Article: https://proton.me/blog/deepseek
Calls it “Deepsneak”, failing to make it clear that the reason people love Deepseek is that you can download and it run it securely on any of your own private devices or servers - unlike most of the competing SOTA AIs.
I can’t speak for Proton, but the last couple weeks are showing some very clear biases coming out.
What??? Whoever wrote this sounds like he has 0 understanding of how it works. There is no “more privacy-friendly version” that could be developed, the models are already out and you can run the entire model 100% locally. That’s as privacy-friendly as it gets.
Operated, yes. Trained, no. The model is MIT licensed, China has nothing on you when you run it yourself. I expect better from a company whose whole business is on privacy.
To be fair, most people can’t actually self-host Deepseek, but there already are other providers offering API access to it.
There are plenty of step-by-step guides to run Deepseek locally. Hell, someone even had it running on a Raspberry Pi. It seems to be much more efficient than other current alternatives.
That’s about as openly available to self host as you can get without a 1-button installer.
You can run an imitation of the DeepSeek R1 model, but not the actual one unless you literally buy a dozen of whatever NVIDIA’s top GPU is at the moment.
A server grade CPU with a lot of RAM and memory bandwidth would work reasonable well, and cost “only” ~$10k rather than 100k+…
I saw posts about people running it well enough for testing purposes on an NVMe.
Can you link that post?
https://old.reddit.com/r/LocalLLaMA/comments/1idseqb/deepseek_r1_671b_over_2_toksec_without_gpu_on/
Thanks!
That’s cool! I’m really interested to know how many tokens per second you can get with a really good U.2. My gut is that it won’t actually be better than the 24VRAM+96RAM cache setup this user already tested with though.
Running R1 locally isn’t realistic. But you can rent a server and run it privately on someone else’s computer. It costs about 10 per hour to run. You can run it on CPU for a little less. You need about 2TB of RAM.
If you want to run it at home, even quantized in 4 bit, you need 20 4090s. And since you can only have 4 per computer for normal desktop mainboards, that’s 5 whole extra computers too, and you need to figure out networking between them. A more realistic setup is probably running it on CPU, with some layers offloaded to 4 GPUs. In that case you’ll need 4 4090s and 512GB of system RAM. Absolutely not cheap or what most people have, but technically still within the top top top end of what you might have on your home computer. And remember this is still the dumb 4 bit configuration.
Edit: I double-checked and 512GB of RAM is unrealistic. In fact anything higher than 192 is unrealistic. (High-end) AM5 mainboards support up to 256GB, but 64GB RAM sticks are much more expensive than 48GB ones. Most people will probably opt for 48GB or lower sticks. You need a Threadripper to be able to use 512GB. Very unlikely for your home computer, but maybe it makes sense with something else you do professionally. In which case you might also have 8 RAM slots. And such a person might then think it’s reasonable to spend 3000 Euro on RAM. If you spent 15K Euro on your home computer, you might be able to run a reduced version of R1 very slowly.
You don’t need that much ram to run this
How much do you need? Show your maths. I looked it up online for my post, and the website said 1747GB, which is completely in-line with other models.
https://apxml.com/posts/gpu-requirements-deepseek-r1
Unfortunately it is you who have 0 understanding of it. Read my comment below. Tldr: good luck to have the hardware
I understand it well. It’s still relevant to mention that you can run the distilled models on consumer hardware if you really care about privacy. 8GB+ VRAM isn’t crazy, especially if you have a ton of unified memory on macbooks or some Windows laptops releasing this year that have 64+GB unified memory. There are also websites re-hosting various versions of Deepseek like Huggingface hosting the 32B model which is good enough for most people.
Instead, the article is written like there is literally no way to use Deepseek privately, which is literally wrong.
So I’ve been interested in running one locally but honestly I’m pretty confused what model I should be using. I have a laptop with a 3070 mobile in it. What model should I be going after?
as I said in my original comment, it’s not only VRAM that matters.
I honestly doubt that even gamer laptops can run these models with a usable speed, but even if we add up the people who have such a laptop, and those who have a PC powerful enough to run these models, they are tiny fractions of those that use the internet on the world. it is basically not available to those that want to use it. ot is available to some of them, but not nearly all who may want it
Is it Open Source? I cannot find the source code. The official repository https://github.com/deepseek-ai/DeepSeek-R1 only contains images, a PDF file, and links to download the model. But I don’t see any code. What exactly is Open Source here?
I don’t see the source either. Fair cop.
Thanks for confirmation. I made a top level comment too, because this important information gets lost in the comment hierarchy here.
Open source is in general wrong term in all of these “open source” LLM’s (like LLAMA and R1), the model is shared, but there is no real way of reproducing the model. This is because the training data is never shared.
In my mind open source means that you can reproduce the same binary from source. The models are shared for free, but not “open”.
There are already other providers like Deepinfra offering DeepSeek. So while the the average person (like me) couldn’t run it themselves, they do have alternative options.
which probably also collects and keeps everything you say in the chat. just look in ublock origin’s expanded view to see their approach to privacy, by having a look at all the shit they are pushing to your browser
Obviously you need lots of GPUs to run large deep learning models. I don’t see how that’s a fault of the developers and researchers, it’s just a fact of this technology.
and that is not what I was complaining about
Down votes be damned, you are right to call out the parent they clearly dont articulate their point in a way that confirms they actually understand what is going on and how an open source model can still have privacy implications if the masses use the company’s hosted version.