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.
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 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 tried it a couple of times in comparison to Claude. Kimi wrote much simpler and more readable code than Claude's over-engineered solutions.
It missed a few minor subtle edge cases that Claude took care of though.
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.
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.
Yes, this quote is by Baudrillard. None of the other models fixated on the fact that it’s an email (I only used the word once in the prompt). My gut feeling is that this reflects not so much a lack of intelligence as a difference in model personality. Here's what it replied when I shared Gemini's analysis. The point was to have it understand that:
Gemini, second sentence: It is less a simple email than an intellectual
and personal exploration still in the making.
Point deflected as soon as understood:
**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.
The solution to sycophancy is not disparagement (misplaced criticism). The classical true/false positive/negative dilemma is at play here. I guess the bot got caught in the crossfire of 1°) its no-bullshit attitude (it can only be an attitude) 2°) preference for delivering blunt criticism over insincere flattery 3°) being a helpful assistant. Remove point 3°), and it could have replied: "I'm not engaging in this nonsense". Preserve it and it will politely suggest that you condense your bullshit text, because shorter explanations are better than long winding rants (it's probably in the prompt).
For what it's worth, I think Kimi's modified MIT license still meets the OSI definition of "open source." For example, the explicitly OSI-approved "Attribute Assurance License"[1] contains similar wording:
> each time the resulting executable program or a program dependent thereon is launched, a prominent display (e.g., splash screen or banner text) of the Author’s attribution information
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.
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:
Technical strengths aside, I’ve been impressed with how non-robotic Kimi K2 is. Its personality is closer to Anthropic’s best: pleasant, sharp, and eloquent. A small victory over botslop prose.
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.
I found that while looking for reports of the best agents to use with K2. The usual suspects like Cline and forks, Aider, and Zed should be interesting to test with K2 as well.
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.
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.
"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.
According to the benchmarks, Kimi K2 beats GPT-4.1 in many ways. So to "compete", OpenAI would have to release the GPT-4.1 weights, or a similar model. Which, I guess, they likely won't do.
I like new, solid non-reasoning models that push the frontier. These still have nice use cases (basically anything where logic puzzles or STEM subjects don't apply) where you don't want to spend cash on reasoning tokens.
If the SWE Bench results are to be believed... this looks best in class right now for a local LLM. To be fair, show me the guy who is running this locally...
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 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.
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.
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 seems like a combination of Llama's "prominently display “Built with Llama”" and "greater than 700 million monthly active users" terms but put into one and masquerading as "slightly changed MIT".
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.
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.
"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.
It's silly, but in the LLM world - "open source" is usually used to mean "weights are published". This is not to be confused with the software licensing meaning of "open source".
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.
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!
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.
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.
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).
This is the model release that made Sam Altman go "Oh wait actually we can't release the new open source model this week, sorry. Something something security concerns".
Perhaps their open source model release doesn't look so good compared to this one
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
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.
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.
I chatted with this model about stress testing Hazelcast and comparing/contrasting Java Virtual Threads, Goroutines and Kotlin's Coroutines. I really liked its responses. They were concise and useful.
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.
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
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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
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> or even a single socket Epyc system with >1TB of RAM
How many tokens/second would this likely achieve?
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This is fairly affordable if you’re a business honestly
looks very much usable for local usage.
I tried it a couple of times in comparison to Claude. Kimi wrote much simpler and more readable code than Claude's over-engineered solutions. It missed a few minor subtle edge cases that Claude took care of though.
Claude what? Sonnet? 3.7? 3.5? Opus? 4?
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.
Yes, this quote is by Baudrillard. None of the other models fixated on the fact that it’s an email (I only used the word once in the prompt). My gut feeling is that this reflects not so much a lack of intelligence as a difference in model personality. Here's what it replied when I shared Gemini's analysis. The point was to have it understand that:
Point deflected as soon as understood:
The solution to sycophancy is not disparagement (misplaced criticism). The classical true/false positive/negative dilemma is at play here. I guess the bot got caught in the crossfire of 1°) its no-bullshit attitude (it can only be an attitude) 2°) preference for delivering blunt criticism over insincere flattery 3°) being a helpful assistant. Remove point 3°), and it could have replied: "I'm not engaging in this nonsense". Preserve it and it will politely suggest that you condense your bullshit text, because shorter explanations are better than long winding rants (it's probably in the prompt).
Pelican on a bicycle result: https://simonwillison.net/2025/Jul/11/kimi-k2/
For what it's worth, I think Kimi's modified MIT license still meets the OSI definition of "open source." For example, the explicitly OSI-approved "Attribute Assurance License"[1] contains similar wording:
> each time the resulting executable program or a program dependent thereon is launched, a prominent display (e.g., splash screen or banner text) of the Author’s attribution information
[1] https://opensource.org/license/attribution-php
It probably doesn't because the attribution requirement discriminates against certain groups (large commercial organisations).
Huh, I hadn't seen that one before!
At this point, they have to be training it. At what point will you start using something else?
Once I get a picture that genuinely looks like a pelican riding a bicycle!
I'm glad we are looking to build nuclear reactors so we can do more of this...
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.
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"I'm glad we are looking to build nuclear reactors so we can do more of this..."
Does this actually mean "they" not "we"
I honestly don't see an issue with that.
Except that instead of this, we're spinning up old coal plants, because apparently nuclear bad.
Much better than that of Grok 4.
That's perhaps the best one I've seen yet! For an open weight model, this performance is of course particularly remarkable and impactful.
wow!
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.
>If someone took Kimi k2 and trained a reasoning model with it
I imagine that's what they are going at MoonshotAI right now
Why hasn’t Kimis current and older models been benchmarked and added to Artificial analysis yet?
[dead]
Technical strengths aside, I’ve been impressed with how non-robotic Kimi K2 is. Its personality is closer to Anthropic’s best: pleasant, sharp, and eloquent. A small victory over botslop prose.
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.
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
Here's a neat looking project that allows for using other models with Claude Code: https://github.com/musistudio/claude-code-router
I found that while looking for reports of the best agents to use with K2. The usual suspects like Cline and forks, Aider, and Zed should be interesting to test with K2 as well.
how do you low cost run a 1T param model?
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According to the bench its closer to Opus, but I venture primarily for English and Chinese.
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 new too. Found this article helpful: https://crawshaw.io/blog/programming-with-agents
> 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.
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.
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According to the benchmarks, Kimi K2 beats GPT-4.1 in many ways. So to "compete", OpenAI would have to release the GPT-4.1 weights, or a similar model. Which, I guess, they likely won't do.
To me, K2 is a mountain and SOTA is “summits on the air”. I saw that headline and thought “holy crap” :-)
To me K2 is the Kotlin 2.0 compiler. https://blog.jetbrains.com/kotlin/2023/02/k2-kotlin-2-0/
I like new, solid non-reasoning models that push the frontier. These still have nice use cases (basically anything where logic puzzles or STEM subjects don't apply) where you don't want to spend cash on reasoning tokens.
If the SWE Bench results are to be believed... this looks best in class right now for a local LLM. To be fair, show me the guy who is running this locally...
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 not open source, they have a "modified MIT license" where they have other restrictions on users over a certain threshold.
> 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.
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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.
That seems like a combination of Llama's "prominently display “Built with Llama”" and "greater than 700 million monthly active users" terms but put into one and masquerading as "slightly changed MIT".
The difference is it doesn't include Llama's usage restrictions that disqualify it from being an Open Source license.
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.
[1]: https://en.wikipedia.org/wiki/License_proliferation
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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.
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That's basically less restrictive than OpenStreetMap.
What part of this goes against the four fundamental freedoms? Can you point at it?
Exactly, I wouldn’t mind adding that text on our service if we made 20m $, the parent made it sound like a huge clause
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"The freedom to run the program as you wish, for any purpose (freedom 0)."
Being required to display branding in that way contradicts "run the program as you wish".
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It's silly, but in the LLM world - "open source" is usually used to mean "weights are published". This is not to be confused with the software licensing meaning of "open source".
The more tasteful corners of the LLM world use "open weights" instead of "open source" for licenses that aren't OSI.
This is just so Google doesn't build a woke version of it and calls it gemini-3.0-pro
"Open source" lol
Open-weight. As usual, you don't get the dataset, training scripts, etc.
Wont happen under the current copyright regime, it is impossible to train SOTA without copyrighted text, how do you propose distributing that?
Bibtex
List the titles.
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It's not even open-weight. It's weight-available. It uses a "modified MIT license":
This seems significantly more permissive than GPL. I think it's reasonable to consider it open-weight.
4-clause BSD is considered open source by Debian and the FSF and has a similar requirement.
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!
We really need to stop diluting the meaning of open source
Would be hilarious if Zuck with his billion dollar poaching failed to beat budget Chinese 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.
I thought that too
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I can't tell if Kimi is quite top tier, but since Llama 4 performed so poorly then yes, this did in fact happen just now.
Wikipedia listed a FAIR alumni as cofounder for this "Moonshot AI". Make it funnier probably.
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.
[1] https://en.wikipedia.org/wiki/Moonshot_AI
This is both the largest oss model release thus far, and the largest Muon training run.
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.
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).
Which host did you use? I noticed the same using parasail. Switching to novita and temp 0.4 solved it.
The host was Moonshot AI at Kimi dot com :)
This is the model release that made Sam Altman go "Oh wait actually we can't release the new open source model this week, sorry. Something something security concerns".
Perhaps their open source model release doesn't look so good compared to this one
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?
I don't know, but as someone who likes using em-dashes in my writing it is disappointing that they have become a marker of LLM slop.
> 1T total / 32B active MoE model
Is this the largest open-weight model?
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 believe so.
Grok-1 is 341B, DeepSeek-v3 is 671B, and recent new open weights models are around 70B~300B.
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.
Sure, it's done per token, but the question is: how much do the knowledge domains match up with experts. I could not find hard data on this.
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I chatted with this model about stress testing Hazelcast and comparing/contrasting Java Virtual Threads, Goroutines and Kotlin's Coroutines. I really liked its responses. They were concise and useful.
Quite impressive benchmark, how come I don't see Kimi in Artificial analysis benchmarks?
kimi K2 really excels at autonomous tool use, complex reasoning, and multi-step task execution.
I developed an intelligent vector database agent using Kimi K2 and Milvus, which enhances document interaction via natural language commands.
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.
Impressive benchmarks!
I love the fact that I can use this right away and test it out in practice. The ecosystem around LLM is simply awesome and improving by the day.
Glad it’s non-reasoning.
Often a faster answer is more useful to me for quick research. Reasoning has its place but don’t think that place is always
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!
Kimi.com
The problem with Chinese models is finding decent hosting. The best you can find right now for kimi k2 is only 30 tps, not great.
Open source" lol
It's open-weight. As usual, you don't get the dataset, training scripts, etc.
How does it stack up against the new Grok 4 model?
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The web chat has extremely low limits FYI. I ran into the limit twice before getting a sane answer and gave up
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The web chat has extremely low limits FYI. I ran into the limit twice before getting a sane answer and gave up
You can use it on OpenRouter without limits (paid API calls)
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Is Kimi the new deep seek?
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