8 ms·
Run Kimi K3 using 29 GB of RAM at 0.50 tok/s
- marcobambini 2mo agoThanks guys for all the comments, I am going to rewrite the README (without using an LLM)
- cjbprime 2mo agoDoes it not use Metal, on macOS? Would it be faster if it did?
- scuppernong 2mo ago[dead]
- marcobambini 2mo agoWe tried to use Metal, but for that specific project it was slower than just using NEON ARM optimizations. It is all documented in the docs.
- deleted 2mo ago[deleted]
- pja 2mo agoThat README hits all my “this is authored by an LLM” instincts. I presume the codebase is also written by an LLM?
- gruez 2mo ago>Contributors >... >claude You don't need to presume. If someone is so lazy that they tell claude to commit their code (ie. they're too lazy to run git commit themselves), the chances they reviewed the code is slim.
- k8sToGo 2mo agoWhy do you say lazy? maybe they are ok with people seeing it is claude?
- danirod 2mo agoTo be fair, I appreciate when they are so upfront about who wrote the code without requiring further heuristics, so I encourage this behavior.
- bensyverson 2mo agoYes, I do this all the time, and also check in the co-authored project plans which drove the commits. For a project that is transparently only possible due to agentic coding, I don't see any reason to conceal the methods.
- simonw 2mo agoHonestly, Claude writes better commit messages than most people. Personally I've mostly given in to letting it commit for me now, though I do occasionally take over and hand-write the messages if it's a particularly important concept and Claude's is too verbose. Codex/GPT-x defaults to one-line commit messages, which are too short. Claude likes to write several paragraphs, which is usually too long. If you tell it how to commit properly once per session it will stick with your standards for the rest of that session, and you can put that in AGENTS.md if you can be bothered to.
- VulgarExigency 2mo agoThe agents write much better commit messages than me, at least. They also write better PR descriptions. At work we have a PR authoring skill and we’ve included an instruction for it to write a reviewer guide that tells the most logical way to review the code and I’ve found that really helps, so much so that I’m creating a tool to have “literate” PR reviews, where it constructs a narrative interwoven with diffs.
- Teever 2mo agoThat’s a needlessly antagonistic and insulting thing to say. This person that you’ve never met and probably never will doesn’t owe any of us anything. They’re out there doing what they want to do how they want to do it and if you don’t like it the correct response isn’t to insult them in front of a bunch of strangers on the internet for clout or whatever. I doubt that you’d ever call them lazy to their face — why do it here?
- marcobambini 2mo agoI wrote tons of software, even a programming language by hand https://github.com/marcobambini/gravity https://github.com/marcobambini/gravity. I'm using my skills to orchestrate LLMs and agents, and I can write better code much faster. As developers, we can choose to adapt to new technologies or become extinct.
- misterderpie 2mo ago> I'm using my skills to orchestrate LLMs and agents, and I can write better code much faster. The fact that the top comment on this thread calls it out, in a negative way, hints at that you aren't.
- outworlder 2mo agoNon sequitur
- pja 2mo ago> > I'm using my skills to orchestrate LLMs and agents, and I can write better code much faster. > > The fact that the top comment on this thread calls it out, in a negative way, hints at that you aren't. It's interesting that what I meant as a purely factual question with no prejudice either way has been taken by almost everyone to be explicitly negative & critical. I need to be a tad less blunt I guess if I don't want to
- ensjdidk 2mo ago[dead]
- Wowfunhappy 2mo agoPlease consider writing your Readmes by hand even if the code is computer-generated. I want to read what human authors think about their projects. I virtually never want to read what a computer thought.
- IshKebab 2mo agoAlso irrespective of the merits of LLMs, it's simply unpleasant to read LLM generated prose. It can't write well. The annoyance is compounded when you read the same poor writing everywhere. I don't know how people that shovel AI prose don't realise this. Are they not also reading other people's shitty AI text? I did see one sloperator who told his agent to copy his writing style. I have no idea if that works but it's got to be better than yet more "Here's the kicker" LinkedIn drivel.
- cyanydeez 2mo agodo people think these projects related to LLMs are ever going to be in anyway a pure human endevour? How bout we make a new rule: only complain about LLM writing when the product as zero relevents to use with LLMs.
- deleted 2mo ago[deleted]
- bglazer 2mo agoYeah I'm begging these authors to at least *read* the LLM generated README's. They're so, so incomprehensible because the LLM has a super limited theory of mind for readers. They always assume that external readers have access to the full context and history of decisions in the project development. These decisions and instructions from the user are extremely important for the model and almost completely irrelevant for an outside reader looking at a "finished" product. So, we get sentences like this: "Where the levers were is not where they are. Overlapping the expert reads with the arithmetic was worth ~1.6x and shipped; the two that looked bigger — reading fewer bytes per token, and keeping more of them in RAM — were both measured and both refused, one because this family's router has no tail to demote and one because a cache the machine will not leave resident cannot be bought at any price." What the fuck does that mean? Obviously some internal development decision, using the absolutely inscrutable internal terminology that Claude loves. If people would just read what they publish, I'm sure this would stick out immediately. I'm not an LLM hater, I use them a ton and they work very well for writing complex code, it's undeniable. But they generate absolute dogshit first draft writing.
- chambored 2mo agoIf that isn’t the perfect way to frame what I’ve seen and hated about LLM text, I don’t know what is. They certainly write for an audience with a historical context that almost no one has.
- andai 2mo ago>They're so, so incomprehensible because the LLM has a super limited theory of mind for readers. They always assume that external readers have access to the full context and history of decisions in the project development The transformer does not yet understand the non-transformer.[0] This is probably because all the data we trained it on was created by non-transformers, so it thinks it's a non-transformer, but it isn't. I don't think we know how to train a transformer yet. All the training data is linear, but that's not how they think at all. [0] It's a bit like the communication difficulties experienced between autistic people and neurotypicals. Each follow the Golden Rule, i.e. do unto others as you would have them do unto you -- and it fails in both directions. A Platinum Rule is necessary: do unto others as their API demands.
- sergiomattei 2mo agoDoes it matter?
- jkahrs595 2mo agoI need an llm to block these comments.
- artemonster 2mo agoyes. its a quality test. pre LLMs you could easily judge if a project was a labor of love by attention to details, like docs and README. Nowadays if even readme is sloped, what else was slop vibecoded? everything? how much effort was put in there besides 3 prompts? 5? you can never tell
- guybedo 2mo agoi thought, here on HN, we were past the "oooh it's written by a LLM it's bad!". I care about the craft, well designed systems, good clean architecture and code, etc... But i also care about reaching goals. Whether i do it working on my own, or with human coworkers or with AI coworkers doesn't matter that much to me. Yes, the result is sometimes the most important thing.
- crent 2mo agoThis is my take as well. I think the voice LLMs tend to use in writing sucks but I’m honestly not good enough at English as a discipline to tell you why. That said when it comes to code, did it do the thing and is it causing problems over time? If it did and doesn’t cause more issues than your coworker would, why would someone be so vehemently opposed? Lately I've been landing on a couple of different reasons but I don’t think it’s one size fits all: - Ego/identity - “I am the crafter of code. That is what I do.” If someone has their identity deeply wrapped up in the concept of being a “software engineer” or “programmer” then LLMs are a direct threat to that. People don’t tend to do well with this. Think about dogmatically religious people, pseudoscience followers etc who are confronted with evidence directly contradicting their beliefs. Their entire world view revolves around that identity and if you threaten that, you threaten the foundations of their self-perception. - Career - you can take a lot of what I said above but also map it to threatening someone’s career. What if crafting software by hand becomes a niche, artisanal craft but most software is industrially generated? A lot of people will experience hardship if so and if they can’t find a way to be flexible into the future. - Passion - the ones who love software purely for the craft see this change as robbing them of the one thing they enjoyed in their career. Work is a big part of our life and if you kill the joy for a large group of people, that’s tough to deal with. - Lack of understanding their current purpose in role - I often see software engineers who don’t realize why they were hired. It was never to “write good clean code”, that was a means to an end. The end is generating business value for the company that hired you. That’s really it. It’s banal but it’s just a job like bagging groceries only it has required specialized knowledge so it pays well. Only very niche roles have actually hired for the craftsmanship. If someone is working for Groupon and they believe perfectly elegant systems are the value they provide, they are a bit deluded IMO. They build a platform that peddles coupons. That’s hardly comparable to building software that allows a surgeon to remotely operate a robot that does open heart surgery. Most of us do not work on truly mission critical software like that. Most importantly though, I think this topic needs to be approached with empathy. This is truly a seismic shift in how we, as software developers, work. Change is not easy to cope with especially when it threatens physical safety and identity. Will this change be here to stay and are we in for the extinction of software creation as we have known it? I don’t know. I do know that the world’s financial and governmental systems do seem to be betting on that outcome, however.
- zozbot234 2mo agoYup, I hate to engage in anything that looks like a "shallow dismissal" but the project documentation seems to outright contradict itself wrt. whether it's running the model at genuinely native precision (though the claimed 3-bit quant is potentially interesting) and the headline claim of achieving 2 secs/token in a mere 29GB RAM footprint looks outright nonsensical given what we know about K3 itself (~115GB in dense parameters alone at native precision, plus ~25GB active sparse experts per token and some comparatively minor footprint for the KV cache). This is just not very helpful.
- verisimi 2mo ago[flagged]
- pkulak 2mo agoYes, but very slowly.
- jv-k 2mo agoAbsolutely. Anyone can remove flag and remove typical LLM slop at least. I wrote a simple tool for it as part of my CI: https://github.com/jv-k/deslopper https://github.com/jv-k/deslopper
- a-dub 2mo agoit is definitely the claudiest. it's weird, it's like there's a spot developing in my head next to all the other spots where i park mental models for how people write, but instead of being for a person, it's for the terse-staccato-prosodic-diarrhea that claude generates in readmes by default.
- cdud3 2mo agoI prefer to have such detailed readme's created by a LLM while iterating over no iteration documentation at all and usually the later is the standard.
- jpecar 2mo agoWhere can this 1tb k3.waste be downloaded?
- marcobambini 2mo agoIt is not yet available, the only way is to download the official Kimi K3 model and then convert it: # 1. preflight: reachable? how big? does it fit? tools/fetch_weights.sh --dest /Volumes/staging/k3 --dry-run # 2. download — resumable, safe to kill, safe to re-run tools/fetch_weights.sh --dest /Volumes/staging/k3 # 3. convert into a container uv run --with torch --with safetensors python tools/convert.py \ --src /Volumes/staging/k3 \ --out ~/models/k3.waste --jobs 3
- jpecar 2mo agoYeah, saw this ... was hoping that there's a torrent of it somewhere already. Or something.
- logicallee 2mo agoInteresting project. The headline number (29 GB of RAM) is for 4k context. From what I've read elsewhere, Kimi K3 is quite verbose in its thinking. At the quoted rate, it would generate only a total of 1.8k tokens in 1 hour. Is that enough for it to get any thinking done and produce output on more complicated prompts?
- 0cf8612b2e1e 2mo agoI saw someone’s excellent idea that if you have a slow system like this, you should communicate by email. It is no longer meant for realtime iteration, but more pointed questions for which there is more effort and time expected on both parties.
- rwz 2mo ago0.5t/s is still too slow even for email. For a moderately large inquiry (1MTok output, let's ignore the 4k context window limitation for now) it'll take the model around 23 days or uninterrupted execution to answer a single email. Real world inquiries are gonna be much slower of course, but this setup is still too slow to do anything meaningfully useful I think.
- springtimesun 2mo agoI’m sure it’s possible, but I really struggle to think of an example that would result in a 1m token output.
- 8note 2mo agoit would be 1M tokens worth of work, with some small report for the end
- 0cf8612b2e1e 2mo agoSure, you cannot do 1M, but there are plenty of useful questions you could ask that are far more modest. Simple Q+A, look at this function, how would you design X? All of those could have few paragraphs of outputs that would finish within a day.
- herf 2mo agoSo if this Mac uses 30-50W, that's 40-60 tok/Wh...vs maybe 80k for a modern GPU cluster? So that's about 1000-2000x more power for the SSD streaming, unfortunately.
- walrus01 2mo agoGPU sufficient to hold and run Kimi have a totally different up front capex cost, however.
- SSilver2k2 2mo agoThis sounds a lot like what the colibri project did for GLM-5.2. I'm a fan so keep at it! justvugg.github.io/colibri
- Catloafdev 2mo agoNeat! But, what do you do with a 0.5tk/s LLM? Have you tried running it via llamacpp or other software that supports naive SSD offloading to compare speeds?
- gcampos 2mo agoYou could use it for long run tasks while you don’t use the laptop.
- ElectricalUnion 2mo agoBy the time the tokens start coming out 30h later you might need to use your laptop again...
- dotancohen 2mo agoHave it summarise the week overnight for the meeting in the morning. Then have it summarise the meeting transcription overnight for the report tomorrow. Then someone else will have it summarise the report overnight to read on a 6" handheld screen in the small office the next morning after breakfast.
- ElectricalUnion 2mo agoThen have it summarise the meeting transcription over the entire week for the report next week. ftfy.
- Dylan16807 2mo agoIf we estimate a meeting with pauses between speakers as 2.25 words per second, and .75 words per token, then a meeting generates 3 tokens per second. This says prefill and decode are both .5 tokens per second? Then each hour of meeting turns into 6 hours to read and 1 hour to output a summary. You could summarize two hours of meeting overnight, not too bad.
- 2mo ago
- bgirard 2mo agoApproximate calculation is putting the cost at ~$5 per million tokens (assuming 42W sustained, 20¢/kWh), and that's excluding hardware and other costs.
- AnotherGoodName 2mo agoI got to the same conclusion another way. There's ~2.6million seconds a month and this is getting 0.5tok/s which is 1.3million tokens a month. Give some room for overhead and a reasonable rule of thumb; The cost to run the machine per month is the cost per million tokens.
- Austiiiiii 2mo agoShit, beats the hell out of GitHub CoPilot charging $.01 per "credit." For Opus that's one credit for 400 tokens genned, so $5 converts to 200k tokens down, plus the cost of whatever context you sent up.
- darkwater 2mo agoAnd what if I have PV?
- IshKebab 2mo agoIn the UK you can sell electricity domestically for 20c/kWh so at least here that's the cost if you have PV. If you don't, the cost is higher.
- darkwater 2mo agoIt's not a cost if you self-consume your energy, even if you get paid for what you inject. You are just amortizing differently the cost of the PV installation. But what you get for the energy you sell is not real money, it's just money you MIGHT consume as energy in other periods.
- dannyw 2mo ago
- roundup 2mo agoHow does this project compare to https://github.com/gavamedia/deltafin https://github.com/gavamedia/deltafin ?
- chrisringrose 2mo agoHi! I'm one of the deltafin devs. The biggest difference is that this is not actually a 100% "pure" uncut Kimi K3. This is requantized to 3-bit residual, whereas deltafin is the full real unaltered k3, through and through. WASTE reads about 17 GB/token versus Deltafin’s 25.8 GB/pass—roughly. That's 34% less expert traffic, and some could argue a 34% reduction in quality.
- withinboredom 2mo agoWhy quantize to int8 when k3 is int4? I could be wrong, but that’s what I remember seeing.
- cadamsdotcom 2mo agoDear creator: you didn't ship the first draft of your code - why did you ship the first draft of your README??
- w45wsdfgdgdf 2mo agobecause claude ships these verbose READMEs with its 'honest' takes and justifications for the naming. Its goal is to prime the next Agent that reads it, not you, human
- 8note 2mo agowe need a new HUMANS.md to replace the readme
- righthand 2mo agoI couldnt find anything explaining the name of this company on their website but is it okay that they’re riding on the name of an open source tool? SQLite code itself is public domain but I’m not sure about the name.
- nharada 2mo agoIt's too bad Optane PMem is dead
- throwawayffffas 2mo agoI don't get the fascination how is optane better than a modern nvme? I don't think it was faster.
- PhilipRoman 2mo agoIt still has much better latency than modern NVMEs and much, much better write endurance (probably not critical for AI workloads)
- ElectricalUnion 2mo agoOk, this one will take just 30h (compared to that other project that would take 6.25 days) to start writing output tokens after you say hi in Claude Code.
- mrdootdoot 2mo agoFun. I see the novelty.
- yieldcrv 2mo agolooks helpful your readme is overly verbose agents don't need that and its extremely low signal for humans too tell your language model to get it to the point
- OutOfHere 2mo agoBe advised that the firm behind it ("sqliteai") had a nasty history of using non-open source licenses, e.g. Elastic License. I advise against using anything by them for this reason even if this project currently has an open license.
- airstrafer 2mo agoAre these the sqlite developers..?
- orliesaurus 2mo agono, this is sqliteai aka SQLite Cloud, Inc
- airstrafer 2mo agowow. shamelessly stealing the name of one of the most robust and effective libraries ever, to push some ai bullshit. what an embarrassment
- marcobambini 2mo agoWe are backed by the SQLite author, and we have the right to use the SQLite name. Please do your homework before writing such comments. We used an Elastic license for some projects to protect our work from being used in SASS without a prior agreement. WASTE is and will always be available with a very permissive license. I really don't understand why some people prefer to spread hate instead of just asking for clarification first.
- OutOfHere 2mo agoEven so, sqliteai squandered cultivating its reputation by using a non-free license for various projects.
- oefrha 2mo ago[dead]
- bilsbie 2mo agoClaude might as well be .5 tok/s. I end up waiting several minutes and what it tells me could usually be summarized in under 100 words. So I could potentially live with this if it was concise.
- bpodgursky 2mo agoThis is not how thinking works. Claude uses tens of thousands of thinking tokens to get to 100 words. Kimi is no different.
- no-name-here 2mo agoAdditionally, GP can use the word ‘concise’ (or similar) in their prompt if they want more concise output from a model.
- deleted 2mo ago[deleted]
- walrus01 2mo agoI have one setup that gets about 1.5 tok/s of a very large on prem LLM, on a system that lives under my desk. It's used for overnight project review runs and code review that it is fed at the end of each work day. When I look at it the next morning it has done quite a lot of useful work. Dealing with a big slow LLM as an effective tool is really about planning the workflow to feed it.
- 2mo ago
- brcmthrowaway 2mo agoHow does it compare to dsv4?
- albertomr3 2mo ago[flagged]
- mappu 2mo agoStandard llama.cpp can mmap the gguf, so it'll stay on disk if it doesn't fit on memory, and the kernel page cache will ensure the hot parts ("resident trunk") stay resident. What's the benefit of a custom implementation at all?
- walrus01 2mo agoLetting the kernel use SSD based swap space for something this big would be a good way to destroy its cumulative write endurance over a period of just a couple months. I would be very interested in seeing SMART self reported drive cumulative write and wear out stats if this was done for more than a short test. In my experience llama-server is better run with --no-mmap on things that will fit entirely into RAM. Though obviously you need a 2TB server for full Kimi k3 and 1M context.
- mappu 2mo agoThe really big thing is the model weights, which are a read workload not a write one, it won't affect an SSD's write endurance. The write workloads are just the context and any K/V cache - llama.cpp does not mmap those to disk, so they would remain in memory or VRAM as space affords.
- walrus01 2mo agoI plan to give it a try in a day or two with llama-server from the main branch compiled today, when my Q8 GGUF download of K3 finishes, on a system with 256GB (should be more than ample for context and KV cache and a moderate chunk of the whole 1.6TB). If it works it's going to be sloooooooow as hell, but it'll be an interesting data point to see just how slow.
- johnvanommen 2mo ago> Letting the kernel use SSD based swap space for something this big would be a good way to destroy its cumulative write endurance over a period of just a couple months. Optanes are a good option here, right? I bought mine for $100 for each 128GB DDR4 stick. I believe write performance is off-the-charts on these, besides the fact they're c-h-e-a-p.
- nialv7 2mo agoare they allowed to use the "SQLite" name?
- lukax 2mo agoYes. They are. https://news.ycombinator.com/item?id=46389934 https://news.ycombinator.com/item?id=46389934
- broadsidepicnic 2mo agoWaste is/was a p2p client back in the 2000s
- ikurei 2mo agoThey say it's a waste that you pay for the tokens and then the inference provider pays for the electricity. Isn't that how everything works? I pay cucumbers and the farmers have to pay for the water and the fertilizer... I hope that reasoning is an after-the-fact justification by the LLM that wrote this. It's a ver interesting idea and I wouldn't mind trying it out, but with a smaller model. At 0.5t/s and reading many gigabytes from the SDD every second... I wonder if this wouldn't be extremely practical if targeting a 500gib or 250gib model, something that is still outside most consumers' laptop.
- Garlef 2mo agoIf you grow your own tomatoes, you'll have free tomatoes! (Doesn't really get you a BLT but hey... at least you'll have saved the world a bit because they're not from the supermarket) /s
- make3 2mo agohere at 0.5 tok/s, it's like getting 1/100 of a tomato for your lunch per hour, and you can't just let it grow, you have to look at it growing just as you're about to eat, and it doesn't accumulate between meals
- rcxdude 2mo agoIt is worth thinking about, when you're buying something, how much of what you're paying is the supplier's margin. And whether they have efficiency advantages over you doing it yourself. But for large LLMs it does seem like there's a pretty big efficiency advantage to the rack-scale hardware in datacenters compared to hacks like this.
- hddambo 2mo agoPretty soon we'll have 3T param models down to 1 bit. They'll be able to tell you whether they're off or on.
- theanonymousone 2mo agoFor me at least, it makes way more sense on a DSv4 Flash size and ~10 tps and I would definitely try (or try to try) such a thing. The concept and the proof of it is great, of course.
- tim-projects 2mo agoOnce the tech catches up to the point that we can accurately select the right model for the task, then this ends up becoming a valuabke thing to have. You would spin it up sparingly as part of an automated discovery process maybe for 30 mins a day. And the rest of the time is spent using tiny models. I could see a future like that.
- jurgenburgen 2mo agoThat’s not going to happen. We can’t even estimate how long it will take to complete a backlog item until after we complete it, there is no way to know how complex a task is without doing it.
- tim-projects 2mo agoThat's too high level. Even a basic regex for words leading to tool calls follows the principle. Claude does it right now. We need something more sophisticated, but you could just escalate the model quality if if keeps failing the test. Not elegant but it will work.
- fennecbutt 2mo agoIdk about you guys but I'd find 0.5t/s useless. Even for long tasks. I'd rather just shell out the money to offload as much as possible to say 2x 4060ti 16gb with tensor parallelisation. Anything but that low token rate. This is the sort of thing I'd expect in 20 years for some cyberpunk esque "turtlebot" that thinks at 0.5t/s, is solar powered and performs some menial civic maintenance background task like cutting grass, or scrubbing pavements. Or the "slowbot" that sits in the garden slowly pruning a bonsai, only just keeping up with the growth of the young plant.
- fsuts 2mo agoYes it’s mostly unusable but ongoing iterations of projects like this will eventually lead to a usuable version so good to see
- deleted 2mo ago[deleted]
- preommr 2mo agoI pay $20 for codex, use it daily for coding, and still haven't dipped below 50% for weekly usage. I wouldn't even be able to afford buying 29 gigs of ram, or a new video card with hardware prices the way they are now. Maybe if it was 2016-2018 prices, I'd think about it.
- root_axis 2mo agoWell, if you only ever needed a $20 subscription's worth of tokens it would never make any sense to buy hardware since even at 2018 prices you could buy 20 years of subscriptions.
- fragmede 2mo agoAssuming $20 subscriptions continue to be available for 20 years. Maybe they will, I don't have a time machine, but $20 subscriptions seem to be the $1 Uber ride level of pricing.
- whatsThisBtn4 2mo agoPeople who bought CPU instead of Nvidia are coping with this reality. I'm not sure how this happens. Reality distortion field? Like, it is common knowledge at this point right?
- ruler88 2mo agoReally cool!! For all of the other commenters - this project isn't about practicality today. Obviously this isn't gonna be as good as using a cloud provider. But the tool draws a line of what is possible. Combination of making the models more efficient, and making local machines more capable can one day get us to a world where very high quality local models are economically feasible.
- deleted 2mo ago[deleted]
- JakaJancar 2mo agoLove that it’s embeddable, I needed exactly this for the upcoming app I’m shipping!
- 1vuio0pswjnm7 2mo ago28 Jul 2026 21:35:19 UTC Running Kimi K3 on a M1 Max https://github.com/gavamedia/deltafin https://github.com/gavamedia/deltafin https://news.ycombinator.com/item?id=49090233 https://news.ycombinator.com/item?id=49090233 [ok] 29 Jul 2026 02:01:04 UTC Kimi K3 running on any device locally https://github.com/RightNow-AI/local-kimi https://github.com/RightNow-AI/local-kimi https://news.ycombinator.com/item?id=49092591 https://news.ycombinator.com/item?id=49092591 [dead] 29 Jul 2026 13:49:32 UTC Show HN: Waste Run the full 2.78T-parameter Kimi K3 on a laptop https://news.ycombinator.com/item?id=49097512 https://news.ycombinator.com/item?id=49097512 [flagged] [dead] 29 Jul 2026 14:38:35 UTC Self-hosting Kimi K3: 20% more hardware cost, 20% better task resolution https://aistack.imec-int.com/blog/gpu-self-hosting https://aistack.imec-int.com/blog/gpu-self-hosting https://news.ycombinator.com/item?id=49098130 https://news.ycombinator.com/item?id=49098130 [ok] 29 Jul 2026 14:57:14 UTC Run Kimi K3 on a local computer https://github.com/sqliteai/waste https://github.com/sqliteai/waste https://news.ycombinator.com/item?id=49098395 https://news.ycombinator.com/item?id=49098395 [ok] 29 Jul 2026 15:41:50 UTC Show HN: A new engine to run Kimi K3 on a laptop https://news.ycombinator.com/item?id=49098966 https://news.ycombinator.com/item?id=49098966 [ok] 29 Jul 2026 19:54:13 UTC Kimi K3 for local use (1.56TB > 594GB) compressed by Unsloth https://huggingface.co/unsloth/Kimi-K3-GGUF https://huggingface.co/unsloth/Kimi-K3-GGUF https://news.ycombinator.com/item?id=49102192 https://news.ycombinator.com/item?id=49102192 [ok] 29 Jul 2026 20:02:40 UTC Kimi k3 now runs on one consumer GPU https://twitter.com/Akashi203/status/2082555972380401852 https://twitter.com/Akashi203/status/2082555972380401852 https://news.ycombinator.com/item?id=49102291 https://news.ycombinator.com/item?id=49102291 [ok] 30 Jul 2026 06:12:55 UTC Show HN: Run Full Kimi K3 with 29 GB of RAM https://github.com/sqliteai/waste/ https://github.com/sqliteai/waste/ https://news.ycombinator.com/item?id=49106591 https://news.ycombinator.com/item?id=49106591 [ok] 30 Jul 2026 13:05:09 UTC Kimi k3 run on RTX 5090 https://github.com/RightNow-AI/local-kimi https://github.com/RightNow-AI/local-kimi https://news.ycombinator.com/item?id=49109455 https://news.ycombinator.com/item?id=49109455 [ok] 30 Jul 2026 14:01:44 UTC A new inference engine to run Kimi K3 2.78T parameter with 29GB of RAM https://marcobambini.substack.com/p/the-waste-inference-engine https://marcobambini.substack.com/p/the-waste-inference-engi... https://news.ycombinator.com/item?id=49110183 https://news.ycombinator.com/item?id=49110183 [ok] 30 Jul 2026 16:52:58 UTC Running Kimi K3 on a local computer https://github.com/sqliteai/waste https://github.com/sqliteai/waste https://news.ycombinator.com/item?id=49112587 https://news.ycombinator.com/item?id=49112587 [ok]
- danieltk76 2mo agowow that sounds miserable...
- darkoob12 2mo agoOne thing about local llm projects is that they don't mention the maximum context length which for this project is 4k. I think for it to be used with a harness you need at least 200k and reliable tool calling and structured output which obviously degrade at high quantizations.
- LoveMortuus 2mo agoI was going to try it with my laptop RTX 3080 16GB VRAM and 32GB RAM, but there's a requirement that I couldn't fulfill... 1TB of storage...