If you don’t find any hostable service, perhaps you could try Obsidian if its Kanban plugin works well in the mobile client. It’s closed source, but all data is stored in markdown files, and you could use a self-hosted git server for storage and synchronization between users.
https://github.com/resemble-ai/chatterbox is pretty good, and has both TTS and voice cloning. Main disadvantage for me was that even if the cloning gives a consistent voice, the generated samples can get random accents.
https://huggingface.co/zai-org/GLM-TTS also seemed pretty promising, but I haven’t had time to test it yet.
I just wanted to test if it was viable to run larger MoE LLMs on CPU, e.g. Qwen3-next-80B-A3B… Even if I got acceptable generation speeds I’d probably get bored with it after a few hours, as with other local models. Had I got it for €700 it was pretty low value for money anyway, since my current RAM is enough for everything else I use the computer for. On the positive side, I can put that money towards a Steam Frame instead.
… I was thinking about buying a 96GB DDR5 kit from the local computer store a few weeks ago, but wasn’t sure it was actually worth €700. Checked again now and the exact same product costs €1500. I guess that settles it, 32GB will have to be enough for the next couple of years then.
Still are, but I guess a lot of people don’t know much about them
So far that has never happened because I’m not using that much storage :) But I shut it down when I need to turn off the mains electricity, and for powering it on afterwards the fake wall can be lifted off. It’s just the area underneath the desk so the panel might be smaller than it sounds like, and it hangs on some hooks so it’s fairly easy to remove if you know what you’re doing. Painted in the same colour as the wall, and with some some random junk on the floor in front, it blends in quite well though. I think the risk of burglary is fairly low, so it’s primarily to soothe my own paranoia.
I mounted mine on the wall under a desk in a room with no other electronics, and then put up a fake wall in front of the server. It can draw in air from the sides, and exhaust upwards behind the desk. But the only real solution is offsite backup, which will also protect against fire and other disasters.
Can’t help but think about this old XKCD from 2010.
Products targeted towards businesses have always been unreasonably more expensive than those targeted towards consumers. It sucks for us AI hobbyists that Nvidia are stingy with VRAM on consumer cards, but I don’t find it surprising.
Personally I only have a single RTX 3090, but I know a lot of people online who are stacking multiple consumer cards to run AI. Buying used 3090s and putting them in a mining rig is probably still the best value for money if you need a large amount of VRAM.
How much VRAM do you actually need btw?
Intel NUC running Linux. Not the cheapest solution but can play anything and I have full control over it. At first I tried to find some kind of programmable remote but now we have a wireless keyboard with built-in touchpad.
Biggest downside is that the hardware quality is kind of questionable and the first two broke after 3 years + a few months, so we’re on our third now.
This is my wireguard docker setup:
version: "3.6"
services:
wireguard:
image: linuxserver/wireguard
container_name: wireguard
cap_add:
- NET_ADMIN
- SYS_MODULE
environment:
- PUID=116
- PGID=122
- TZ=Europe/Stockholm
- ALLOWEDIPS=192.168.1.0/24
volumes:
- /data/torrent/wireguard/config:/config
- /lib/modules:/lib/modules
ports:
- 192.168.1.111:8122:8122 # Deluge webui
- 192.168.1.111:9127:9127 # jackett webui
- 192.168.1.111:9666:9666 # prowlarr webui
- 51820:51820/udp # wireguard
- 192.168.1.111:58426:58426 # Deluge RPC
sysctls:
- net.ipv4.conf.all.src_valid_mark=1
- net.ipv6.conf.all.disable_ipv6=1
- net.ipv6.conf.default.disable_ipv6=1
restart: unless-stopped
Can reach the webuis from LAN, no other network configuration was necessary. 192.168.1.111 is the server’s LAN address. The other services are configured very similar to your qbittorrent, and don’t expose any ports. Can’t promise it’s 100% correct but it’s working for me.
Add “site:reddit.com” to your google query.
Sad thing is that search engines have got so bad, and usually return so much garbage blog spam that searching directly on reddit is more likely to give useful results. I hope a similar amount of knowledge will build up on Lemmy over time.
Assuming they already own a PC, if someone buys two 3090 for it they’ll probably also have to upgrade their PSU so that might be worth including in the budget. But it’s definitely a relatively low cost way to get more VRAM, there are people who run 3 or 4 RTX3090 too.
For LLMs it entirely depends on what size models you want to use and how fast you want it to run. Since there’s diminishing returns to increasing model sizes, i.e. a 14B model isn’t twice as good as a 7B model, the best bang for the buck will be achieved with the smallest model you think has acceptable quality. And if you think generation speeds of around 1 token/second are acceptable, you’ll probably get more value for money using partial offloading.
If your answer is “I don’t know what models I want to run” then a second-hand RTX3090 is probably your best bet. If you want to run larger models, building a rig with multiple (used) RTX3090 is probably still the cheapest way to do it.
Is max tokens different from context size?
Might be worth keeping in mind that the generated tokens go into the context, so if you set it to 1k with 4k context you only get 3k left for character card and chat history. I think i usually have it set to 400 tokens or something, and use TGW’s continue button in case a long response gets cut off
llama.cpp uses the gpu if you compile it with gpu support and you tell it to use the gpu…
Never used koboldcpp, so I don’t know why it would it would give you shorter responses if both the model and the prompt are the same (also assuming you’ve generated multiple times, and it’s always the same). If you don’t want to use discord to visit the official koboldcpp server, you might get more answers from a more llm-focused community such as [email protected]
I use https://www.criticker.com/ for movies because it has a really nice recommendation algorithm based on your personal scores. They also have a section for rating games but I haven’t tried that part
Hooray, I can finally play it. Had it on my wish-list for years, when I finally bought it I found out that neither the native Linux or the Windows+Proton version was working.
Never used MS Planner, but the Kanban plugin does everything I need and I use it for my larger hobby programming projects. I was already using obsidian.md for other things so for me it was very convenient.