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Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model
GitHub: https://github.com/MoonshotAI/Kimi-K2 https://github.com/MoonshotAI/Kimi-K2
- gs17 1y ago> 1T total / 32B active MoE model Is this the largest open-weight model?
- bigeagle 1y agoI believe so. Grok-1 is 341B, DeepSeek-v3 is 671B, and recent new open weights models are around 70B~300B.
- adt 1y agoNo. 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 https://huggingface.co/CofeAI/Tele-FLM-1T You can always check here: https://lifearchitect.ai/models-table/ https://lifearchitect.ai/models-table/
- simonw 1y agoBig release - https://huggingface.co/moonshotai/Kimi-K2-Instruct https://huggingface.co/moonshotai/Kimi-K2-Instruct model weights are 958.52 GB
- c4pt0r 1y agoPaired with programming tools like Claude Code, it could be a low-cost/open-source replacement for Sonnet
- martin_ 1y agohow do you low cost run a 1T param model?
- maven29 1y ago32B active parameters with a single shared expert.
- JustFinishedBSG 1y agoThis doesn’t change the VRAM usage, only the compute requirements.
- maven29 1y agoYou can probably run this on CPU if you have a 4090D for prompt processing, since 1TB of DDR4 only comes out to around $600. For GPU inference at scale, I think token-level batching is used.
- t1amat 1y agoWith 32B active parameters it would be ridiculously slow at generation.
- selfhoster11 1y agoDDR3 workstation here - R1 generates at 1 token per second. In practice, this means that for complex queries, the speed of replying is closer to an email response than a chat message, but this is acceptable to me for confidential queries or queries where I need the model to be steerable. I can always hit the R1 API from a provider instead, if I want to. Given that R1 uses 37B active parameters (compared to 32B for K2), K2 should be slightly faster than that - around 1.15 tokens/second.
- CamperBob2 1y agoThat's pretty good. Are you running the real 600B+ parameter R1, or a distill, though?
- deleted 1y ago
- kkzz99 1y agoAccording to the bench its closer to Opus, but I venture primarily for English and Chinese.
- scottyeager 1y agoHere's a neat looking project that allows for using other models with Claude Code: https://github.com/musistudio/claude-code-router 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.
- cyanf 1y agoThis is both the largest oss model release thus far, and the largest Muon training run.
- mistressgabby 1y ago[flagged]
- wiradikusuma 1y agoI'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.
- ozten 1y agoIt 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.
- simonw 1y ago"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.
- dcre 1y agoReasonable 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.
- simonw 1y agoPelican on a bicycle result: https://simonwillison.net/2025/Jul/11/kimi-k2/ https://simonwillison.net/2025/Jul/11/kimi-k2/
- _alex_ 1y agowow!
- ebiester 1y agoAt this point, they have to be training it. At what point will you start using something else?
- simonw 1y agoOnce I get a picture that genuinely looks like a pelican riding a bicycle!
- qmmmur 1y agoI'm glad we are looking to build nuclear reactors so we can do more of this...
- sergiotapia 1y agome 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.
- GenerWork 1y agoYou're saying this in jest, but I would LOVE to have a nuclear reactor in my backyard that produced enough power to where I could have a minisplit for every room in my house, including the garage so I could work out in there.
- CaptainFever 1y agoRelated: https://en.wikipedia.org/wiki/Kardashev_scale https://en.wikipedia.org/wiki/Kardashev_scale > The Kardashev scale (Russian: шкала Кардашёва, romanized: shkala Kardashyova) is a method of measuring a civilization's level of technological advancement based on the amount of energy it is capable of harnessing and using. > Under this scale, the sum of human civilization does not reach Type I status, though it continues to approach it.
- MaxPock 1y agoWould be hilarious if Zuck with his billion dollar poaching failed to beat budget Chinese models.
- physix 1y agoThat 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.
- stogot 1y agoI thought that too
- physix 1y agoIn the meantime, I discovered that it might simply be a type of acquisition that circumvents regulatory oversight https://medium.com/@villispeaks/the-blitzhire-acquisition-e39361ed00bb https://medium.com/@villispeaks/the-blitzhire-acquisition-e3... seen from https://news.ycombinator.com/item?id=44553257 https://news.ycombinator.com/item?id=44553257
- rfoo 1y agoWikipedia listed a FAIR alumni as cofounder for this "Moonshot AI". Make it funnier probably.
- jug 1y agoI 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.
- aliljet 1y agoIf 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...
- selfhoster11 1y agoIt'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.
- helloericsf 1y agoHow does it stack up against the new Grok 4 model?
- Imustaskforhelp 1y agoI 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!
- blahgeek 1y agoKimi.com
- Alifatisk 1y agoQuite impressive benchmark, how come I don't see Kimi in Artificial analysis benchmarks?
- viraptor 1y agoHow 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)
- orbital-decay 1y agoInseparable, routing is done per token in a statistically optimal way, not per request on the knowledge domain basis.
- deleted 1y ago[deleted]
- viraptor 1y agoSure, 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.
- boroboro4 1y agoCheck out DeepSeek v3 model paper. They changed the way they train experts (went from aux loss to different kind expert separation training). It did improve experts domain specialization, they have neat graphics on it in the paper.
- renonce 1y agoMy 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 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.
- viraptor 1y ago
- 38 1y agoThe web chat has extremely low limits FYI. I ran into the limit twice before getting a sane answer and gave up
- deleted 1y ago[deleted]
- brcmthrowaway 1y agoIs Kimi the new deep seek?
- Alifatisk 1y agoIt 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
- ozgune 1y agoThis 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/ https://artificialanalysis.ai/ If someone took Kimi k2 and trained a reasoning model with it, I’d be curious how that model performs.
- GaggiX 1y ago>If someone took Kimi k2 and trained a reasoning model with it I imagine that's what they are going at MoonshotAI right now
- Alifatisk 1y agoWhy hasn’t Kimis current and older models been benchmarked and added to Artificial analysis yet?
- the_precipitate 1y ago[dead]
- awestroke 1y agoThis 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
- data_maan 1y ago"Open source" lol Open-weight. As usual, you don't get the dataset, training scripts, etc.
- mistercheph 1y agoWont happen under the current copyright regime, it is impossible to train SOTA without copyrighted text, how do you propose distributing that?
- irthomasthomas 1y agoList the titles.
- mixel 1y agoBut probably they don't have the rights to actually train on them and that's why they do not publish the list. Otherwise it may be laziness who knows
- msk-lywenn 1y agoBibtex
- CaptainFever 1y agoIt'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.
- data_maan 1y agoOpen source" lol It's open-weight. As usual, you don't get the dataset, training scripts, etc.
- vessenes 1y agoI 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.
- summarity 1y agoReasonable speeds are possible with 4bit quants on 2 512GB Mac Studios (MLX TB4 Ring - see https://x.com/awnihannun/status/1943723599971443134 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.
- refulgentis 1y agoI 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
- segmondy 1y agoI 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.
- jug 1y agoI 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.
- fzysingularity 1y agoIf I had to guess, the OpenAI open-source model got delayed because Kimi K2 stole their thunder and beat their numbers.
- tempaccount420 1y agoTime to RL the hell out of it so it looks better on benchmarks... It's going to be fried.
- fzysingularity 1y agoIf I had to guess, the OpenAI open-source model got delayed because Kimi K2 stole their thunder and beat their numbers.
- irthomasthomas 1y agoSomeone 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.
- johnb231 1y agoThese 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_guidance.md 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.
- ls612 1y agoThis is a dumb question I know, but how expensive is model distillation? How much training hardware do you need to take something like this and create a 7B and 12B version for consumer hardware?
- satvikpendem 1y agoThis 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.
- diggan 1y agoThat 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".
- mrob 1y agoThe difference is it doesn't include Llama's usage restrictions that disqualify it from being an Open Source license.
- kragen 1y agoI 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.
- unit149 1y ago[dead]
- bhouston 1y agoImpressive benchmarks!
- emacdona 1y agoTo me, K2 is a mountain and SOTA is “summits on the air”. I saw that headline and thought “holy crap” :-)
- esafak 1y agoTo me K2 is the Kotlin 2.0 compiler. https://blog.jetbrains.com/kotlin/2023/02/k2-kotlin-2-0/ https://blog.jetbrains.com/kotlin/2023/02/k2-kotlin-2-0/
- 38 1y agoThe web chat has extremely low limits FYI. I ran into the limit twice before getting a sane answer and gave up
- awestroke 1y agoYou can use it on OpenRouter without limits (paid API calls)
- exegeist 1y agoTechnical 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.
- orbital-decay 1y agoI 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.
- MichaelKSpencer 1y ago[dead]
- pxc 1y agoSo 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).
- irthomasthomas 1y agoWhich host did you use? I noticed the same using parasail. Switching to novita and temp 0.4 solved it.
- pxc 1y agoThe host was Moonshot AI at Kimi dot com :)
- jacooper 1y agoThe problem with Chinese models is finding decent hosting. The best you can find right now for kimi k2 is only 30 tps, not great.
- sagarpatil 1y agoAll 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?
- cristoperb 1y agoI 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.
- deleted 1y ago[deleted]
- MichaelKSpencer 1y ago[dead]
- ksec 1y agoKimi 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 https://en.wikipedia.org/wiki/Moonshot_AI
- RandyOrion 1y agoThis 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.
- lvl155 1y agoI 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.
- Havoc 1y agoGlad 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
- mring33621 1y agoI 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.
- LuminaWang7 1y agokimi 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.