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coffeecoders
giancarlostoro
I feel like Apple needs a new CEO, I've felt this way for a long time. If I had been in charge of Apple I would have embraced local LLMs and built an inference engine that optimizes models that are designed for Nvidia, I also would have probably toyed around with the idea of selling server-grade Apple Silicon processors and opening up the GPU spec so people can build against it. Seems like Apple tries to play it too safe. While Tim Cook is good as a COO, he's still running Apple as a COO. They need a man of vision, not a COO at the helm.
aurareturn
I think if Cook had vision, he could have started something called Apple Enterprise and sold Apple Silicon as a server and made AI chips. I agree he’s too conservative and has no product vision. Great manager though.
seanmcdirmid
I was pleasantly surprised Apple Silicon came out at all. Someone has their eye on long term vision at Apple at least, they just didn't do this on a whim.
mrexroad
They did have Xserve back in the day. As great as Apple silicon is for running local llms along with being a general-purpose computing device, it’s not clear that Apple silicon have enough of a differentiating advantage over a rack of nvidia gpus to make it worthwhile in enterprise…
nxobject
I think that would spread Apple’s chip team too thinly between competing priorities - and require them to do E2E stuff they’d never be interested in doing. What’s always happened, even during Jobs, is that Apple would do something nice and backend-y, and then not be able to keep it up as they’d pour resources into some consumer product. (See: WebObjects, Xserve, Mac OS Server.)
alt227
Apple silicon does not compete well in multicore spaces. People seem to think that because it can run single core things really well on a laptop, it can do anything. Servers regularly have 100-200 cpu cores maxing out of rapid fire threads. This is not what Apple silicon excels at.
On top of that, it only performs so well on consumer devices because they control the hardware and OS and can tune both together. Creating server hardware would mean allowing linux to be installed on it, and would need to run equally well. Apple would never put the development time into linux kernel/drivers to make this happen.
brookst
Is expansion to all possible markets really a sign of product vision? Windows is in everything from ATMs to servers to cheap laptops, and I am not sure it’s a better product for it OR that Microsoft makes more money that way. Certainly the support burden for a huge number of long tail applications is huge.
And I suppose we’re giving credit to other people for Watch, AirPods, Vision Pro?
giancarlostoro
It doesn't just end with AI, but it seems the most blatant. At a bare minimum, he could assign someone to fulfill that vision for AI. Google has their own chips which they scale. Apple doesn't need to rebuild ChatGPT, but they could very much do what Microsoft does with Phi and provide Apple Silicon trained and optimized base models for all their users. It seems they are already doing something for XCode and Swift, but they're just barely scratching the surface.
I remember when the iPhone X became a thing, it was because consumers were extremely underwhelmed by Apple at the time. It's like they kicked it up less than a notch sadly.
If Tim Cook decided to be a little more of a visionary, I would say keep him. I would at least prefer he would delegate someone to do the visionary work, he will eventually need a successor.
alexashka
[flagged]
jbverschoor
Local llm.. everybody is scared of privacy.. many people don’t want to buy subscriptions (still).
Just sell a proper HomePod with 64GB-128GB ram, which handles everything including your personal LLM, Time Machine if needed, back to Mac (Tailscale/zerotier)
+ they can compete efficiently with the other. Cloud providers.
brookst
It’s a mistake to generalize from the HN population.
Most people don’t care about privacy (see: success of Facebook and TikTok). Most people don’t care about subscriptions (see: cable TV, Netflix).
There may be a niche market for a local inference device that costs $1000 and has to be replaced every year or two during the early days of AI, but it’s not a market with decent ROI for Apple.
bigyabai
> Just sell a proper HomePod with 64GB-128GB ram
The same Homepod that almost sold as poorly as Vision Pro despite a $349.99 MSRP? Apple charges $400 to upgrade an M4 to 64GB and a whopping $1,200 for the 128GB upgrade.
The consumer demand for a $800+ device like this is probably zilch, I can't imagine it's worth Apple's time to gussy up a nice UX or support it long-term. What you are describing is a Mac with extra steps, you could probably hack together a similar experience with Shortcuts if you had enough money and a use-case. An AI Homepod-server would only be efficient at wasting money.
VagabundoP
Have a team pushing out opitmised open source models. Over time this thing could become the house AI. Basically Star Treks computer.
ako
They have local LLMs, apple foundation models: https://developer.apple.com/documentation/FoundationModels
andruby
Apple often wants to do it their way. Unfortunately, their foundation models are way behind even the open models.
_delirium
There are local LLM coding models that ship with XCode now too.
jbs789
Sounds like you’ve got a solid handle on things - go do it!
giancarlostoro
Give me a majority share in AAPL if that's what you want ;)
elAhmo
I think shareholders are fine with Tim Cook as a CEO.
utyop22
I sometimes read posts on here and just laugh.
Its easy to sit in the armchair and say "just be a visionary bro" when they forget Tim worked under Steve for awhile before his death - he has some sense and understanding of what it takes to get a great product out of the door.
Nvidia is generating a lot of revenue, sure - but what is the downstream impact on its customers with the hardware? All they have right now is negative returns to show for their spending. Could this change? Maybe. Is it likely? Not in my view.
As it stands, Apple has made the absolute right choice in not wasting its cash and is demonstrating discipline. Which when all this LLM mania quietens, shareholders will respect.
spease
Yes. And everyone is glossing over the benefit of unified memory for LLM applications. Apple may not have the models, but it has customer goodwill, a platform, and the logistical infrastructure to roll them out. It probably even has the cash to buy some AI companies outright; maybe not the big ones (for a reasonable amount, anyway) but small to midsize ones with domain-specific models that could be combined.
Not to mention the “default browser” leverage it has with with iPhones, iPods, and watches.
woooooo
Not to mention, build a car with all that cash they have. Xiaomi makes awesome cars, Apple branded electric could scoop all the brand equity that Elon passed away.
saagarjha
One does not simply put a 5090 into an existing chip.
giancarlostoro
Not what I am suggesting. However, having trained a few different things on a modest M4 Pro chip (so not even their absolute most powerful chips mind you), and using it for local-first AI inference, I can see the value. A single server could serve an LLM for a small business and cost a lot less than running the same inference through a 5090 in terms of power usage.
I could also see universities giving this type of compute access to students for cheaper to work on more basic less resource intensive models.
__loam
I'm glad Tim is the CEO instead of you.
jasonvorhe
Why? This is something that plays into all of Apple's supposed strengths: Privacy/no strict cloud dependency/on-device compute, hardware/software optimization while owning the stack and combine that with good UI/UX for a broad target audience without sacrificing too much for the power users. OP never said that local AI would be the only topic a new CEO should focus on.
zozbot234
From reverse engineered information (in the context of Asahi Linux, which can have raw hardware access to the ANE) it seems that the M1/M2 Apple Neural Engine provides exclusively for statically scheduled MADD's of INT8 or FP16 values.[0] This wastes a lot of memory bandwidth on padding in the context of newer local models which generally are more heavily quantized.
(That is, when in-memory model values must be padded to FP16/INT8 this slashes your effective use of memory bandwidth, which is what determines token generation speed. GPU compute doesn't have that issue; one can simply de-quantize/pad the input in fast local registers to feed the matrix compute units, so memory bandwidth is used efficiently.)
The NPU/ANE is still potentially useful for lowering power use in the context of prompt pre-processing, which is limited by raw compute as opposed to the memory bandwidth bound of token generation. (Lower power usage in this context will save on battery and may help performance by avoiding power/thermal throttling, especially on passively-cooled laptops. So this is definitely worth going for.)
[0] Some historical information about bare-metal use of the ANE is available from the Whisper.cpp pull req: https://github.com/ggml-org/whisper.cpp/pull/1021 Even older information at: https://github.com/eiln/ane/tree/33a61249d773f8f50c02ab0b9fe... .
More extensive information at https://github.com/tinygrad/tinygrad/tree/master/extra/accel... (from the Tinygrad folks) seems to basically confirm the above.
(The jury is still out for M3/M4 which currently have no Asahi support - thus, no current prospects for driving the ANE bare-metal. Note however that the M3/Pro/Max ANE reported performance numbers are quite close to the M2 version, so there may not be a real improvement there either. M3 Ultra and especially the M4 series may be a different story.)
slacka
I too found that interesting that Apple's Neural Engine doesn't work with local LLMs. Seems like Apple, AMD, and Intel are missing the AI boat by not properly supporting their NPUs in llama.cpp. Any thoughts on why this is?
bigyabai
Most NPUs are almost universally too weak to use for serious LLM inference. Most of the time you get better performance-per-watt out of GPU compute shaders, the majority of NPUs are dark silicon.
Keep in mind - Nvidia has no NPU hardware because that functionality is baked-into their GPU architecture. AMD, Apple and Intel are all in this awkward NPU boat because they wanted to avoid competition with Nvidia and continue shipping simple raster designs.
aurareturn
Apple is in this NPU boat because they are optimized for mobile first.
Nvidia does not optimize for mobile first.
AMD and Intel were forced by Microsoft to add NPUs in order to sell “AI PCs”. Turns out the kind of AI that people want to run locally can’t run on an NPU. It’s too weak like you said.
AMD and Intel both have matmul acceleration directly in their GPUs. Only Apple does not.
numpad0
Perhaps due to sizes? AI/NN models before LLM were magnitudes smaller, as evident in effectively all LLMs carrying "Large" in its name regardless of relative size differences.
Someone
I guess that hardware doesn’t make things faster (¿yet?). If so I guess they would have mentioned it in https://machinelearning.apple.com/research/core-ml-on-device.... That is updated for Sequoia and says
“This technical post details how to optimize and deploy an LLM to Apple silicon, achieving the performance required for real time use cases. In this example we use Llama-3.1-8B-Instruct, a popular mid-size LLM, and we show how using Apple’s Core ML framework and the optimizations described here, this model can be run locally on a Mac with M1 Max with about ~33 tokens/s decoding speed. While this post focuses on a particular Llama model, the principles outlined here apply generally to other transformer-based LLMs of different sizes.”
cma
If it uses a lot less power it could still be a win for some use cases, like while on battery you might still want to run transformer based speech to text, RTX voice-like microphone denoising, image generation/infill in photo editing programs. In some use cases like RTX-voice like stuff during multiplayer gaming, you might want the GPU free to run the game even if it still suffers some memory bandwidth impact from having it running.
GeekyBear
There is no NPU "standard".
Llama.cpp would have to target every hardware vendor's NPU individually and those NPUs tend to have breaking changes when newer generations of hardware are released.
Even Nvidia GPUs often have breaking changes moving from one generation to the next.
montebicyclelo
I think OP is suggesting that Apple / AMD / Intel do the work of integrating their NPUs into popular libraries like `llama.cpp`. Which might make sense. My impression is that by the time the vendors support a certain model with their NPUs the model is too old and nobody cares anyway. Whereas llama.cpp keeps up with the latest and greatest.
svachalek
I think I saw something that got Ollama to run models on it? But it only works with tiny models. Seems like the neural engine is extremely power efficient but not fast enough to do LLMs with billions of parameters.
reddit_clone
I am running Ollama with 'SimonPu/Qwen3-Coder:30B-Instruct_Q4_K_XL' on a M4 pro MBP with 48 GB of memory.
From Emacs/gptel, it seems pretty fast.
I have never used the proper hosted LLMS, so I don't have a direct comparison. But the above LLM answered coding questions in a handful of seconds.
The cost of memory (and disk) upgrades in apple machines is exorbitant.
undefined
GeekyBear
> Hopefully Apple optimizes Core ML to map transformer workloads to the ANE.
If you want to convert models to run on the ANE there are tools provided:
> Convert models from TensorFlow, PyTorch, and other libraries to Core ML.
ls-a
I thought Apple MLX can do that if you convert your model using it https://mlx-framework.org/
woadwarrior01
MLX does not support the ANE.
GeekyBear
It does indeed, and is more modern than Core ML.
coffeecoders
It is less about conversion and more about extending ANE support for transformer-style models or giving developers more control.
The issue is in targeting specific hardware blocks. When you convert with coremltools, Core ML takes over and doesn't provide fine-grained control - run on GPU, CPU or ANE. Also, ANE isn't really designed with transformers in mind, so most LLM inference defaults to GPU.
aurareturn
Neural Engine is optimized for power efficiency, not performance.
Look for Apple to add matmul acceleration into the GPU instead. Thats how to truly speed up local LLMs.
ai-christianson
I can run GLM 4.5 Air and gpt-oss-120b both very reasonably. GPT OSS has particularly good latency.
I'm on a 128GB M4 macbook. This is "powerful" today, but it will be old news in a few years.
These models are just about getting as good as the frontier models.
ru552
You're better served using Apple's MLX if you want to run models locally.
daemonologist
ONNX Runtime purports to support CoreML: https://onnxruntime.ai/docs/execution-providers/CoreML-Execu... , which gives a decent amount of compatibility for inference. I have no idea to what extent workloads actually end up on the ANE though.
(Unfortunately ONNX doesn't support Vulkan, which limits it on other platforms. It's always something...)
wslh
I find surprising that you can also do that from the browser (e.g. WebLLM). I imagine that in the near future we will run these engines locally for many use cases, instead of via APIs.
punitvthakkar
So far I've not run into the kind of use cases that local LLMs can convincingly provide without making me feel like I'm using the first ever ChatGPT from 2022, in that they are limited and quite limiting. I am curious about what use cases the community has found that work for them. The example that one user has given in this thread about their local LLM inventing a Sun Tzu interview is exactly the kind of limitation I'm talking about. How does one use a local LLM to do something actually useful?
narrator
I have tried a lot of different LLMs and Gemma3:27b on a 48gb+ Macbook is probably the best for analyzing diaries and personal stuff you don't want to share with the cloud. The China models are comically bad with life advice. For example, I asked Deepseek to read my diaries and talk to me about my life goals and it told me in a very Confucian manner what the proper relationships in my life were for my stage of life and station in society. Gemma is much more western.
solardev
Lol, that's actually kinda cool. Did you get any interesting Eastern responses to your diary entries?
I'm imagining something like...
> Dear diary, I got bullied again today, and the bread was stale in my PB&J :(
>> My son, remember this: The one who mocks others wounds his own virtue. The one who suffers mockery must guard his heart. To endure without hatred is strength; to strike without cause is disgrace. The noble one corrects himself first, then the world will follow.
elorant
Chinese models are also awful with translations. Even the Deepseek R1 model performs worse than Mistral small.
punitvthakkar
That is fascinating. One insight I read about LLMs is that they do represent a world-view of the people who train it, and hence the country that ships the dominant LLM technology can spread widely its world-view on others. Your experience seems to validate that insight.
crazygringo
I see local LLM's being used mainly for automation as opposed to factual knowledge -- for classification, summarization, search, and things like grammar checking.
So they need to be smart about your desired language(s) and all the everyday concepts we use in it (so they can understand the content of documents and messages), but they don't need any of the detailed factual knowledge around human history, programming languages and libraries, health, and everything else.
The idea is that you don't prompt the LLM directly, but your OS tools make use of it, and applications prompt it as frequently as they fetch URL's.
theshrike79
And local models are static, predictable and don't just go away when a new one comes out.
This makes them perfect for automation tasks.
dxetech
There are situations where internet access is limited, or where there are frequent outages. An outdated LLM might be more useful than none at all. For example: my internet is out due to a severe storm, what safety precautions do I need to take?
theshrike79
Just use Kiwix: https://kiwix.org/en/
punitvthakkar
Yes - emergency use cases make tons of sense.
volemo
Surely not the ones you get from an LLM?
jondwillis
I use, or at least try to use local models while prototyping/developing apps.
First, they control costs during development, which depending on what you're doing, can get quite expensive for low or no budget projects.
Second, they force me to have more constraints and more carefully compose things. If a local model (albeit something somewhat capable like gpt-oss or qwen3) can start to piece together this agentic workflow I am trying to model, chances are, it'll start working quite well and quite quickly if I switch to even a budget cloud model (something like gpt-5-mini.)
However, dealing with these constraints might not be worth the time if you can stuff all of the documents in your context window for the cloud models and get good results, but it will probably be cheaper and faster on an ongoing basis to have split the task up.
vorticalbox
I keep a lot of notes, all my thoughts feelings both happy and sad, things I’ve done, etc. in obsidian. These are deeply personal and I don’t want this going to a cloud provider even if they “say” they don’t train on my chats.
I forget a lot of things so I feed these into chromeDB and then use a LLM to chat with all my notes.
I’ve started using abliterated models which have their refusal removed [0]
Other use case is for work. I work with financial data and I have created an mcp that automates some of my job. Running model locally allows me to not worry about the information I feed it.
[0] https://github.com/Sumandora/remove-refusals-with-transforme...
dragonwriter
Well, a lot of what is possible with local models depends on what your local hardware is, but docling is a pretty good example of a library that can use local models (VLMs instead of regular LLMs) “under the hood” for productive tasks.
ivape
I use Claude code in the terminal only mostly to figure out what to commit along with what to write for the commit message. I believe a solid 7-8b model can do this locally.
So, that’s at least one small highly useful workflow robot I have a use for (and very easy to cook up on your own).
I also have a use for terminal command autocompletion, which again, a small model can be great for.
Something felt kind really wrong about sending entire folder contents over to Claude online, so I am absolutely looking to create the toolkit locally.
The universe off offline is just getting started, and these big companies literally are telling you “watch out, we save this stuff”.
rukuu001
I'm running Gemma3-270M locally (MLX). I got a Python script that pulls down emails based on a whitelist and summarises them. The 270M model does a good job of this. This is running in a terminal. It means I barely look at my email during the day.
ghilston
Any willingness to share this script? I've been working on some code to ingest things and summarize for them and I haven't gotten to email just yet.
rukuu001
Watch this space. It’s pretty scrappy code and needs a cleanup. It also does other random stuff relating to calendar entries that I want to be reminded to appropriately prepare for.
But yes I’ll share, and I guess post an update in this thread?
daoboy
I'm running Hermes Mistral and the very first thing it did was start hallucinating.
I recently started an audio dream journal and want to keep it private. Set up whisper to transcribe the .wav file and dump it in an Obsidian folder.
The plan was to put a local llm step in to clean up the punctuation and paragraphs. I entered instructions to clean the transcript without changing or adding anything else.
Hermes responded by inventing an intereview with Sun Tzu about why he wrote the Art of War. When I stopped the process it apologized and advised it misunderstood when I talked about Sun Tzu. I never mentioned Sun Tzu or even provided a transcript. Just instructions.
We went around with this for a while before I could even get it to admit the mistake, and it refused to identify why it occurred in the first place.
Having to meticulously check for weird hallucinations will be far more time consuming than just doing the editing myself. This same logic applies to a lot of the areas I'd like to have a local llm for. Hopefully they'll get there soon.
simonh
It’s often been assumed that accuracy and ‘correctness’ would be easy to implement on computers because they operate on logic, in some sense. It’s originality and creativity that would be hard, or impossible because it’s not logical. Science Fiction has been full of such assumptions. Yet here we are, the actual problem is inventing new heavy enough training sticks to beat our AIs out of constantly making stuff up and lying about it.
I suppose we shouldn’t be surprised in hindsight. We trained them on human communicative behaviour after all. Maybe using Reddit as a source wasn’t the smartest move. Reddit in, Reddit out.
smallmancontrov
Pre-training gets you GPT-3, not InstructGPT/ChatGPT. During fine-tuning OpenAI (and everyone else) specifically chose to "beat in" a heavy bias-to-action because a model that just answers everything with "it depends" and "needs more info" is even more useless than a model that turns every prompt into a creative writing exercise. Striking a balance is simply a hard problem -- and one that many humans have not mastered for themselves.
root_axis
> It’s often been assumed that accuracy and ‘correctness’ would be easy to implement on computers because they operate on logic, in some sense. It’s originality and creativity that would be hard
More fundamental than the training data is the fact that the generative outputs are statistical, not logical. This is why they can produce a sequence of logical steps but still come to incorrect or contradictory conclusions. This is also why they tackle creativity more easily since the acceptable boundaries of creative output is less rigid. A photorealistic video of someone sawing a cloud in half can still be entertaining art despite the logical inconsistencies in the idea.
HankStallone
The worst news I've seen about AI was a study that said the major ones get 40% of their references from Reddit (I don't know how they determined that). That explains the cloying way it tries to be friendly and supportive, too.
sandbags
I saw someone reference this today and the question I had was whether this counted the trillions of words accrued from books and other sources. i.e. is it 40%? Or 40% of what they can find a direct attribution link for?
dragonwriter
> It’s often been assumed that accuracy and ‘correctness’ would be easy to implement on computers because they operate on logic, in some sense. It’s originality and creativity that would be hard, or impossible because it’s not logical.
It is easy, comparatively. Accuracy and correctness is what computers have been doing for decades, except when people have deliberately compromised that for performance or other priorities (or used underlying tools where someone else had done that, perhaps unwittingly.)
> Yet here we are, the actual problem is inventing new heavy enough training sticks to beat our AIs out of constantly making stuff up and lying about it.
LLMs and related AI technologies are very much an instance of extreme deliberate compromise of accuracy, correctness, and controllability to get some useful performance in areas where we have no idea how to analytically model the expected behavior but have lots of more or less accurate examples.
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JumpCrisscross
I don't think we're anywhere close to running cutting-edge LLMs on our phones or laptops.
What may be around the corner is running great models on a box at home. The AI lives at home. Your thin client talks to it, maybe runs a smaller AI on device to balance latency and quality. (This would be a natural extension for Apple to go into with its Mac Pro line. $10 to 20k for a home LLM device isn't ridiculous.)
simonw
Right now you can run some of the best available open weight models on a 512GB Mac Studio, which retails for around $10,000. Here's Qwen3-Coder-480B-A35B-Instruct running at 24 tokens/second at 4bit: https://twitter.com/awnihannun/status/1947771502058672219 and Deep Seek V3 0324 in 4-bit at 20 toks/sec https://twitter.com/awnihannun/status/1904177084609827054
You can also string two 512GB Mac Studios together using MLX to load even larger models - here's 671B 8-bit DeepSeek R1 doing that: https://twitter.com/alexocheema/status/1899735281781411907
zargon
What these tweets about Apple silicon never show you: waiting 20+ minutes for it to ingest 32k context tokens. (Probably a lot longer for these big models.)
logicprog
Yeah, I bought a used Mac Studio (an M1, to be fair, but still a Max and things haven't changed since) hoping to be able to run a decent LLM on it, and was sorely disappointed thanks to the prompt processing speed especially.
brokencode
Not sure about the Mac Pro, since you pay a lot for the big fancy case. The Studio seems more sensible.
And of course Nvidia and AMD are coming out with options for massive amounts of high bandwidth GPU memory in desktop form factors.
I like the idea of having basically a local LLM server that your laptop or other devices can connect to. Then your laptop doesn’t have to burn its battery on LLM work and it’s still local.
JumpCrisscross
> Not sure about the Mac Pro, since you pay a lot for the big fancy case. The Studio seems more sensible
Oh wow, a maxed out Studio could run a 600B parameter model entirely in memory. Not bad for $12k.
There may be a business in creating the software that links that box to an app on your phone.
simonw
I have been using a Tailscale VPN to make LM Studio and Ollama running on my Mac available to my iPhone when I leave the house.
brokencode
Perhaps said software could even form an end to end encrypted tunnel from your phone to your local LLM server anywhere over the internet via a simple server intermediary.
The amount of data transferred is tiny and the latency costs are typically going to be dominated by the LLM inference anyway. Not much advantage to doing LAN only except that you don’t need a server.
Though the amount of people who care enough to buy a $3k - $10k server and set this up compared to just using ChatGPT is probably very small.
dghlsakjg
That software is an HTTP request, no?
Any number of AI apps allow you to specify a custom endpoint. As long as your AI server accepts connections to the internet, you're gravy.
theshrike79
It's really easy to whip up a simple box that runs local LLM for a whole home.
Marketing it though? Not doable.
Apple is pretty much the only company I see attempting this with some kind of AppleTV Pro.
data-ottawa
This is what I’m doing with my amd 395+.
I’m running docker containers with different apps and it works well enough for a lot of my use cases.
I mostly use Qwen Code and GPT OSS 120b right now.
When the next generation of this tech comes through I will probably upgrade despite the price, the value is worth it to me.
milgrum
How many TPS do you get running GPT OSS 120b on the 395+? Considering a Framework desktop for a similar use case, but I’ve been reading mixed things about performance (specifically with regards to memory bandwidth, but I’m not sure if that’s really the underlying issue)
data-ottawa
30-40 at 64k context, but it's a mixture of experts model.
A 70b dense model is slower
Qwen coder 30b Q4 runs 40+.
bigyabai
> $10 to 20k for a home LLM device isn't ridiculous.
At that point you are almost paying more than the datacenter does for inference hardware.
JumpCrisscross
> At that point you are almost paying more than the datacenter does for inference hardware
Of course. You and I don't have their economies of scale.
bigyabai
Then please excuse me for calling your one-man $10,000 inference device ridiculous.
vonneumannstan
Almost? Isn't a single h100 like 30k which is the bare minimum to run a big model?
ben_w
> $10 to 20k for a home LLM device isn't ridiculous.
That price is ridiculous for most people. Silicon Valley payscales can afford that much, but see how few Apple Vision Pros got sold for far less.
vonneumannstan
Doesnt gpt-oss-120b perform better across the board at a fraction of the memory? Just specced a $4k mac studio that can easily run that at 128 gb memory.
floweronthehill
I believe local llms are the future. It will only get better. Once we get to the level of even last year's state of the art I don't see any reason to use chatgpt/anthropic/other.
We don't even need one big model good at everything. Imagine loading a small model from a collection of dozens of models depending on the tasks you have in mind. There is no moat.
root_axis
It's true that local LLMs are only going to get better, but it's not clear they will become generally practical for the foreseeable future. There have been huge improvements to the reasoning and coding capabilities of local models, but most of that comes from refinements to training data and training techniques (e.g. RLHF, DPO, CoT etc), while the most important factor by far remains the capability to reduce hallucinations to comfortable margins using the raw statistical power you get with massive full-precision parameter counts. The hardware gap between today's SOTA models and what's available to the consumer are so massive that it'll likely be at least a decade before they become practical.
undefined
nomel
Secure/private cloud compute seems to be the obvious future, to me.
linux2647
Unrelated but I really enjoyed the wavy text effect on “opinions” in the first paragraph
frontsideair
Thank you, it was the integral part of the whole post!
Olshansky
+1 to LM Studio. Helped build a lot of intuition.
Seeing and navigating all the configs helped me build intuition around what my macbook can or cannot do, how things are configured, how they work, etc...
Great way to spend an hour or two.
deepsquirrelnet
I also like that it ships with some cli tools, including an openai compatible server. It’s great to be able to take a model that’s loaded and open up an endpoint to it for running local scripts.
You can get a quick feel for how it works via the chat interface and then extend it programmatically.
atentaten
Every blog post or article about running local LLMs should include something about which hardware was used.
frontsideair
Good point, let me add a quick note.
tpae
Check out Osaurus - MIT Licensed, native, Apple Silicon–only local LLM server - https://github.com/dinoki-ai/osaurus
colecut
thank you
mg
Is anyone working on software that lets you run local LLMs in the browser?
In theory, it should be possible, shouldn't it?
The page could hold only the software in JavaScript that uses WebGL to run the neural net. And offer an "upload" button that the user can click to select a model from their file system. The button would not upload the model to a server - it would just let the JS code access it to convert it into WebGL and move it into the GPU.
This way, one could download models from HuggingFace, store them locally and use them as needed. Nicely sandboxed and independent of the operating system.
simonw
Transformers.js (https://huggingface.co/docs/transformers.js/en/index) is this. Some demos (should work in Chrome and Firefox on Windows, or Firefox Nightly on macOS and Linux):
https://huggingface.co/spaces/webml-community/llama-3.2-webg... loads a 1.24GB Llama 3.2 q4f16 ONNX build
https://huggingface.co/spaces/webml-community/janus-pro-webg... loads a 2.24 GB DeepSeek Janus Pro model which is multi-modal for output - it can respond with generated images in addition to text.
https://huggingface.co/blog/embeddinggemma#transformersjs loads 400MB for an EmbeddingGemma demo (embeddings, not LLMs)
I've collected a few more of these demos here: https://simonwillison.net/tags/transformers-js/
You can also get this working with web-llm - https://github.com/mlc-ai/web-llm - here's my write-up of a demo that uses that: https://simonwillison.net/2024/Nov/29/structured-generation-...
mg
This might be a misunderstanding. Did you see the "button that the user can click to select a model from their file system" part of my comment?
I tried some of the demos of transformers.js but they all seem to load the model from a server. Which is super slow. I would like to have a page the lets me use any model I have on my disk.
simonw
Oh sorry, I missed that bit.
I got Codex + GPT-5 to modify that Llama chat example to implement the "load from local directory" pattern. It appears to work.
First you'll need to grab the checkout of the local model (~1.3GB):
git lfs install
git clone https://huggingface.co/onnx-community/Llama-3.2-1B-Instruct-q4f16
Now click "Browse folder" and select the folder you just checked out with Git.
Click the confusing "Upload" confirmation (it doesn't upload anything, just opens those files in the current browser session).
Now click "Load local model" - and you should get a full working chat interface.
Code is here: https://github.com/simonw/transformers.js-examples/commit/cd...
Here's the full Codex session that I used to build this: https://gist.github.com/simonw/3c46c9e609f6ee77367a760b5ca01...
I ran Codex against the https://github.com/huggingface/transformers.js-examples/tree... folder and prompted:
> Modify this application such that it offers the user a file browse button for selecting their own local copy of the model file instead of loading it over the network. Provide a "download model" option too.
Then later:
> Build the production app and then make it available on localhost somehow
And:
> Uncaught (in promise) Error: Invalid configuration detected: both local and remote models are disabled. Fix by setting `env.allowLocalModels` or `env.allowRemoteModels` to `true`.
And:
> Add a bash script which will build the application such that I can upload a folder called llama-3.2-webgpu to http://static.simonwillison.net/static/2025/llama-3.2-webgpu... and http://static.simonwillison.net/static/2025/llama-3.2-webgpu... will serve the app
(Note that this doesn't allow you to use any model on your machine, but it proves that it's possible.)
SparkyMcUnicorn
Yes. MLC's inference engine runs on WebGPU/WASM.
https://github.com/mlc-ai/web-llm-chat
mg
Yeah, something like that, but without the WebGPU requirement.
Neither FireFox nor Chromium support WebGPU on Linux. Maybe behind flags. But before using a technology, I would wait until it is available in the default config.
Lets see when browsers will bring WebGPU to Linux.
SparkyMcUnicorn
This should be what you're looking for. It doesn't utilize the GPU, but WebGL support is in the TODOs.
simonw
Firefox Nightly on macOS now supports WebGPU, and the documentation says the Linux build supports it too.
generalizations
This is an in-browser llamacpp implementation: https://github.com/ngxson/wllama
And related is the whisper implementation: https://ggml.ai/whisper.cpp/
vonneumannstan
This one is pretty cool. Compile the gguf of an OSS LLM directly into an executable. Will open an interface in the browser to chat. Can also launch an OpenAI API style interface hosted locally.
Doesn't work quite as well on Windows due to the executable file size limit but seems great for Mac/Linux flavors.
adastra22
You don’t need a browser to sandbox something. Easier and more performant to do GOU pass through to a container or VM.
01HNNWZ0MV43FF
Container or VM is a bigger commitment. VMs need root and containers need Docker group and something like docker-compose or a shell script or something.
idk it's just like, do I want to run to the store and buy a 24-pack of water bottles, and stash them somewhere, or do I want to open the tap and have clean drinking water
adastra22
Neither of requirements are true on recent OS versions. Users have had the ability to make containers or VMs without special privileges for a very long time now.
paulirish
Beyond all the wasm/webgpu approaches other folks have linked (mostly in the transformers.js ecosystem), there's been a standardized API brewing since 2019: https://webmachinelearning.github.io/webnn-intro/
Demos here: https://webmachinelearning.github.io/webnn-samples/ I'm not sure any of them allow you to select a model file from disk, but that should be entirely straightforward.
samsolomon
Is Open WebUI something like you are looking for? The design has some awkwardness, but overall it's incorporated a ton of great features.
mg
No, I'm looking for an html page with a button "Select LLM". After pressing that button and selecting a local LLM from disk, it would show an input field where you can type your question and then it would use the given LLM to create the answer.
I'm not sure what OpenWebUI is, but if it was what I mean, they would surely have the page live and not ask users to install Docker etc.
tmdetect
I think what you want is this: https://github.com/mlc-ai/web-llm
bravetraveler
It's both what you want and not; the chat/question interface is as you describe, lack-of-installation is not. The LLM work is offloaded to other software, not the browser.
I would like to skip maintaining all this crap, though: I like your approach
Jemaclus
You should install it, because it's exactly what you just described.
Edit: From a UI perspective, it's exactly what you described. There's a dropdown where you select the LLM, and there's a ChatGPT-style chatbox. You just docker-up and go to town.
Maybe I don't understand the rest of the request, but I can't imagine a software where a webpage exists and it just magically has LLMs available in the browser with no installation?
coip
Have you seen/used the webGPU spaces?
https://huggingface.co/docs/transformers.js/en/guides/webgpu
eta: its predecessor was using webGL
mg
WebGPU is not yet available in the default config of Linux browsers, so WebGL would have been perfect :)
grim_io
It's a crazy upside-down world where the Mac Studio M3 Ultra 512GB is the reasonable option among the alternatives if you intend to run larger models at usable(ish) speeds.
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I agree that it's kind of magical that you can download a ~10GB file and suddenly your laptop is running something that can summarize text, answer questions and even reason a bit.
The trick is balancing model size vs RAM: 12B–20B is about the upper limit for a 16GB machine without it choking.
What I find interesting is that these models don't actually hit Apple's Neural Engine, they run on the GPU via Metal. Core ML isn't great for custom runtimes and Apple hasn't given low-level developer access to the ANE afaik. And then there is memory bandwidth and dedicated SRAM issues. Hopefully Apple optimizes Core ML to map transformer workloads to the ANE.