I Self-Hosted Llama 3.2 with Coolify on My Home Server

[bambax]

> I decided to explore self-hosting some of my non-critical applications

Self-hosting static or almost-static websites is now really easy with a Cloudflare front. I just closed my account on SmugMug and published my images locally using my NAS; this costs no extra money (is basically free) since the photos were already on the NAS, and the NAS is already powered on 24-7.

The NAS I use is an Asustor so it's not really Linux and you can't install what you want on it, but it has Apache, Python and PHP with Sqlite extension, which is more than enough for basic websites.

Cloudflare free is like magic. Response times are near instantaneous and setup is minimal. You don't even have to configure an SSL certificate locally, it's all handled for you and works for wildcard subdomains.

And of course if one puts a real server behind it, like in the post, anything's possible.

[ghoomketu]

> Cloudflare free is like magic

Cloudflare is pretty strict about the Html to media ratio and might suspend or terminate your account if you are serving too many images.

I've read far too many horror stories about this on hn only so please make sure what you're doing is allowed by their TOS.

[archerx]

You could also use openVPN or wireguard and not have a man in the middle for no reason.

I have a VPN on a raspberry pi and with that I can connect to my self hosted cloud, dev/staging servers for projects, gitlab and etc when I’m not on my home network.

[Reubend]

Is the NAS exposed to the whole internet? Or did you find a clever way to get CloudFlare in front of it despite it just being local?

[taosx]

For the people who self-host LLMs at home: what use cases do you have?

Personally, I have some notes and bookmarks that I'd like to scrape, then have an LLM summarize, generate hierarchical tags, and store in a database. For the notes part at least, I wouldn't want to give them to another provider; even for the bookmarks, I wouldn't be comfortable passing my reading profile to anyone.

[xyc]

llama3.2 1b & 3b is really useful for quick tasks like creating some quick scripts from some text, then pasting them to execute as it's super fast & replaces a lot of temporary automation needs. If you don't feel like invest time into automation, sometimes you can just feed into an LLM.

This is one of the reason why recently I added floating chat to https://recurse.chat/ to quickly access local LLM.

Here's a demo: https://x.com/recursechat/status/1846309980091330815

[TechDebtDevin]

I keep an 8b running with ollama/openwebui to ask it to format things, summarization, and to generate SQL/simple bash commands and what not.

[archerx]

For me at least the biggest feature of some self hosted LLMs is that you can get it then to be “uncensored”, you can get them to tell you dirty jokes or have the bias removed with controversial and politically incorrect subjects. Basically you have a freedom you won’t get from most of the main providers.

[Rick76]

I essentially use it like everyone else. I use it to search through my personal documents because I can control the token size and file embedding

[williamcotton]

I work with a lot of attorney’s eyes only documents and most protective orders do not allow for shipping off these files to a third-party.

[ein0p]

I run Mistral Large on 2xA6000. 9 times out of 10 the response is the same quality as GPT 4o. My employer does not allow the use of GPT for privacy related reasons. So I just use a private Mistral for that

[laniakean]

I mostly use it to write some quick scripts or generate texts if it follows some pattern. Also, getting it up running with LM studio is pretty straightforward.

[segalord]

I use it exclusively for users on my personal website to chat with my data. I've given the setup tools to have read access my files and data

[cma]

They are much more flexible, you can e.g. edit the system's own responses rather than waste context on telling it a correction.

[theodric]

I've been enjoying fine-tuning various models with various data, for example 17 years of my own tweets, and then just cranking up the temperature and letting the model generate random crap that cracks me up. Is that practical? Is joy practical? I think there's a place for it.

[m0wer]

TabbyML! Autocompletion like GitHub Copilot, using qwen-2.5-coder 7B.

[netdevnet]

Am I right thinking that a self-hosted llama wouldn't have the kind restrictions ChatGPT has since it has no initial system prompt?

[dtquad]

All the self-hosted LLM and text-to-image models come with some restrictions trained into them [1]. However there are plenty of people who have made uncensored "forks" of these models where the restrictions have been "trained away" (mostly by fine-tuning).

You can find plenty of uncensored LLM models here:

https://ollama.com/library

[1]: I personally suspect that many LLMs are still trained on WebText, derivatives of WebText, or using synthetic data generated by LLMs trained on WebText. This might be why they feel so "censored":

>WebText was generated by scraping only pages linked to by Reddit posts that had received at least three upvotes prior to December 2017. The corpus was subsequently cleaned

The implications of so much AI trained on content upvoted by 2015-2017 redditors is not talked about enough.

[Kudos]

Many protections are baked into the models themselves.

[nubinetwork]

That depends on the frontend, you can supply a system prompt if you want to... whether it follows it to the letter is another problem...

[exe34]

It has a sanitised output. You might want to look for "abliterated" models, where the general performance might drop a bit but the guard-rails have been diminished.

[seungwoolee518]

Great post!

However, Do I need to Install CUDA toolkit on host?

I haven't install CUDA toolkit when I use on Containerized platform (like docker)

[thangngoc89]

You don't need to install CUDA toolkit on host system.

Nvidia driver + Nvidia container toolkit would do the job. You could check official instructions at [0]

[0] https://docs.nvidia.com/datacenter/cloud-native/container-to...

[ossusermivami]

ai generated blog post (or reworded, whatever) are kinda getting very irritating, like playing chess against the computer, it feel soulless

[hmcamp]

How can you tell this post was ai generated? I’m curious.

[varun_ch]

I’m curious about how good the performance with local LLMs is on ‘outdated’ hardware like the author’s 2060. I have a desktop with a 2070 super that it could be fun to turn into an “AI server” if I had the time…

[magicalhippo]

I've been playing with some LLMs like Llama 3 and Gemma on my 2080Ti. If it fits in GPU memory the inference speed is quite decent.

However I've found quality of smaller models to be quite lacking. The Llama 3.2 3B for example is much worse than Gemma2 9B, which is the one I found performs best while fitting comfortably.

Actual sentences are fine, but it doesn't follow prompts as well and it doesn't "understand" the context very well.

Quantization brings down memory cost, but there seems to be a sharp decline below 5 bits for those I tried. So a larger but heavily quantized model usually performs worse, at least with the models I've tried so far.

So with only 6GB of GPU memory I think you either have to accept the hit on inference speed by only partially offloading, or accept fairly low model quality.

Doesn't mean the smaller models can't be useful, but don't expect ChatGPT 4o at home.

That said if you got a beefy CPU then it can be reasonable to have it do a few of the layers.

Personally I found Gemma2 9B quantized to 6 bit IIRC to be quite useful. YMMV.

[khafra]

If you want to set up an AI server for your own use, it's exceedingly easy to install LM Studio and hit the "serve an API" button.

Testing performance this way, I got about 0.5-1.5 tokens per second with an 8GB 4bit quantized model on an old DL360 rack-mount server with 192GB RAM and 2 E5-2670 CPUs. I got about 20-50 tokens per second on my laptop with a mobile RTX 4080.

[dtquad]

I am using an old laptop with a GTX 1060 6 GB VRAM to run a home server with Ubuntu and Ollama. Because of quantization Ollama can run 7B/8B models on an 8 year old laptop GPU with 6 GB VRAM.

[whitefables]

Here's how it looks like in real time: https://youtu.be/3vhJ6fNW8AI

[alias_neo]

You can get a relative idea here: https://developer.nvidia.com/cuda-gpus

I use a Tesla P4 for ML stuff at home, it's equivalent to a 1080 Ti, and has a score of 7.1. A 2070 (they don't list the "super") is a 7.5.

For reference, 4060 Ti, 4070 Ti, 4080 and 4090 are 8.9, which is the highest score for a gaming graphics card.

[taosx]

Last time I tried a local llm was about a year ago with a 2070S and 3950x and the performance was quite slow for anything beyond phi 3.5 and the small models quality feels worse than what some providers offer for cheap or free so it doesn't seem worth it with my current hardware.

Edit: I've loaded llama 3.1 8b instruct GGUF and I got 12.61 tok/sec and 80tok/sec for 3.2 3b.

[nubinetwork]

I'm happy with a Radeon VII, unless the model is bigger than 16gb...

[_blk]

Why disable LVM for a smoother reboot experience? For encryption I get it since you need a key to mount, but all my setups have LVM or ZFS and I'd say my reboots are smooth enough.

[satvikpendem]

I love Coolify, used to use v3, anyone know how their v4 is going? I thought it was still a beta release from what I saw on GitHub.

[j12a]

Coolify is quite nice, have been running some things with the v4 beta.

It reminds a bit of making web sites with a page builder. Easy to install and click around to get something running without thinking too much about it fairly quickly.

Problems are quite similar also, training wheels getting stuck in the woods more easily, hehe.

[raybb]

V4 beta is working well for me. Also the new core dev Coolify hired mentioned in a Tweet this week that they're fixing up lots of bugs to get ready for V4 stable.

[whitefables]

I'm using v4 beta in the blog post. Didn't try v3 so there's no point of comparison but I'm loving it so far!

It was so easy to get other non-AI stuffs running!

[sorenjan]

Can you use a selfhosted LLM that fits in 12 GB VRAM as a reasonable substitute for copilot in VSCode? And if so, can you give it documentation and other code repositories to make it better at a particular language and platform?

[0xedd]

Technically, yes, but will yield poor results. We did it internally at big corp n+1 and it, frankly, blows. Other than menial tasks, it's good for nothing but a scout badge.

[vincentclee]

Instead of `watch -n 0.5 nvidia-smi` to track GPU usage. One can use `nvtop`

https://github.com/Syllo/nvtop

[cranberryturkey]

How is coolify different than ollama? is it better? worse? I like ollama because I can pull models and it exposes a rest api to me. which is great for development

[grahamj]

Might want to skim the article

[eloycoto]

I have something like this, and I'm super happy with anythingLLM, which allows me to add a custom board with my workspaces, RAG, etc.. I love it!

[ragebol]

Probably saves a bit on the gas bill for heating too

[rglullis]

Snark aside, even in Germany (where electricity is very expensive) it is more economical to self host than to pay for a subscription to any of the commercial providers.

[CraigJPerry]

I don’t know, it’s kind of amazing how good the lighter weight self hosted models are now.

Given a 16gb system with cpu inference only, I’m hosting gemma2 9b at q8 for llm tasks and SDXL turbo for image work and besides the memory usage creeping up for a second or so while i invoke a prompt, they’re basically undetectable in the background.

[szundi]

If only we had heat-pump computers

[keriati1]

What model size is used here? How much memory does the GPU have?

[thawab]

he is using the 3b one, since it's the default when downloading it from ollama: https://ollama.com/library/llama3.2