I agree that hosted models are usually a better option for most people - much faster, higher quality, handle longer inputs, really cheap.
I enjoy local models for research and for the occasional offline scenario.
I'm also interested in their applications for journalism, specifically for dealing with extremely sensitive data like leaked information from confidential sources.
Think it is NOT just you. Most company with decent management also would not want their data going to anything outside the physical server they have in control of. But yeah for most people just use an app and hosted server. But this is HN,there are ppl here hosting their own email servers, so shouldn't be too hard to run llm locally.
I don't think that's been true for over a decade: AWS wouldn't be trillion dollar business if most companies still wanted to stay on-premise.
Using AWS is basically a "physical server they have control of".
What amuses me even more is people thinking their code is too unique and precious, and that GitHub/Microsoft wants to steal it.
With LLMs, they're thinking of examples that regurgitated proprietary code, and contrary to everyday general observation, valuable proprietary code does exist.
But with GitHub, the thinking is generally the opposite: the worry is that the code is terrible, and seeing it would be like giant blinkenlights* indicating the way in.
I assume because of the assumption that the AI companies will train off of your data, causing it to leak? But I thought all these services had enterprise tiers where they'll promise not to do that?
Again, I'm not complaining, it's good to see people caring about where their data goes. Just interesting that they care now, but not before. (In some ways LLMs should be one of the safer services, since they don't even really need to store any data, they can delete it after the query or conversation is over.)
Laundering of data through training makes it a more complicated case than a simple data theft or copyright infringement.
Leaks could be accidental, e.g. due to an employee logging in to their free-as-in-labor personal account instead of a no-training Enterprise account. It's safer to have a complete ban on providers that may collect data for training.
There is no world in which training on customer data without permission would be worth it for AWS.
Your data really isn't that useful anyway.
? One single random document, maybe, but as an aggregate, I understood some parties were trying to scrape indiscriminately - the "big data" way. And if some of that input is sensitive, and is stored somewhere in the NN, it may come out in an output - in theory...
Actually I never researched the details of the potential phenomenon - that anything personal may be stored (not just George III but Random Randy) -, but it seems possible.
That might have been true a few years ago but today the top AI labs are all focusing on quality: they're trying to find the best possible sources of high quality tokens, not randomly dumping in anything they can obtain.
Andrej Karpathy said this last year: https://twitter.com/karpathy/status/1797313173449764933
> Turns out that LLMs learn a lot better and faster from educational content as well. This is partly because the average Common Crawl article (internet pages) is not of very high value and distracts the training, packing in too much irrelevant information. The average webpage on the internet is so random and terrible it's not even clear how prior LLMs learn anything at all.
If there exists some advantage on quantity though, then achieving high quality imposes questions about tradeoffs and workflows - sources where authors are "free participants" could have odd data sip in.
And the matter of whether such data may be reflected in outputs remains as a question (probably tackled by some I have not read... Ars longa, vita brevis).
Any important data should NOT be in devices that is NOT physically with in our jurisdiction.
Lots of AI companies have some of these, but not to the same extent.
Most companies physical and digital security controls are so much worst than anything from AWS or Google. Note I dont include Azure...but a physical server they have control of is a phrase that screams vulnerability.
Can you explain this further? It seems in contrast to your previous comment about trusting Anthropic with your data
If they get hit by a government subpoena because a journalist has been using them to analyze leaked corporate or government secret files I also trust them to honor that subpoena.
Sometimes journalists deal with material that they cannot risk leaving their own machine.
"News is what somebody somewhere wants to suppress"
You may need to "right-size" the models you use to match your hardware, model, and TPS expectations, which may involve using a smaller version of the model with faster TPS, upgrading your jardware, or paying for hosted models.
Alternatively, if you can use agentic workflows or tools like Aider, you don't have to watch the model work slowly with large modles locally. Instead you queue work for it, go to sleep, or eat, or do other work, and then much later look over the Pull Requests whenever it completes them.
Regarding agentic workflows -- sounds nice but I am too scared to try it out, based on my experience with standard LLMs like GPT or Claude for writing code. Small snippets or filling in missing unit tests, fine, anything more complicated? Has been a disaster for me.
That's not the stuff you want to send to a public API, this is something you want as a 24/7 locally running batch job.
("AI assistant" is an evolutionary dead end, and Star Trek be damned.)
There is another aspect to consider, aside from privacy.
These models are trained by downloading every scrap of information from the internet, including the works of many, many authors who have never consented to that. And they for sure are not going to get a share of the profits, if there is every going to be any. If you use a cloud provider, you are basically saying that is all fine. You are happy to pay them, and make yourself dependent on their service, based on work that wasn't theirs to use.
However, if you use a local model, the authors still did not give consent, but one could argue that the company that made the model is at least giving back to the community. They don't get any money out of it, and you are not becoming dependent on their hyper capitalist service. No rent-seeking. The benefits of the work are free to use for everyone. This makes using AI a little more acceptable from a moral standpoint.
On some images where Gemma3 struggles Mistral Small produces better descriptions, BTW. But it seems harder to make it follow my instructions exactly.
I'm looking forward to the day when I can also do this with videos, a lot of which I also have no interest in uploading to someone else's computer.
As an aside, what model/tools do you prefer for tagging people?
As it stands, Gemma will just say "Woman looking out in the desert sky."
If you want natural language to resolve the names, that'd at a minimum require bounding boxes of the faces and their corresponding names. It'd also require either preprocessing, or specialized training, or both. To my knowledge no locally-hostable model as of today has that. I don't know if any proprietary models can do this either, but it's certainly worth a try - they might just do it. The vast majority of the things they can do is emergent, meaning they were never specifically trained to do them.
`ollama run gemma3:27b --verbose` gives me 42.5 TPS +-0.3TPS
`ollama run gemma3:27b-it-qat --verbose` gives me 41.5 TPS +-0.3TPS
Strange results; the full model gives me slightly more TPS.
(-t 32 -ngl 100 -c 8192 -fa -ctk q8_0 -ctv q8_0 -m models/gemma-3-27b-it-qat-q4_0.gguf)
LLM Studio running on a Mac Studio M4 Max with 128GB, gemma-3-27B-it-QAT-Q4_0.gguf with a 4096 token context I get 8.89 tps.
I think it was just the filename. I tried the model you suggested by opening it in LLMStudio and keep getting 8.3 tps.
I get 24tps in LM Studio now with gemma-3-27b-it-qat.
query: "make me a snake game in python with pygame"
(mlx 4 bit quant) mlx-community/gemma-3-27b-it-qat@4bit: 26.39 tok/sec • 1681 tokens 0.63s to first token
(gguf 4 bit quant) lmstudio-community/gemma-3-27b-it-qat: 22.72 tok/sec • 1866 tokens 0.49s to first token
using Unsloth's settings: https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-...
More and more I start to realize that cost saving is a small problem for local LLMs. If it is too slow, it becomes unusable, so much that you might as well use public LLM endpoints. Unless you really care about getting things done locally without sending information to another server.
With OpenAI API/ChatGPT, I get response much faster than I can read, and for simple question, it means I just need a glimpse of the response, copy & paste and get things done. Whereas on local LLM, I watch it painstakingly prints preambles that I don't care about, and get what I actually need after 20 seconds (on a fast GPU).
And I am not yet talking about context window etc.
I have been researching about how people integrate local LLMs in their workflows. My finding is that most people play with it for a short time and that's about it, and most people are much better off spending money on OpenAI credits (which can last a very long time with typical usage) than getting a beefed up Mac Studio or building a machine with 4090.