https://github.com/vladmandic/sdnext

• For LLM hosting, ik_llama.cpp. You can really gigantic models at acceptable speeds with its hybrid CPU/GPU focus, at higher quality/speed than mainline llama.cpp, and it has several built in UIs.

• LanguageTool, for self run grammar/spelling/style checking.

Keep an eye on Open Web UI. I’ve heard rumblings that it’s starting to enshittify.

FYI, A1111 is obsolete. The diffusers or comfy-based backends are way faster, richer, less buggy and support newer things.

I’d recommend UIs that support SVDQuant, in particular.

I hate to break it to you, but people on Lemmy call for the death of politicians and their families all the tame.

It’s… icky.

It’s made me really close to leaving the whole platform, and the .world admin’s sentiment against it is about the only thing that stopped me (even if they don’t enforce it very strictly).

deleted by creator

To be clear, VMs absolutely have overhead but Docker/Podman is the question. It might be negligible.

And this is a particularly weird scenario (since prompt processing literally has to shuffle ~112GB over the PCIe bus for each batch). Most GPGPU apps aren’t so sensitive to transfer speed/latency.

In my case it’s performance and sheer RAM need.

GLM 4.5 needs like 112GB RAM and absolutely every megabyte of VRAM from the GPU, at least without the quantization getting too compressed to use. I’m already swapping a tiny bit and simply cannot afford the overhead.

I think containers may slow down CPU<->GPU transfers slightly, but don’t quote me on that.

I dunno, still feels like a honeypot to me, lol.

100% a parody instance: https://maga.place/post/2245?scrollToComments=true

Even mention Epstein+Trump like that would be a site-wide shadowban posted on /r/conservative.

It’s good though. It’s plausibly pro trump.

Yeah. But it also messes stuff up from the llama.cpp baseline, and hides or doesn’t support some features/optimizations, and definitely doesn’t support the more efficient iq_k quants of ik_llama.cpp and its specialzied MoE offloading.

And that’s not even getting into the various controversies around ollama (like broken GGUFs or indications they’re going closed source in some form).

…It just depends on how much performance you want to squeeze out, and how much time you want to spend on the endeavor. Small LLMs are kinda marginal though, so IMO its important if you really want to try; otherwise one is probably better off spending a few bucks on an API that doesn’t log requests.

In case I miss your reply, assuming a 3080 + 64 GB of RAM, you want the IQ4_KSS (or IQ3_KS, for more RAM for tabs and stuff) version of this:

https://huggingface.co/ubergarm/GLM-4.5-Air-GGUF

Part of it will run on your GPU, part will live in system RAM, but ik_llama.cpp does the quantizations split and GPU offloading in a particularly efficient way for these kind of ‘MoE’ models. Follow the instructions on that page.

If you ‘only’ have 32GB RAM or less, that’s tricker, and the next question is what kind of speeds do you want. But it’s probably best to wait a few days and see how Qwen3 80B looks when it comes out. Or just go with the IQ4_K version of this: https://huggingface.co/ubergarm/Qwen3-30B-A3B-Thinking-2507-GGUF

And you don’t strickly need the hyper optimization of ik_llama.cpp for a small model like Qwen3 30B. Something easier like lm studio or the llama.cpp docker image would be fine.

Alternatively, you could try to squeeze Gemma 27B into that 11GB VRAM, but it would be tight.

How much system RAM, and what kind? DDR5?

ik doesn’t have great documentation, so it’d be a lot easier for me to just point you places, heh.

At risk of getting more technical, ik_llama.cpp has a good built in webui:

https://github.com/ikawrakow/ik_llama.cpp/

Getting more technical, its also way better than ollama. You can run models way smarter than ollama can on the same hardware.

For reference, I’m running GLM-4 (667 GB of raw weights) on a single RTX 3090/Ryzen gaming rig, at reading speed, with pretty low quantization distortion.

And if you want a ‘look this up on the internet for me’ assistant (which you need for them to be truly useful), you need another docker project as well.

…That’s just how LLM self hosting is now. It’s simply too hardware intense and ad hoc to be easy and smart and cheap. You can indeed host a small ‘default’ LLM without much tinkering, but its going to be pretty dumb, and pretty slow on ollama defaults.

Because however one feels about blockchain tech and its future, past companies within the crypto industry are notorious for selling the moon, being shady, and cashing out early. ‘ZCash’ appears to be a good example, particularly because a small group exerts such a high level of control over it.

And if the parallel holds, and at least some of that applies Jay Gaeber’s own personal experience and expectations of what a company’s trajectory should look like, it doesn’t bode well for Bluesky.

Mobile 5090 would be an underclocked, binned desktop 5080, AFAIK:

https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_processing_units#GeForce_50_series

In KCD2 (a fantastic CryEngine game, a great benchmark IMO), at QHD, the APU is a hair less half as fast. For instance, 39 FPS at QHD vs 84 FPS for the mobile 5090:

https://www.notebookcheck.net/Nvidia-GeForce-RTX-5090-Laptop-Benchmarks-and-Specs.934947.0.html

https://www.notebookcheck.net/AMD-Radeon-8060S-Benchmarks-and-Specs.942049.0.html

Synthetic benchmarks between the two

But these are both presumably running at high TDP (150W for the 5090). Also, the mobile 5090 is catastrophically overpriced and inevitably tied to a weaker CPU, whereas the APU is a monster of a CPU. So make of that what you will.

Oh wow, that’s awesome! I didn’t know folks ran TDP tests like this, just that my old 3090 seems to have a minimum sweet spot around that same same ~200W based on my own testing, but I figured the 4000 or 5000 series might go lower. Apparently not, at least for the big die.

I also figured the 395 would draw more than 55W! That’s also awesome! I suspect newer, smaller GPUs like the 9000 or 5000 series still make the value proposition questionable, but still you make an excellent point.

And for reference, I just checked, and my dGPU hovers around 30W idle with no display connected.

Eh, but you’d be way better off with an X3D CPU in that scenario, which is both significantly faster in games, about as fast outside them (unless you’re dram bandwidth limited) and more power efficient (because they clock relatively low).

You’re right about the 395 being a fine HTPC machine by itself.

But I’m also saying even an older 7900, 4090 or whatever would be way lower power at the same performance as the 395’s IGP, and whisper quiet in comparison. Even if cost is no object. And if that’s the case, why keep a big IGP at all? It just doesn’t make sense to pair them without some weirdly specific use case that can use both at once, or that a discrete GPU literally can’t do because it doesn’t have enough VRAM like the 395 does.

Eh, actually that’s not what I had in mind:

• Discrete desktop graphics idle hot. I think my 3090 uses at least 40W doing literally nothing.

• It’s always better to run big dies slower than small dies at high clockspeeds. In other words, if you underclocked a big desktop GPU to 1/2 its peak clockspeed, it would use less than a fourth of the energy and run basically inaudible… and still be faster than the iGPU. So why keep a big iGPU around?

My use case was multitasking and compute stuff. EG game/use the discrete GPU while your IGP churns away running something. Or combine them in some workloads.

Even the 395 by itself doesn’t make a ton of sense for an HTPC because AMD slaps so much CPU on it. It’s way too expensive and makes it power thirsty. A single CCD (8 cores instead of 16) + the full integrated GPU would be perfect and lower power, but AMD inexplicably does not offer that.

Also, I’ll add that my 3090 is basically inaudible next to a TV… key is to cap its clocks, and the fans barely even spin up.

Rumor is it’s successor is 384 bit, and after that their designs are even more modular:

https://www.techpowerup.com/340372/amds-next-gen-udna-four-die-sizes-one-potential-96-cu-flagship

Hybrid inference prompt processing actually is pretty sensitive to PCIe bandwidth, unfortunately, but again I don’t think many people intend on hanging an AMD GPU off these Strix Halo boards, lol.