I recommend a used 3090, as that has 24 GB of VRAM and generally can be found for $800ish or less (at least when I last checked, in February). It’s much cheaper than a 4090 and while admittedly more expensive than the inexpensive 24GB Nvidia Tesla card (the P40?) it also has much better performance and CUDA support.
I have dual 3090s so my performance won’t translate directly to what a single GPU would get, but it’s pretty easy to find stats on 3090 performance.
The above post says it has support for Ollama, so I don’t think this is the case… but the instructions in the Readme do make it seem like it’s dependent on OpenAI.
16 GB of RAM, though? Is it even optimized for the Ryzen 9950X3D?
And a 4 TB SSD - not even necessarily NVME?
Doesn’t seem high powered to me.
Are you saying that NAT isn’t effectively a firewall or that a NAT firewall isn’t effectively a firewall?
Is there a way to use symlinks instead? I’d think it would be possible, even with Docker - it would just require the torrent directory to be mounted read-only in the same location in every Docker container that had symlinks to files on it.
Depending on setup this can be true with Jellyfin, too. I have a domain registered, use dynamic DNS, and have Traefik direct a subdomain to my Jellyfin server. My mobile clients are configured using that. My local clients use the local static IP.
If my internet goes down, my mobile clients can’t connect, even on the LAN.
OP: “Zen 5 CPU”
You: “Unpatched Zen 1 through Zen 4 CPUs are vulnerable”
You: Is it vulnerable?
???
Under notes, where you said my name, did you mean “Hedgedoc?”
local docker hub proxy
Do you mean a Docker container registry? If so, here are a couple options:
They put their repo first on the list.
Right. And are we talking about the list for OBS or of repos in general? I doubt Fedora sets the priority on a package level. And if they don’t, and if there are some other packages in Flathub that are problematic, then it makes sense to prioritize their own repo over them.
That said, if those problematic packages come from other repositories, or if not but there’s another alternative to putting their repo first that would have prevented unofficial builds from showing up first, but wouldn’t have deprioritized official, verified ones like OBS, then it’s a different story. I haven’t maintained a package on Flathub like the original commenter you replied to but I don’t get the impression that that’s the case.
Why did Fedora make their packages take priority? Is it because the priority is otherwise random and if you don’t have a priority set, that leads to the issue they mentioned? Because if so, that sounds like a reasonable action by Fedora and like the real culprit is Flathub.
Clearly they’re cosplaying as a Canonical engineer whose internal explanation and pleas for them to not take this approach fell upon deaf ears /j
If you’re a C developer who doesn’t know Rust, no.
Giphy has a documented API that you could use. There have been bulk downloaders, but I didn’t see any that had recent activity. However you still might be able to use one to model your own script after, like https://github.com/jcpsimmons/giphy-stacks
There were downloaders for Gfycat - gallery-dl supported it at one point - but it’s down now. However you might be able to find collections that other people downloaded and are now hosting. You could also use the Internet Archive - they have tools and APIs documented
There’s a Tenor mass downloader that uses the Tenor API and an API key that you provide.
Imgur has GIFs is supported by gallery-dl, so that’s an option.
Also, read over https://github.com/simon987/awesome-datahoarding - there may be something useful for you there.
In terms of hosting, it would depend on my user base and if I want users to be able to upload GIFs, too. If it was just my close friends, then Immich would probably be fine, but if we had people I didn’t know directly using it, I’d want a more refined solution.
There’s Gifable, which is pretty focused, but looks like it has a pretty small following. I haven’t used it myself to see how suitable it is. If you self-host it (or something else that uses S3), note that you can use MinIO or LocalStack for the S3 container rather than using AWS directly. I’m using MinIO as part of my stack now, though for a completely different app.
MediaCMS is another option. Less focused on GIFs but more actively developed, and intended to be used for this sort of purpose.
No, game mechanics aren’t subject to copyright law. Game mechanics can be patented in the US, so long as they’re unique and nonobvious (to someone with ordinary skill in the field).
Monopoly and Magic: The Gathering both had patents on their mechanics, for example.
And of course, patents in Japan are a completely different animal than patents in the US.
500 grams of what, though? Folgers?
The current average price per pound (454 grams) of ground coffee beans in the US was double that just a couple months ago, so spending $3.00 per pound would necessitate getting cheaper than average - and therefore, likely lower quality than average, or at least lower perceived quality than average - beans.
The sorts of beans that companies tend to stock (IME) that are perceived as higher quality aren’t the same brands that I tend to buy (generally from local roasters), but they’re comparably priced. For a 5 pound (2267 grams) bag of one of their blends (which are roughly half the price of their higher end beans), it’s similar to what you’d pay for 5 pounds of Starbucks beans - about $50-$60.
Often when a company says “free coffee,” they don’t mean “free batch-brewed drip coffee,” but rather, free espresso beverages, potentially in a machine (located in the break room) that automates the whole process. I assume that’s what Intel is doing.
At $10 per pound (16 ounces) and roughly 1 ounce (28 grams) of beans per two ounce pour of espresso, that means that if each person on average drinks two per day, then that’s $1.25 for coffee per person per day.
However, logistics costs (delivering coffee to all the company’s break rooms) and operational costs (the cost of the automatic machine and repairs, at minimum; or the cost of baristas, or adding the responsibility to someone’s existing job (and thus needing more people or more hours) if just batch brewing) have to be added on top of that. Then add in the cost of milk, milk alternatives, sweeteners, cups, lids, stir sticks, etc…
Obviously if they just had free coffee grounds and let people handle the actual brewing of coffee in the break room, it would be much cheaper. But if the goal is to improve morale, having higher quality coffee that people don’t have to make themselves is going to do that better.
That’s $3.33 per employee per work day, assuming 50 5-day weeks per year. Seems a bit high to me, but not exorbitant. If the figure included things that they’re not reinstating (like free fruit) then that would make sense.
That’s fair, but he would be able to play the games in the meantime. And since hardware generally gets cheaper as time passes, if he could set aside more than the subscription fee each month to save for his own hardware, he’d be able to game in the meantime. And if he ever had to cancel it, he’d be closer to being able to buy his own hardware than he is today - meaning more time total spent gaming.
Look up “LLM quantization.” The idea is that each parameter is a number; by default they use 16 bits of precision, but if you scale them into smaller sizes, you use less space and have less precision, but you still have the same parameters. There’s not much quality loss going from 16 bits to 8, but it gets more noticeable as you get lower and lower. (That said, there’s are ternary bit models being trained from scratch that use 1.58 bits per parameter and are allegedly just as good as fp16 models of the same parameter count.)
If you’re using a 4-bit quantization, then you need about half that number in VRAM. Q4_K_M is better than Q4, but also a bit larger. Ollama generally defaults to Q4_K_M. If you can handle a higher quantization, Q6_K is generally best. If you can’t quite fit it, Q5_K_M is generally better than any other option, followed by Q5_K_S.
For example, Llama3.3 70B, which has 70.6 billion parameters, has the following sizes for some of its quantizations:
This is why I run a lot of Q4_K_M 70B models on two 3090s.
Generally speaking, there’s not a perceptible quality drop going to Q6_K from 8 bit quantization (though I have heard this is less true with MoE models). Below Q6, there’s a bit of a drop between it and 5 and then 4, but the model’s still decent. Below 4-bit quantizations you can generally get better results from a smaller parameter model at a higher quantization.
TheBloke on Huggingface has a lot of GGUF quantization repos, and most, if not all of them, have a blurb about the different quantization types and which are recommended. When Ollama.com doesn’t have a model I want, I’m generally able to find one there.