actually mean "they" not "we"
me too - we must energymaxx. i want a nuclear reactor in my backyard powering everything. I want ac units in every room and my open door garage while i workout.
I have a different experience in chatting/creative writing. It tends to overuse certain speech patterns without repeating them verbatim, and is strikingly close to the original R1 writing, without being "chaotic" like R1 - unexpected and overly dramatic sci-fi and horror story turns, "somewhere, X happens" at the end etc.
Interestingly enough, EQ-Bench/Creative Writing Bench doesn't spot this despite clearly having it in their samples. This makes me trust it even less.
Kimi K2 is the large language model series developed by Moonshot AI team.
Moonshot AI [1] (Moonshot; Chinese: 月之暗面; pinyin: Yuè Zhī Ànmiàn) is an artificial intelligence (AI) company based in Beijing, China. As of 2024, it has been dubbed one of China's "AI Tiger" companies by investors with its focus on developing large language models.
I guess everyone is up to date with AI stuff but this is the first time I heard of Kimi and Moonshot and was wondering where it is from. And it wasn't obvious from a quick glance of comments.
If I had to guess, the OpenAI open-source model got delayed because Kimi K2 stole their thunder and beat their numbers.
Someone at openai did say it was too big to host at home, so you could be right. They will probably be benchmaxxing, right now, searching for a few evals they can beat.
These are all "too big to host at home". I don't think that is the issue here.
https://github.com/MoonshotAI/Kimi-K2/blob/main/docs/deploy_...
"The smallest deployment unit for Kimi-K2 FP8 weights with 128k seqlen on mainstream H200 or H20 platform is a cluster with 16 GPUs with either Tensor Parallel (TP) or "data parallel + expert parallel" (DP+EP)."
16 GPUs costing ~$30k each. No one is running a ~$500k server at home.
For most people, before it makes sense to just buy all the hardware yourself, you probably should be renting GPUs by the hour from the various providers serving that need. On Modal, I think should cost about $72/hr to serve Kimi K2 https://modal.com/pricing
Once that's running it can serve the needs of many users/clients simultaneously. It'd be too expensive and underutilized for almost any individual to use regularly, but it's not unreasonable for them to do it in short intervals just to play around with it. And it might actually be reasonable for a small number of students or coworkers to share a $70/hr deployment for ~40hr/week in a lot of cases; in other cases, that $70/hr expense could be shared across a large number of coworkers or product users if they use it somewhat infrequently.
So maybe you won't host it at home, but it's actually quite feasible to self-host, and is it ever really worth physically hosting anything at home except as a hobby?
I think what GP means is that because the (hopefully) pending OpenAI release is also "too big to run at home", these two models may be close enough in size that they seem more directly comparable, meaning that it's even more important for OpenAI to outperform Kimi K2 on some key benchmarks.
The real users for these open source models are businesses that want something on premises for data privacy reasons
Not sure if they’ll trust a Chinese model but dropping $50-100k for a quantized model that replaces, say, 10 paralegals is good enough for a law firm
Big release - https://huggingface.co/moonshotai/Kimi-K2-Instruct model weights are 958.52 GB
Paired with programming tools like Claude Code, it could be a low-cost/open-source replacement for Sonnet
To me K2 is the Kotlin 2.0 compiler. https://blog.jetbrains.com/kotlin/2023/02/k2-kotlin-2-0/
This is not open source, they have a "modified MIT license" where they have other restrictions on users over a certain threshold.
Our only modification part is that, if the Software (or any derivative works
thereof) is used for any of your commercial products or services that have
more than 100 million monthly active users, or more than 20 million US dollars
(or equivalent in other currencies) in monthly revenue, you shall prominently
display "Kimi K2" on the user interface of such product or service.I feel like those restrictions don't violate the OSD (or the FSF's Free Software Definition, or Debian's); there are similar restrictions in the GPLv2, the GPLv3, the 4-clause BSD license, and so on. They just don't have user or revenue thresholds. The GPLv2, for example, says:
> c) If the modified program normally reads commands interactively when run, you must cause it, when started running for such interactive use in the most ordinary way, to print or display an announcement including an appropriate copyright notice and a notice that there is no warranty (or else, saying that you provide a warranty) and that users may redistribute the program under these conditions, and telling the user how to view a copy of this License. (Exception: if the Program itself is interactive but does not normally print such an announcement, your work based on the Program is not required to print an announcement.)
And the 4-clause BSD license says:
> 3. All advertising materials mentioning features or use of this software must display the following acknowledgement: This product includes software developed by the organization.
Both of these licenses are not just non-controversially open-source licenses; they're such central open-source licenses that IIRC much of the debate on the adoption of the OSD was centered on ensuring that they, or the more difficult Artistic license, were not excluded.
It's sort of nonsense to talk about neural networks being "open source" or "not open source", because there isn't source code that they could be built from. The nearest equivalent would be the training materials and training procedure, which isn't provided, but running that is not very similar to recompilation: it costs millions of dollars and doesn't produce the same results every time.
But that's not a question about the license.
It may not violate the OSD, but I would still argue that this license is a Bad Idea. Not because what they're trying to do is inherently bad in any way, but simply because it's yet another new, unknown, not-fully-understood license to deal with. The fact that we're having this conversation illustrating that very fact.
My personal feeling is that almost every project (I'll hedge a little because life is complicated) should prefer an OSI certified license and NOT make up their own license (even if that new license is "just" a modification of an existing license). License proliferation[1] is generally considered a Bad Thing for good reason.
Aren't most licenses "not fully understood" in any reasonable legal sense? To my knowledge only the Artistic License and the GPL have seen the inside of a court room. And yet to this day nobody really knows how the GPL works with languages that don't follow C's model of a compile and a link step. And the boundaries of what's a derivative work in the GPL are still mostly set by convention, not a legal framework.
What makes us comfortable with the "traditional open source licenses" is that people have been using them for decades and nothing bad has happened. But that's mostly because breaking an open source license is rarely litigated against, not because we have some special knowledge of what those licenses mean and how to abide by that
Aren't most licenses "not fully understood" in any reasonable legal sense?
OK, fair enough. Pretend I said "not well understood" instead. The point is, the long-standing, well known licenses that have been around for decades are better understood that some random "I made up my own thing" license. And yes, some of that may be down to just norms and conventions, and yes, not all of these licenses have been tested in court. But I think most people would feel more comfortable using an OSI approved license, and are hesitant to foster the creation of even more licenses.
If nothing else, license proliferation is bad because of the combinatorics of understanding license compatibility issues. Every new license makes the number of permutations that much bigger, and creates more unknown situations.
I'm of the personal opinion that it's quite reasonable for the creators to want attribution in case you manage to build a "successful product" off their work. The fact that it's a new or different license is a much smaller thing.
A lot of open source, copyleft things already have attribution clauses. You're allowed commerical use of someone else's work already, regardless of scale. Attribution is a very benign ask.
The OSD does not allow for discrimination:
"The license must not discriminate against any person or group of persons."
"The license must not restrict anyone from making use of the program in a specific field of endeavor. For example, it may not restrict the program from being used in a business, or from being used for genetic research."
By having a clause that discriminates based on revenue, it cannot be Open Source.
If they had required everyone to provide attribution in the same manner, then we would have to examine the specifics of the attribution requirement to determine if it is compatible... but since they discriminate, it violates the open source definition, and no further analysis is necessary.
This license with the custom clause seems equivalent to dual-licensing the product under the following licenses combined:
* Small companies may use it without attribution
* Anyone may use it with attribution
The first may not be OSI compatible, but if the second license is then it’s fair to call the offering open weights, in the same way that dual-licensing software under GPL and a commercial license is a type of open source.
Presumably the restriction on discrimination relates to license terms which grant _no_ valid open source license to some group of people.
You are still free to run the program as you wish, you just have to provide attribution to the end user. It's essentially CC BY but even more permissive, because the attribution only kicks in once when specific, relatively uncommon conditions are met.
I think basically everybody considers CC BY to be open source, so a strictly more permissive license should be too, I think.
This freedom might be against the freedom of others to get your modifications.
> This is not open source
OSI purism is deleterious and has led to industry capture.
Non-viral open source is simply a license for hyperscalers to take advantage. To co-opt offerings and make hundreds of millions without giving anything back.
We need more "fair source" licensing to support sustainable engineering that rewards the small ICs rather than mega conglomerate corporations with multi-trillion dollar market caps. The same companies that are destroying the open web.
This license isn't even that protective of the authors. It just asks for credit if you pass a MAU/ARR threshold. They should honestly ask for money if you hit those thresholds and should blacklist the Mag7 from usage altogether.
The resources put into building this are significant and they're giving it to you for free. We should applaud it.
> small ICs
The majority of open source code is contributed by companies, typically very large corporations. The thought of the open source ecosystem being largely carried by lone hobbyist contributors in their spare time after work is a myth. There are such folks (heck I'm one of them) and they are appreciated and important, but their perception far exceeds their real role in the open source ecosystem.
Yep, awesome stuff. Call it "fair source" if you want to. Don't call it open source. I'm an absolutist about very few things, but the definition of open source is one of them. Every bit of variation given in the definition is a win for those who have ulterior motives for polluting the definition. Open source isn't a vague concept, it's a defined term with a legally accepted meaning. Very much like "fair use". It's dangerous to allow this definition to be altered. OpenAI (A deliberate misnomer if ever there was one) and friends would really love to co-opt the term.
That's great, nothing wrong with giving away something for free, just don't call it open source.
This is just so Google doesn't build a woke version of it and calls it gemini-3.0-pro
I've only started using Claude, Gemini, etc in the last few months (I guess it comes with age, I'm no longer interested in trying the latest "tech"). I assume those are "non-agentic" models.
From reading articles online, "agentic" means like you have a "virtual" Virtual Assistant with "hands" that can google, open apps, etc, on their own.
Why not use existing "non-agentic" model and "orchestrate" them using LangChain, MCP etc? Why create a new breed of model?
I'm sorry if my questions sound silly. Following AI world is like following JavaScript world.
Reasonable question, simple answer: "New breed of model" is overstating it — all these models for years have been fine-tuned using reinforcement learning on a variety of tasks, it's just that the set of tasks (and maybe the amount of RL) has changed over time to include more tool use tasks, and this has made them much, much better at the latter. The explosion of tools like Claude Code this year is driven by the models just being more effective at it. The orchestration external to the model you mention is what people did before this year and it did not work as well.
"Agentic" and "agent" can mean pretty much anything, there are a ton of different definitions out there.
When an LLM says it's "agentic" it usually means that it's been optimized for tool use. Pretty much all the big models (and most of the small ones) are designed for tool use these days, it's an incredibly valuable feature for a model to offer.
I don't think this new model is any more "agentic" than o3, o4-mini, Gemini 2.5 or Claude 4. All of those models are trained for tools, all of them are very competent at running tool calls in a loop to try to achieve a goal they have been given.
It is not a silly question. The various flavors of LLM have issues with reliability. In software we expect five 9s, LLMs aren't even a one 9. Early on it was reliability of them writing JSON output. Then instruction following. Then tool use. Now it's "computer use" and orchestration.
Creating models for this specific problem domain will have a better chance at reliability, which is not a solved problem.
Jules is the gemini coder that links to github. Half the time it doesn't create a pull request and forgets and assumes I'll do some testing or something. It's wild.
> I'm sorry if my questions sound silly. Following AI world is like following JavaScript world.
You are more right than you could possibly imagine.
TL;DR: "agentic" just means "can call tools it's been given access to, autonomously, and then access the output" combined with an infinite loop in which the model runs over and over (compared to a one-off interaction like you'd see in ChatGPT). MCP is essentially one of the methods to expose the tools to the model.
Is this something the models could do for a long while with a wrapper? Yup. "Agentic" is the current term for it, that's all. There's some hype around "agentic AI" that's unwarranted, but part of the reason for the hype is that models have become better at tool calling and using data in their context since the early days.
It's not even open-weight. It's weight-available. It uses a "modified MIT license":
Modified MIT License
Copyright (c) 2025 Moonshot AI
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Our only modification part is that, if the Software (or any derivative works
thereof) is used for any of your commercial products or services that have
more than 100 million monthly active users, or more than 20 million US dollars
(or equivalent in other currencies) in monthly revenue, you shall prominently
display "Kimi K2" on the user interface of such product or service.This seems significantly more permissive than GPL. I think it's reasonable to consider it open-weight.
So "MIT with attribution" (but only for huge commercial use cases making tons of money off the product) is not open-weight? Do you consider CC BY photos on Wikipedia to be Image Available or GPL licensed software to be code-available too?
Tangent: I don't understand the contingent that gets upset about open LLMs not shipping with their full training regimes or source data. The software a company spent hundreds of millions of dollars creating, which you are now free to use and distribute with essentially no restrictions, is open source. It has weights in it, and a bunch of related software for actually running a model with those weights. How dare they!
4-clause BSD is considered open source by Debian and the FSF and has a similar requirement.
Wont happen under the current copyright regime, it is impossible to train SOTA without copyrighted text, how do you propose distributing that?
It's challenging, but not impossible. With 2-bit quantisation, only about 250-ish gigabytes of RAM is required. It doesn't have to be VRAM either, and you can mix and match GPU+CPU inference.
In addition, some people on /r/localLlama are having success with streaming the weights off SSD storage at 1 token/second, which is about the rate I get for DeepSeek R1.
This is an open weight model, which is in contrast with closed-source models.
However, 1t parameters makes it nearly impossible for local inference, let alone fine-tuning.
So far, I like the answer quality and its voice (a bit less obsequious than either ChatGPT or DeepSeek, more direct), but it seems to badly mangle the format of its answers more often than I've seen with SOTA models (I'd include DeepSeek in that category, or close enough).
If I had to guess, the OpenAI open-source model got delayed because Kimi K2 stole their thunder and beat their numbers.
Time to RL the hell out of it so it looks better on benchmarks... It's going to be fried.
All the AI models are no using em-dashes. ChatGPT keeps using them even after explicitly told not to. Anybody know what’s up with these models?
That reminds me of a thought I had about the poachings.
The poaching was probably more aimed at hamstringing Meta's competition.
Because the disruption caused by them leaving in droves is probably more severe than the benefits of having them on board. Unless they are gods, of course.
How well separated are experts per domain in a model like that? Specifically, if I'm interested in a programming use only, could we possibly strip it to one or two of them? Or should I assume a much wider spread? (And there would be some overlap anyway from the original root model)
My experience is that experts are not separated in any intuitive way. I would be very interested (and surprised) if someone manages to prune a majority of experts in a way that preserves model capabilities in a specific domain but not others.
See https://github.com/peteryuqin/Kimi-K2-Mini, a project that keeps a small portion of experts and layers and keep the model capabilities across multiple domains.
Sounds like dumping the routing information from programming questions would answer that... I guess I can do a dump from qwen or deepseek locally. You'd think someone would created that kind of graph already, but I couldn't find one.
What I did find instead is that some MoE models are explicitly domain-routed (MoDEM), but it doesn't apply to deepseek which is just equally load balanced, so it's unlikely to apply to Kimi. On the other hand, https://arxiv.org/html/2505.21079v1 shows modality preferences between experts, even in mostly random training. So maybe there's something there.
Inseparable, routing is done per token in a statistically optimal way, not per request on the knowledge domain basis.
No.
At 1T MoE on 15.5T tokens, K2 is one of the largest open source models to date. But BAAI's TeleFM is 1T dense on 15.7T tokens: https://huggingface.co/CofeAI/Tele-FLM-1T
You can always check here: https://lifearchitect.ai/models-table/
I really really want to try this model for free since I just don't have a gpu.
Is there any way that I could do so?
Open Router? Or does kimi have their own website? Just curious to really try it out!
It kinda feels like it, but Moonshots delivery has been like this before aswell, it was just now their new release got way more highlight than usual. When they released Kimi k1.5, those bench were impressive at the time! But everyone was busy with Deepseek v3 and QwQ-32B
I tried Kimi on a few coding problems that Claude was spinning on. It’s good. It’s huge, way too big to be a “local” model — I think you need something like 16 H200s to run it - but it has a slightly different vibe than some of the other models. I liked it. It would definitely be useful in ensemble use cases at the very least.