- text-to-speech - speech-to-text - dictionary - encyclopedia - help troubleshooting errors - generate common recipes and nutritional facts - proofread emails, blog posts - search a large trove of documents, find information, summarize it (RAG) - manipulate your terminal/browser/etc - analyze a picture or video - generate a picture or video - generate PDFs, documents, etc (code exec) - simple programming - financial analysis/planning - math and science analysis - find simple first aid/medical information - "rubber ducking" but the duck talks back

A quarter of those don't need more than a gig of RAM, the rest benefit from more RAM. Technically you don't even need a GPU, it just makes it faster. I do half that stuff on my laptop with local models every day.

That said, it really doesn't need to be local. I like the idea that I can do all that stuff offline if I'm traveling, but I usually have cell service, and the total tokens is pretty cheap (like $2/month for all my non-coding AI use).

Until then, I'm going to keep sending my JSON to the server farm in Virginia because it's the only place that can serve me a model that actually works for my uses.

1- Do a particular task with great capability (due to its constrained, limited scope) 2- Do it in such a way, it integrates gracefully in your workflow without ever requiring you to know you are using an LM.

There is a difference between outsourcing your workflow to AI and actually utilizing it.

Check this: https://www.distillabs.ai/blog/we-benchmarked-12-small-langu...

I think the future will probably be a hybrid of:

1. local AI for simple, private, everyday tasks

2. online AI for very hard or long tasks

I haven't seen a text-based model sharing site spring up yet (perhaps they already have and I don't know about it yet). Civitai, being focused on image-generation, has the obvious advantage that it's easy to show off impressive results from the model on the front page of the website, and judging what someone's home-grown fine-tuned LLM will produce is a lot harder. But at some point I expect a Civitai equivalent site for text models, especially code-based ones, to become popular. That will seriously undercut Anthropic, OpenAI, et al, and will probably force them to find a price equilibrium.

Because once you're competing with "I spend $2,500 up front on a powerful video card, download an open-source model for free, and then I get pretty much everything I need for free" (additional power cost of running that video card isn't nothing, but probably not noticeable in your power bill compared to what you're already using)... then suddenly $200/month means your customers are thinking "after one year I would have been better off with the homegrown solution". The only way they'll continue to pay $200/month is if Claude/GPT/Gemini/whoever is truly head-and-shoulders above the "pay upfront once for hardware then use it for free afterwards" models available. And that's going to be doable, perhaps, but tough.

The dependency we have with anthropic and openai for coding for instance is insane. Most accept it because either they don't care, or they just hope chinese will never stop open weights. The business model of open weights is very new, include some power play between countries and labs, and move an absurd amount of money without any concrete oversight from most people.

It's a very dangerous gamble. Today incredible value is available for nearly everyone. But it may stop without any warning, for reason outside our control.

informatics aren't magic, you'll never be able to compress """knowledge""" into a small model in a way equivalent to the 1.5 TB model

A self hosted inference solution that offer good tenant isolation guarantees (ideally zero trust) and is easy enough to deploy and maintain (think Plex for AI) would be my choice for privacy. Now to be honest I have done zero research about this and have zero idea how feasible that is, maybe it already exists and there's some discord servers I should join?

Edit: I don't need to mention it here but what's incredible is that open models are in the ballpark of the best commercial models so supposedly, the hardest part by far is already solved.

I agree local models are great, and it’s cool that Apple has models built in now. But I feel like it basically has to be an OS level feature or users are going to get upset. I’d certainly rather have a small utility call out to OpenAI than download its own model.

[1]: https://news.ycombinator.com/item?id=48019219

- Self hosting is expensive. It involves expensive machines with GPUs that cost hundreds per month if you use cloud based ones. You might need multiple of those. And you need people to mind those machines and they are even more expensive per month.

- If you run stuff on your laptop, it consumes a lot of resources and energy. I have qwen running on my laptop. Even minimal usage turns my laptop in a radiator. Nice as a demo, but I can't have it this hot all the time. It would run out of battery, and it's probably not great for longevity of components in the laptop.

- Models are evolving quickly and the self hosted smaller ones aren't as good when it comes to things like tool usage, reasoning, etc. Being able to switch tot he latest model is valuable.

- It's easier to get your use case working with one of the top models than with one of the smaller self hosted ones.

- If you get the wrong hardware, it might not be able to run the latest models very soon.

- Self hosting models is mostly a cost optimization. It only becomes relevant if you hit a certain scale.

- You have alternatives in the form of hosted models via a wide range of service providers. Some of those are EU based and offer all the things you'd be looking for if you are offering your services there. Including legal requirements.

- Reinventing what these companies do in house is technically challenging and possibly more expensive than self hosting models because now you need a lot of engineering capacity dedicated to that. And legal. And all the rest.

If, like most companies/people, you are at the experimenting stage, the cheapest and fastest is just getting an API key from an API provider of your choice. You can take it from there if your experiment actually works. And then it's mostly about optimizing cost. If your API usage goes to the thousands per month or worse, it becomes a cost/quality trade off.

The additional up-front cost for hardware designed to run an LLM in addition to normal workload is unlikely to be accepted by most consumers.

The scale will be very constrained (like Apples on-device models which are small, heavily quantized, and have a small 4K token context window). It’s also terrible for battery life.

AI as it is implemented today is simply just computationally expensive and unless you put in dedicated hardware (like the ANE) for only this purpose - a large cost driver - I don’t really see it getting large scale adoption.

Companies will probably need a server-backed solution as fallback if they want reasonable user experience, so why even invest in diverse hardware support.

Now today, AI is very expensive and not readily accessible to most people without paying a good amount.

The early internet became now you can just get a free phone from phone companies so long as you get their extras. Then you get a ton of subscriptions and ad-ons, but you don’t have to spend money, could just use youtube with ads etc.

Local AI would similarly shift this dynamic to paying for access to plug-in’s and tools for your local AI to be able to use. Like how the subscription model works right now.

With local model advancements, such as specifically Qwen 3.6 35B A3B, this future is becoming more likely by the year IMO.

TFA is focused on whether big models are necessary for what users want. There's some evidence they may never actually be reliable enough unless a) mechanistic interpretation matures far enough or b) our multi-agent systems all become multi-model.

For (a), advancement in MI might fix problems with big models, but would also mean we can maybe get unified representations, and just slice and dice the useful stuff out of huge models, getting only what we need without the junk. Ability to isolate problems won't really come without bringing the ability to isolate functional subsystems. Only want logic? Only vision? Just cut it out of the big monster and enjoy reduced costs and surface area for problems.

For (b), just look at stuff like the evil vector, or the category of hallucinations specific to tool-use. Without a complete solution for helpful/honest/harmless alignment, it seems likely that creativity and rigor (and many other things) are fundamentally at odds. If you start to need many models for everything anyway, why do we need the huge expensive do-everything ones? So specialization also becomes a pressure to shrink everything towards minimal reliable experts

They need to be able to do a small task well and they need to be able to run reasonably on consumer-class devices. Even better if they can run on mobile phones.

In my experiments with local LLMs I noticed that while increasing the size of the model is nice the real thing that turns a barely useless model into something useful is the ability to use tools. Giving my models the ability to search the web and fetch web pages did way more to solve hallucinations than getting a bigger model. And it doesn't have a training cutoff. Sure, the bigger model is probably better at using tools but I often find the smaller models to be good enough.

Assuming we end up in a future where people pay to run multiple smaller models on their machines for specific tasks (e.g. A summariser model, a python coding model, or however fine grained/macro you want to go), the people training those models will need to turn a profit.

So how much will that cost? And how often will consumers have to pay? Models have a very short self life. Say you have a dedicated python coding model - that needs re-training every time there's a significant update to the language itself, any popular packages, related technologies (e.g. servers, cloud infra etc). So how often will users need to "upgrade" to the lastest version? It's going to be "frequently".

And it still needs the language stuff on top of that. Users aren't going to interact with a python coding model by writing python. They're going to use natural language. So the model needs all that stuff. And they're going to give it problems to solve. What if you asked the model "Write me a Bezier curve function". It needs to know about bezier curves, which have nothing to do with Python. So where do these LLM providers draw the line on what makes it into the training data and what doesn't?

And if an LLM doesn't know what a Bezier curve is, that's not going to stop it from just hallucinating an answer. If a significat proportion of prompts resulted in a response that said "Sorry, I don't know what you're talking about", then people will just stop using it. The utility of these things will be quickly overshadowed by the frustrations.

The way these frontier models have been introduced and promoted has set unrealistic expectations, and there's no putting the genie back in the bottle.

On the other hand… v4 flash model is actual magic compared to what was available 2 years ago. If the rate of improvement stays as is, we’ll get a similar performance in a ~120B model in a year, which is viable (if expensive) for everyman hardware. Possibly you’ll be able to run its equivalent on a ~$1200 laptop by 2028, which for me-in-2020 would sound straight out of a scifi movie. A good harness that lets the model fetch data from other sources like a local wikipedia copy from kiwix could do a lot for factual knowledge, too; there’s only so much you can encode in the model itself, but even a cheapish (pre-curent prices) 2TB drive can hold an immense amount of LLM-accessible data.

Big caveat: I don’t see local models for programming or generally demanding agentic tasks being worth it anytime soon. You likely want bleeding edge models for it, and speed is far more important. Chat at 20tok/s is fine; working on even a small codebase at 20tok/s, especially on a noticeably weaker model, is just a waste of time. Maybe it’s a PEBKAC but I have no idea how people make any meaningful use out of qwen 3.6.

As OP says, it shines in constrained environments where the model is transforming user-owned data. Definitely less useful for anything more open-ended.

This has been the case for way longer than openAI and Anthropic has been around with services like AWS, Cloudflare, etc.

I consider it to be very careless to entrust your emails, your chats, your calendar, your notes, your calls, your pictures, your contacts, your location history, your waking hours, your files, your TODO list, i.e. stuff including your health data to the for-profit AI companies. The temptation to earn money with your data is just too great, plus the risk of the data being stolen and sold illegally.

Local AI should be the default. For everone who can't do local AI, we need confidential compute. Yes, it has been hacked before. But it's making it a lot harder.

Oh yeah , it feels independent and not lazy , sure

The problem is that it's much easier to use the SOTA models (especially if they are subsidized) instead of spending time fixing the knobs with the local one.

I just realized this with coding agents, yeah, you probably shouldn't always use latest version at xhigh, but you will end doing it because you do the job in less time, with less "effort" and basically at the same price.

I guess we'll see a real effort for local AI only when major vendors will start billing based on actual token usage.

A smaller cheaper local model can delivery most the value for coding, while we still use some services for code review and security compliance.

Once the VC money runs out and they start to charge the real price, the C-level will have to impose budges or limits. The current pissing contest over who can expend the most tokens is both ridiculous and shortsighted

Right now it feels like we have all the pieces but nobody integrating all that into an amazing experience.

Based on what I understand about how the former works, I would assume that the latter has the same properties and failure modes.

Damned if they do, damned if they don't.

This is what makes me continuously doubt and rewrite the local-first approach to inline chat in my editor. Next edit/ code complete makes more sense due to latency advantage. But chat is hard.

It's fast and feels good to run locally, but output quality is just not ChatGPT etal.

You don't have any guarantees in terms of data, that's true, you rely on the provider. But this is similar to a database or other services where you don't have the knowledge or resources to run them yourself. Hardware cost is an additional factor here.

If on the other hand your idea works out and the model fits the use case, you can always decide to move to a dedicated infrastructure later.

All of this being said, it seems Claude gave up this "constitution" it used to train on? I remember trying to get it to help me code some video editing tools, and it was convinced I was pirating videos and so wouldn't help me anymore in that session.

I tried Cline and couldn't get it working well and part of this was that at the time it expected OpenAIs output format.

This is why I believe OAI and Anthropic I’ve been so aggressive at offering services outside of their pure models like Claude Design. This is what will be competitive and keeping people subscribed.

- and for the web / javascript / svelte applications?

- suggestions for local OCR for bulk images?

Small models are still in their infancy, and there's still much to sort out about and around them, as well

Is there a solution for this? I'm currently just making users download onnx models if they want a feature, but it's not smooth UX

* What is the answer to local AI for native apps on Windows?

* What is the answer to local AI for Linux?

This is a big opportunity for Linux, given the high quality of open-weight models. I hope some answer emerges before designs fracture and we get a dozen mutually incompatible answers.

I think the Quixotic accelerationists of AI are more or less a vocal minority of the people who make software, and the choice of online APIs over local systems is largely a choice made for users, rather than developer’s laziness.

You can do more and better with private AI today than with local models. There is no getting around that. Even if local AIs get better, being on the cutting edge of LLM performance is often a very worthy investment.

Most people won’t settle for a product if it’s not the very best and incredibly convenient. That’s a high bar, and local AI often doesn’t meet those standards.

HN’s insistence on treating all users like they are open-source, privacy-first, self-hosted Linux fanatics is painfully corny.

The goal is that you would assign roles to models based on tasks, capabilities and observed performance. The router would then take care of model selection in the background.

It's tricky though. Probably have another two weeks before I can release the runtime.

I have a preview up at https://role-model.dev/

You can follow me on Twitter if you want updates (see profile)

We are at least 5 years away from that. And DRAM needs a substantial breakthrough in cost reduction.

``` harbor pull unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_XL

# Open WebUI -> llama.cpp + SearXNG for Web RAG + OpenTerminal as sandbox harbor up searxng webui llamacpp openterminal ```

That's it, it's already better than Claude's or ChatGPT's app.

A useful framing over “local vs cloud AI” can be split along two axes: does the task touch private data, and does it need frontier intelligence? You can use frontier models for developing the software (doesn’t touch data), but open-source models running locally for ops: maintenance, debugging and monitoring (touches data). If you need to fall back to frontier intelligence at some point for a particularly hard to resolve problem, you can still rely on local models for pre-transforming and filtering input in a way that's privacy-preserving or satisfies some constraint before it’s sent off to the cloud for processing. OpenAI's privacy filter is a good example of a model that can be used to mask PII and secrets and that can run locally: https://openai.com/index/introducing-openai-privacy-filter/, before sending any data externally for processing.

Another framing for local vs frontier closed which the article mentions is whether the task saturates model capability. With certain tasks like PDF processing or voice or summarization, adding more intelligence isn't necessarily useful. Arguably we've approached that point for chat interfaces already with frontier open-source models. But for coding and ops through well structured tool use inside a coding capable harness, we're still a ways away.

Tangentially, a contrarian take here is that AI can actually enable more privacy preserving software if you’re so inclined. You can just build personalized software and it lowers the barrier to entry and the effort required to self host. SaaS complexity often comes from scaling and supporting features for all types of customers, and if you're building software for personal use, you don't need all that additional complexity. Additionally, foundational and infra software that is harder to vibecode with AI is often already open source.

Well there’s your problem, control needs to go the other way. If you want your app to be AI-enabled, you need to make it easy for AI to control your app. Have you used OpenClaw? It’s awesome!

Isn’t this true of any application that accesses anything not running on your computer? This is just describing what it means to add an API call to your app. Nothing to do with AI (?)

Dont quite think its ready yet.

proceeds to brutalise the reader with an 88-point headline font.

Work? I don't want it local at all. I want it all cloud agent.

If we could even get something like GPT 5.5 running locally that would be quite useful.

It would be nice if model makers could at minimum embrace test harnesses, and stretch goal if they’re going to change underlying formats then at least land compatible readers in the big engines (e.g. llama.cpp and vllm)

Welcome back to 2014. Let us now continue yelling at the cloud.

1. Local models are likely to be more power-expensive to run (per-"unit-of-intelligence") than remote models, due to datacenter economies of scale. People do not like to engage with this point, but if you have environmental concerns about AI, this is a pretty important one.

2. Using dumb models for simple tasks seems like a good idea, but it ends up being pretty clear pretty quick that you just want the smartest model you can afford for absolutely every task.

When I say 'moat' I don't mean moat specific to a company vis-a-vis other companies, but 'moat' specific to the set of inference providers vis-a-vis self-hosted local inference.

The moat consists primarily of being able to batch inference requests.

If we pretend people weren't interested in long context-lengths, there would be a moat for inference providers. who can batch many requests so that streaming the model weights (regardless if from system RAM to GPU RAM; or from GPU RAM to GPU cache SRAM) can be amortized over multiple requests.

However people do want longer memory than the native context length.

One approach is continual learning (basically continue training by using the past conversation as extra corpus material; interspersed with training on continuations from the frozen model, so it doesn't drift or catastrophically forget knowledge / politeness / ...).

However this is very expensive for inference providers, since they would have to multiply model weight storage with the number of users U=N. For a single user the memory cost of continual learning is much less since they only need to support a single user, and are returned some of the memory cost through elimination of KV-caches, and returned higher quality answers compared to subquadratic approximations of quadratic attention.

An advantage of continual learning is that the conversation / code base / context is continuously rebaked into model weights, and so doesn't need KV caches! It doesn't need imperfect approximations to quadratic attention, it attends through working knowledge being updated.

Nothing prevents local LLM users from implementing this and benefiting from the dropped requirements of KV caches and enjoying true quadratic attention implicitly over the whole codebase, or many overlapping projects indeed.

The only remaining moat of inference providers vis-a-vis continual learning local LLM's is the batching advantage, plus the gradient update costs for continual learning minus the KV storage and compute costs, minus the performance loss due to inexact approximations to quadratic attention.

This points towards a stronger incentive for local hosting than currently realized (none of the popular local LLM tools currently support continual learning, once this genie is out of the bottle it will be a permanent decrease of the inference provider moat, the cost of which can't be expressed merely in hardware or energy costs, since it is difficult to quantify the financial loss of inexact approximations to quadratic attention, the financial loss due to limited effective context length and the concomitant loss in quality of the result)

And local inference requires fairly beefy hardware, that is FAR from ubiquitous across today's userbases. Local models are also still far dumber than what frontier labs can serve.

Weird that this is getting such a tidal wave of upvotes.

NVidia segments the market by limiting the amount of memory on GPUs. It currently tops out at 32GB (on a 5090) but it has excellent memory bandwidth (~1.8TB/s). If you want more than the you need to buy an RTX Pro (eg RTX 6000 Pro w/ 96GB for ~$10K) or you get into high high end solutions like H100, H200, etc that have significantly more memory and even higher bandwidth on HBM memory (eg 3.2TB/s+).

NVidia has released the DGX Spark w/ 128GB of memory for ~$4k. The problem is the memory bandwidth. It's only 273GB/s, which is less than the M5 Pro (307GB/s) but more than the M5. You can buy a 16" Macbook Pro with an M5 Max and 128GB of memory for $6k and it has a bandwidth of 614GB/s. So the DGX Spark is a joke, really.

In case it wasn't clear, Apple is interesting in this space because it has a shared memory architecture so the GPU can use all the memory.

Many, myself include, expect there to be no refresh to the 5000 series consumer GPUs this year, which would otherwise happen based on product cycles. So no 5080 Super, for example. And I wouldn't expect a 6090 before 2028 realistically.

One thing Apple hasn't done yet is release the M5 Mac Studios, which are widely expected in Q3 this year. They are interesting because, for example, the M3 Ultra has a memory bandwidth of 819GB/s and previously had a max spec of 512GB but that got discontinued (and the 256GB version also got discontinued more recently).

So many expect an M5 Max Mac Studio with 1TB/s+ bandwidth and specs up to 256GB or 512GB, probably for ~$10k later this year.

You really have to use this hardware almost 24x7 for it to be economical because otherwise H100 computer hours are probably cheaper.

But what happens when the next generation of GPUs comes out to the trillions in AI DC investment? It's going to halve its value. That's over $1 trillion in capex that will disappear overnight, effectively.

I think Apple is the dark horse here because they have no interest in NVidia's psuedo-monopoly. I'm just waiting for them to realize it.

Now CUDA is an issue here still but I think as time goes on it's going to be less of an issue. Memory is still a huge constraint both in terms of price and just general supply because NVidia can justify paying way more for it than you can, probably.

It's still sad to see that 128GB (2x64GB) DDR5 kits are almost $2k now and werre $400 a year ago. Expect that to continue until this bubble pops (which IMHO it will) and we're likely in a global recession.

So the other issue is models. OpenAI and Anthropic are built on proprietary models. Their entire valuation depends on this moat. I don't think this last so both companies are doomed because open source models are going to be sufficiently good.

We can already do some reasonably cool stuff on local hardware that isn't that expensive and even more so once you get to $5-10k hardware. That's going to be so much better in 2 years that I'm hesitant to spend any amount of money now.

Plus the code for running these things is getting better. Just in the last month there have been huge speed ups in local LLMs with MTP.

I have to conclude that people would like to have powerful local AI but it should at the same time only be a tiny model. In which case it wouldn't be powerful.

Local models need to be resident in expensive RAM, the kind that has fat pipes to compute. And if you have a local app, how do you take a dependency on whatever random model is installed? Does it support your tool calling complexity? Does it have multimodal input? Does it support system messages in the middle of the conversation or not? Is it dumb enough to need reminders all the time?

Spend enough time building against local models and you'll see they're jagged in performance. You need to tune context size, trade off system message complexity with progressive disclosure. You simply can't rely on intelligence. A bunch of work goes into the harness.

Meanwhile, third party inference is getting the benefits of scale. You only need to rent a timeslice of memory and compute. It's consistent and everybody gets the same experience. And yes, it needs paying for, but the economics are just better.