8 ms·
LLMs on device is the future. It's more secure and solves the problem of too much demand for inference compared to data center supply, it also would use less el
by babblingfish 6mo ago
LLMs on device is the future. It's more secure and solves the problem of too much demand for inference compared to data center supply, it also would use less electricity. It's just a matter of getting the performance good enough. Most users don't need frontier model performance.
- gedy 6mo agoMan I really hope so, as, as much as I like Claude Code, I hate the company paying for it and tracking your usage, bullshit management control, etc. I feel like I'm training my replacement. Things feel like they are tightening vs more power and freedom. On device I would gladly pay for good hardware - it's my machine and I'm using as I see fit like an IDE.
- susupro1 6mo ago[dead]
- aurareturn 6mo agoWhen local LLMs get good enough for you to use delightfully, cloud LLMs will have gotten so much smarter that you'll still use it for stuff that needs more intelligence.
- gedy 6mo agoTrue, but I'm already producing code/features faster than company knows what to do with, (even though every company says "omg we need this yesterday", etc). Even coding before AI was basically same. Code tools that free my time up is very nice.
- dgb23 6mo agoThat's not necessarily the case. So far, commercial cloud LLMs have maintained a head-start, but there is no law of nature that prevents us from having competitive open models. In fact the space seems to move at a rapid pace as more and more specialized models come out. There's a possible trajectory where open weight models will compete side by side or even be preferable for many use cases, just like what happened with OS's and SQL DB's.
- aurareturn 6mo agoIt isn't going to replace cloud LLMs since cloud LLMs will always be faster in throughput and smarter. Cloud and local LLMs will grow together, not replace each other. I'm not convinced that local LLMs use less electricity either. Per token at the same level of intelligence, cloud LLMs should run circles around local LLMs in efficiency. If it doesn't, what are we paying hundreds of billions of dollars for? I think local LLMs will continue to grow and there will be an "ChatGPT" moment for it when good enough models meet good enough hardware. We're not there yet though. Note, this is why I'm big on investing in chip manufacture companies. Not only are they completely maxed out due to cloud LLMs, but soon, they will be double maxed out having to replace local computer chips with ones that are suited for inferencing AI. This is a massive transition and will fuel another chip manufacturing boom.
- AugSun 6mo agoLooking at downvotes I feel good about SDE future in 3-5 years. We will have a swamp of "vibe-experts" who won't be able to pay 100K a month to CC. Meanwhile, people who still remember how to code in Vim will (slowly) get back to pre-COVID TC levels.
- QuantumNomad_ 6mo agoWhat is CC and TC? I have not heard these abbreviations (except for CC to mean credit card or carbon copy, neither of which is what I think you mean here).
- Ericson2314 6mo agoI figured it out from context clues CC: Claude Code TC: total comp(ensation)
- AugSun 6mo agoThank you for clarifying! (I had no idea it needs to be explained, sorry.)
- deleted 6mo ago
- AugSun 6mo ago"Most users don't need frontier model performance" unfortunately, this is not the case.
- AugSun 6mo ago[flagged]
- seanhunter 6mo agoComplaining about downvotes is futile and is also against hn guidelines.
- AugSun 6mo agoI'm not complaining "about downvotes" LOL I'm explaining why some people will be replaced by LLMs because of their own "context window" length.
- selcuka 6mo agoAny citations? Because that was my impression, too. I want frontier model performance for my coding assistant, but "most users" could do with smaller/faster models. ChatGPT free falls back to GPT-5.2 Mini after a few interactions.
- asutekku 6mo agoFrontier model has much better knowledge and they usually hallucinate less. It's not about the coding capabilities, it's about how much you can trust the model.
- Barbing 6mo agore: trust- Have you tried the free version of ChatGPT? It is positively appalling. It’s like GPT 3.5 but prompted to write three times as much as necessary to seem useful. I wonder how many people have embarrassed themselves, lost their jobs, and been critically misinformed. All easy with state-of-the-art models but seemingly a guarantee with the bottom sub-slop tier. Is the average person just talking to it about their day or something?
- melvinroest 6mo agoI have journaled digitally for the last 5 years with this expectation. Recently I built a graphRAG app with Qwen 3.5 4b for small tasks like classifying what type of question I am asking or the entity extraction process itself, as graphRAG depends on extracted triplets (entity1, relationship_to, entity2). I used Qwen 3.5 27b for actually answering my questions. It works pretty well. I have to be a bit patient but that’s it. So in that particular use case, I would agree. I used MLX and my M1 64GB device. I found that MLX definitely works faster when it comes to extracting entities and triplets in batches.
- nkzd 6mo agoDid you get any insights about yourself from this process? I am thinking of doing the same
- melvinroest 6mo agoTL;DR: you don't need to do any treasure hunt on your notes by just typing stuff into the search bar. Having your own graphRAG system + LLM on your notes is basically a "Google" but then on your own notes. Any question you have: if you have a note for it, it will bubble up. The annoying thing is that false positives will also bubble up. ---- Full reaction: Yes but perhaps not in a way you might expect. Qwen's reasoning ability isn't exactly groundbreaking. But it's good enough to weave a story, provided it has some solid facts or notes. GraphRAG is definitely a good way to get some good facts, provided your notes are valuable to you and/or contain some good facts. So the added value is that you now have a super charged information retrieval system on your notes with an LLM that can stitch loose facts reasonably well together, like a librarian would. It's also very easy to see hallucinations, if you recognize your own writing well, which I do. The second thing is that I have a hard time rereading all my notes. I write a lot of notes, and don't have the time to reread any of them. So oftentimes I forget my own advice. Now that I have a super charged information retrieval system on my notes, whenever I ask a question: the graphRAG + LLM search for the most relevant notes related to my question. I've found that 20% of what I wrote is incredibly useful and is stuff that I forgot. And there are nuggets of wisdom in there that are quite nuanced. For me specifically, I've seen insights in how I relate to work that I should do more with. I'll probably forget most things again but I can reuse my system and at some point I'll remember what I actually need to remember. For example, one thing I read was that work doesn't feel like work for me if I get to dive in, zoom out, dive in, zoom out. Because in the way I work as a person: that means I'm always resting and always have energy for the task that I'm doing. Another thing that it got me to do was to reboot a small meditation practice by using implementation intentions (e.g. "if I wake up then I meditate for at least a brief amount of time"). What also helps is to have a bit of a back and forth with your notes and then copy/paste the whole conversation in Claude to see if Claude has anything in its training data that might give some extra insight. It could also be that it just helps with firing off 10 search queries and finds a blog post that is useful to the conversation that you've had with your local LLM.
- pezgrande 6mo agoYou could argue that the only reason we have good open-weight models is because companies are trying to undermine the big dogs, and they are spending millions to make sure they dont get too far ahead. If the bubble pops then there wont be incentive to keep doing it.
- aurareturn 6mo agoI agree. I can totally see in the future that open source LLMs will turn into paying a lumpsum for the model. Many will shut down. Some will turn into closed source labs. When VCs inevitably ask their AI labs to start making money or shut down, those free open source LLMS will cease to be free. Chinese AI labs have to release free open source models because they distill from OpenAI and Anthropic. They will always be behind. Therefore, they can't charge the same prices as OpenAI and Anthropic. Free open source is how they can get attention and how they can stay fairly close to OpenAI and Anthropic. They have to distill because they're banned from Nvidia chips and TSMC. Before people tell me Chinese AI labs do use Nvidia chips, there is a huge difference between using older gimped Nvidia H100 (called H20) chips or sneaking around Southeast Asia for Blackwell chips and officially being allowed to buy millions of Nvidia's latest chips to build massive gigawatt data centers.
- spiderfarmer 6mo ago“They will always be behind” Car manufacturers said the same.
- aurareturn 6mo agoIt did take decades to catch and surpass US car makers right?
- seanmcdirmid 6mo agoAbout 2.5 decades from the start of the JVs, but they did it. Semiconductors and jet turbines are really the last two tech trees that China has yet to master.
- deleted 6mo ago[deleted]
- karimf 6mo agoDepending on the use case, the future is already here. For example, last week I built a real-time voice AI running locally on iPhone 15. One use case is for people learning speaking english. The STT is quite good and the small LLM is enough for basic conversation. https://github.com/fikrikarim/volocal https://github.com/fikrikarim/volocal
- Barbing 6mo agoBrilliant. Hope to see you in the App Store!
- podlp 6mo agoThat’s awesome! I’ve got a similar project for macOS/ iOS using the Apple Intelligence models and on-device STT Transcriber APIs. Do you think it the models you’re using could be quantized more that they could be downloaded on first run using Background Assets? Maybe we’re not there yet, but I’m interested in a better, local Siri like this with some sort of “agentic lite” capabilities.
- karimf 6mo ago> Do you think it the models you’re using could be quantized more that they could be downloaded on first run using Background Assets? I first tried the Qwen 3.5 0.8B Q4_K_S and the model couldn't hold a basic conversation. Although I haven't tried lower quants on 2B. I'm also interested on the Apple Foundation models, and it's something I plan to try next. AFAIK it's on par with Qwen-3-4B [0]. The biggest upside as you alluded to is that you don't need to download it, which is huge for user onboarding. [0] https://machinelearning.apple.com/research/apple-foundation-models-2025-updates https://machinelearning.apple.com/research/apple-foundation-...
- troad 6mo agoI very recently installed llama.cpp on my consumer-grade M4 MBP, and I've been having loads of fun poking and prodding the local models. There's now a ChatGPT style interface baked into llama.cpp, which is very handy for quick experimentation. (I'm not entirely sure what Ollama would get me that llama.cpp doesn't, happy to hear suggestions!) There are some surprisingly decent models that happily fit even into a mere 16 gigs of RAM. The recent Qwen 3.5 9B model is pretty good, though it did trip all over itself to avoid telling me what happened on Tiananmen Square in 1989. (But then I tried something called "Qwen3.5-9B-Uncensored-HauhauCS-Aggressive", which veers so hard the other way that it will happily write up a detailed plan for your upcoming invasion of Belgium, so I guess it all balances out?)
- whackernews 6mo agoOh does llama.cpp use MLX or whatever? I had this question, wonder if you know? A search suggests it doesn’t but I don’t really understand.
- irusensei 6mo ago>Oh does llama.cpp use MLX or whatever? No. It runs on MacOS but uses Metal instead of MLX.
- zozbot234 6mo agoANE-powered inference (at least for prefill, which is a key bottleneck on pre-M5 platforms) is also in the works, per https://github.com/ggml-org/llama.cpp/issues/10453#issuecomment-4148905254 https://github.com/ggml-org/llama.cpp/issues/10453#issuecomm...
- OkGoDoIt 6mo agoIs that better or worse?
- irusensei 6mo agoDepends. MLX is faster because it has better integration with Apple hardware. On the other hand GGUF is a far more popular format so there will be more programs and model variety. So its kinda like having a very specific diet that you swear is better for you but you can only order food from a few restaurants.
- overfeed 6mo ago> It's just a matter of getting the performance good enough. Who will pay for the ongoing development of (near-)SoTA local models? The good open-weight models are all developed by for-profit companies - you know how that story will end.
- DrScientist 6mo agoApple via customers paying for the whole solution ( eg a laptop that can run decent local models )? I think Apple had something in the region of 143 billion in revenue in the last quarter. Not saying it will happen - just that there are a variety of business models out there and in the end it all depends on where consumers put their money.
- nikanj 6mo agoThat also means sending every user a copy of the model that you spend billions training. The current model (running the models at the vendor side) makes it much easier to protect that investment
- jl6 6mo agoNot sure about the using less electricity part. With batching, it’s more efficient to serve multiple users simultaneously.
- TeMPOraL 6mo agoIndeed. Data centers have so many ways and reasons to be much more energy-efficient than local compute it's not even funny.
- chongli 6mo agoThey do, though I don’t think they max out on energy efficient technology. It’s much easier to cut a deal for cheap electricity with a regional government, much to the chagrin of the locals (who see their power bills go up).
- ZeroGravitas 6mo agoIt feels like you'll soon need a local llm to intermediate with the remote llm, like an ad blocker for browsers to stop them injecting ads or remind you not to send corporate IP out onto the Internet.
- tomashubelbauer 6mo agoI'd like to coin the term "user agent" for this
- blitzar 6mo ago"copilot" seems a good term could also be considered a triage layer
- miki123211 6mo ago> would use less electricity Sorry to shatter your bubble, but this is patently false, LLMs are far more efficient on hardware that simultaneously serves many requests at once. There's also the (environmental and monetary) cost of producing overpowered devices that sit idle when you're not using them, in contrast to a cloud GPU, which can be rented out to whoever needs it at a given moment, potentially at a lower cost during periods of lower demand. Many LLM workloads aren't even that latency sensitive, so it's far easier to move them closer to renewable energy than to move that energy closer to you.
- kortilla 6mo agoWell this is an article about running on hardware I already have in my house. In the winter that’s just a little extra electricity that converts into “free” resistive heating.
- ysleepy 6mo agoI'm actually not sure that's true. Apart from people buying the device with or without the neural accelerator, the perf/watt could be on par or better with the big iron. The efficiency sweet-spot is usually below the peak performance point, see big.little architectures etc.
- zozbot234 6mo ago> LLMs are far more efficient on hardware that simultaneously serves many requests at once. The LLM inference itself may be more efficient (though this may be impacted by different throughput vs. latency tradeoffs; local inference makes it easier to run with higher latency) but making the hardware is not. The cost for datacenter-class hardware is orders of magnitude higher, and repurposing existing hardware is a real gain in efficiency.
- Tepix 6mo agoSeems doubtful. The utilisation will be super high for data center silicon whereas your PC or phone at home is mostly idle.
- thih9 6mo ago> it also would use less electricity How would it use less electricity? I’d like to learn more.
- jychang 6mo agoThat's completely not true. LLM on device would use MORE electricity. Service providers that do batch>1 inference are a lot more efficient per watt. Local inference can only do batch=1 inference, which is very inefficient.
- amelius 6mo agoLLM in silicon is the future. It won't be long until you can just plug an LLM chip into your computer and talk to it at 100x the speed of current LLMs. Capability will be lower but their speed will make up for it.
- theshrike79 6mo agoI'm expecting someone to come up with an LLM version of the Coral USB Accelerator: https://www.coral.ai/products/accelerator https://www.coral.ai/products/accelerator Just plug in a stick in your USB-C port or add an M.2 or PCIe board and you'll get dramatically faster AI inference.
- angoragoats 6mo agoI think there are drastic differences between computer vision models and LLMs that you’re not considering. LLMs are huge relative to vision models, and require gobs of fast memory. For this reason a little USB dongle isn’t going to cut it. Put another way, there already exist add-in boards like this, and they’re called GPUs.
- amelius 6mo agoGPUs are still software programmable. An "LLM chip" does not need that and so can be much more efficient.
- angoragoats 6mo agoSure, but that’s somewhat orthogonal to the point I was making, which is that LLMs are huge in size. Even in the case of a custom “LLM chip,” you’ll need huge amounts of very fast storage of some sort (likely DRAM), which places constraints on the size, power consumption, and cost of such a device. This device, if it existed, would not in any way resemble the Coral TPU product that the GP was referencing; I think in fact it would be closer in size, price, and form factor to a GPU.
- jillesvangurp 6mo ago
- zozbot234 6mo ago> Most users don't need frontier model performance. SSD weights offload makes it feasible to run SOTA local models on consumer or prosumer/enthusiast-class platforms, though with very low throughput (the SSD offload bandwidth is a huge bottleneck, mitigated by having a lot of RAM for caching). But if you only need SOTA performance rarely and can wait for the answer, it becomes a great option.
- iNic 6mo agoIt will probably be a future. My guess is that for many businesses it will still make sense to have more powerful models and to run them centralized in a datacenter. Also, by batching queries you can get efficiencies at scale that might be hard to replicate locally. I can also see a hybrid approach where local models get good at handing off to cloud models for complex queries.
- niek_pas 6mo ago> For many businesses it will still make sense to have more powerful models and to run them centralized in a datacenter. Agree, and I think of it this way: for a lot of businesses, it already makes sense to have a bunch of more powerful computers and run them centralized in a datacenter. Nevertheless, most people at most companies do most of their work on their Macbook Air or Dell whatever. I think LLMs will follow a similar pattern: local for 90% of use cases, powerful models (either on-site in a datacenter or via a service) for everything else.
- goldenarm 6mo agoIt's more secure, but it would make supply much much worse. Data centers use GPU batching, much higher utilisation rates, and more efficient hardware. It's borderline two order of magnitude more efficient than your desktop.
- nbenitezl 6mo agoBut when using it on the cloud a LLM can consult 50 websites, which is super fast for their datacenters as they are backbone of internet, instead you'll have to wait much more on your device to consult those websites before giving you the LLM response. Am i wrong?
- comboy 6mo agoAs things stand today even when doing research tasks, time spent by model is >> than fetching websites. I don't see it changing any time soon, except when some deals happen behind the scenes where agents get to access CF guarded resources that normally get blocked from automated access.
- Const-me 6mo agoWhile data centres indeed have awesome internet connectivity, don’t forget the bandwidth is shared by all clients using a particular server. If you have 100 mbit/sec internet connection at home, a computer in a data centre has 10 gbit/sec, but the server is serving 200 concurrent clients — your bandwidth is twice as fast.
- dwayne_dibley 6mo agoThis might be how Apple will start to see even more sales, the M series processors are so far ahead of anything else, local LLMs could be their main selling point.
- 3yr-i-frew-up 6mo ago[dead]
- konschubert 6mo agoI disagree with every sentence of this. > solves the problem of too much demand for inference False, it creates consumer demand for inference chips, which will be badly utilised. > also would use less electricity What makes you think that? (MAYBE you can save power on cooling. But not if the data center is close to a natural heat sink) > It's just a matter of getting the performance good enough. The performance limitations are inherent to the limited compute and memory. > Most users don't need frontier model performance. What makes you think that?
- deleted 6mo ago[deleted]
- ekianjo 6mo ago> What makes you think that? Looking at actual users of LLMs
- konschubert 6mo agoWhile not everybody is a professional in YOUR domain, many people are professionals in SOME domain. And even outside of that, they deserve a smart conversation partner, for example on topics like health and politics.
- locknitpicker 6mo ago> What makes you think that? The fact that today's and yesterday's models are quite capable of handling mundane tasks, and even companies behind frontier models are investing heavily in strategies to manage context instead of blindly plowing through problems with brute-force generalist models. But let's flip this around: what on earth even suggests to you that most users need frontier models?
- konschubert 6mo agoEverybody has difficult decisions to make in their daily lives and in their work. Having access to a model that is drawing from good sources and takes time to think instead of hallucinating a response is important in many domains of life.
- g947o 6mo agoHave you spent more than 10 min actually running LLM on a local machine? As it stands today, local LLMs don't work remotely as well as some people try to picture them, in almost every way -- speed, performance, cost, usability etc. The only upside is privacy.
- RALaBarge 6mo agoI agree with you in the sense that if you tried to take any model right now and cram it into an iphone, it wouldnt be a claude-level agent. I run 32b agents locally on a big video card, and smaller ones in CPU, but the lack there isn't the logic or reasoning, it is the chain of tooling that Claude Code and other stacks have built in. Doing a lot of testing recently with my own harness, you would not believe the quality improvement you can get from a smaller LLM with really good opening context. Even Microsoft is working on 1-bit LLMs...it sucks right now, but what about in 5 years? But the OP is correct -- everything will have an LLM on it eventually, much sooner than people who do not understand what is going on right now would ever believe is possible.
- kylehotchkiss 6mo agoYes. I've spent months running Qwen2.5-8B on my barebones 16gb ram M4 Mac mini to handle identifying sites from google search results. It has been rock solid. I'm not even running this MLX-powered improvement on it yet. Your idea of what people need from Local LLMs and others are different. Not everybody needs a /r/myboyfriendisai level performance.
- g947o 6mo agoYou probably want to double check the comment I was responding to.
- adam_patarino 6mo ago[flagged]
- podlp 6mo agoRig sounds cool, I just joined the waitlist! I’m building something similar although with a much narrower purpose. Excited to learn more
- adam_patarino 6mo agoTell me more! Thanks for the waitlist
- podlp 6mo agoSent a LinkedIn request. I’m building a language-specific coding agent using Apple Intelligence with custom adapters. It’s more a proof-of-concept at this point, but basic functionality actually works! The 4K context window is brutal, but there’s a variety of techniques to work around it. Tighter feedback loops, linters, LSPs, and other tools to vet generated code. Plus mechanisms for on-device or web-based API discovery. My hypothesis is if all this can work “well enough” for one language/ runtime, it could be adapted for N languages/ runtimes.
- eeixlk 6mo agoObviously apple would prefer this. It would boost demand for more powerful and expensive devices, and align with their privacy marketing. But they have massively fumbled with siri for a long time and then missed huge deadlines with ai promises. Despite having billions, they have shown no competency in delivering services or accurately marketing what to expect from ai features.
- jonhohle 6mo agoI’ve been using google search AI and Gemini, which I find generally pretty good. In the past week, Gemini and Search AI have been bringing in various details of previous searches I’ve done and Search AI conversations I’ve had and it’s extremely gross and creepy. I was looking for details about cars and it started interjecting how the safety would affect my children by name in a conversation where I never mention my children. I was asking details about Thunderbolt and modern Ryzen processors and a fresh Gemini chat brought in details about a completely unrelated project I work on. I’ve always thought local LLMs would be important, but whatever Google did in the past few weeks has made that even more clear.
- theChaparral 6mo agoIt's Personal Intelligence in the Gemini settings. I just turned that off last night when it was doing similar things.
- Aurornis 6mo ago> solves the problem of too much demand for inference compared to data center supply Maybe in the distant future when device compute capacity has increased by multiples and efficiency improvements have made smaller LLMs better. The current data center buildouts are using GPU clusters and hybrid compute servers that are so much more powerful than anything you can run at home that they’re not in the same league. Even among the open models that you can run at home if you’re willing to spend $40K on hardware, the prefill and token generation speeds are so slow compared to SOTA served models that you really have to be dedicated to avoiding the cloud to run these. We won’t be in a data center crunch forever. I would not be surprised if we have a period of data center oversupply after this rush to build out capacity. However at the current rate of progress I don’t see local compute catching up to hosted models in quality and usability (speed) before data center capacity catches up to demand. This is coming from someone who spends more than is reasonable on local compute hardware.
- babblingfish 6mo agoI see a lot of people are confused about the electricity claim so I'll elaborate on it more. The assumption I'm making here is that on device people will run smaller models, that can fit on their machines without needing to buy new computers. If everyone ran inference on their machine there would be no need for these massive datacenters which use huge quantities of electricity. It would utilize the machines they already have and the electricity they're already using. People are making a comparison of the cost per inference or token or whatever and saying datacenters are more efficient which makes obvious sense. What i'm saying is if we eliminate the need for building out dozens of gigawatt datacenters completely then we would use less electricity. I feel like this makes intuitive sense. People are getting lost in the details about cost per inference, and performance on different models.