Azantys

When models are in the hundreds of gigabyte range, there are not enough nodes to train, because most people dont have 100k in AI hardware. Also there is no training method for machines scattered around the world as far as I know.

Or they suffer from skill atrophy by overreliance on LLMs.

AMD driver support is great due to community effort, even for 10+ year old cards. Nvidia also now has (partly?) open drivers.

The whole point was for the formalization to be clean enough so it could be reused in other parts of mathematics as I understand it. 13M lines of AI slop which have never been checked do not sound like what the original goal for such a formalization was. Also Claude didnt prove anything it just translated an already existing proof by Wiles into Lean, so it didn't actually contribute anything other than "Guys we did this thing, look how great our model is!". We never questioned that a printer can print faster than a human can write, but we dont let printers write novels.

Yes, but you can use local models with opencode/pi

Its 27B not 37B and having just 27B in total and 3T and like 30B active of those is still totally different. A 120B with 5B active is still much slower than a proper 5B. Just like the new Ling 3.0 Tiny with 8B and 1B active only gets around 120tk/s compared to 250tk/s which a real 1B one gets on my hardware.

At one point there were specific -Coder release of Qwen e.g. 2.5 but they dropped that, still wondering how much better 3.6-Coder or 3.8-Coder would be when they ignore everything else

Benchmarks are basically tied https://artificialanalysis.ai/models/comparisons/deepseek-v4...

Dysfunctional vibeslop

Here you have shown yourself that progress slows down and doesnt speed up. 8.9/0.35 = ~25x more performance in 10 years from 2006 to 2016. 104.8/8.9 = ~12x more performance in 10 years from 2016 to 2026. Growth has dropped 50%.

You already can with AMD/Intel gpus. Kraid is a compiler for Arm Mali which is mostly used on mobile stuff, nothing AI related.

I questioned the speed not the output quality, that is another discussion

? Readme says 60-70s per token

0.01 tk/s is unusable for anything, you would wait a whole day for just 1000 token of output, what is the point of projects like this?

Free month is insane for a bit of downtime

Just looking with ncdu/du or another tool which sorts folders by size would have been much faster and easier, was there any need to use Claude here?

If you could render fully in OpenCL and output it via another device it would probably work. But they dont have ROPS so they cant do traditional video rendering. Even something like Blender where you would use it for calculations and not for display/video doesnt work without software emulating hardware features related to textures.

It really depends on the language, popular languages work pretty good

You are comparing a 35B models to a 635B+ frontier model, of course thats not even close

Old datacenter GPUs could game, but new ones can only do OpenCL/CUDA/ROCm etc. and have no display out. Im using an MI50 right now and I would wish newer datacenter cards could also be used for everything like them.

Useless comment

The wording wasnt very good I ment compared to programming or math the amount of logic and reasoning is small (Research level math hardly compares to writing a book in raw reasoning and logic). And I thing the smaller models have enough "intelligence" to write coherent with logical world building, but only the big models can truly do hard math and programming work

Of course there is logic but its nowhere near the complexity of math or programming

Do people really use 100B+ models for writing? I am no writer but to me it seems like writing is one of the easiest tasks with barely any logic or reasoning and as long as its not longer than a handful of pages I expect even 8B models to perform great.

I think model training is pretty hard to do efficiently on a vastly distributed network. If the model cant fit into the VRAM of the node your performance becomes so bad its useless, so a distributed model could only be properly trained if the size of the model doesnt exceed the majority of the nodes VRAM sizes. Maybe there is a different way of doing training but this would be the only way I can see. And it would still be much worse than just using a big datacenter where everything is fully interconnected. BOINC projects work great because its usually just a lot of small compute and memory required so every old desktop and laptop can contribute. Training a model which can compete and is not tiny requires neither low compute or low memory amount. BOINC tasks take minutes usually or sometimes hours but not weeks or months like training a model from scratch. But something like 7B or lower could maybe be trained like this. Im not sure but I think someone is already working on something like this but I dont remember the name of the project.

+1

Game studios should choose CryEngine/Decima again over UE5

Just dont upgrade the Mainboard firmware then

Yeah but it wasnt close to Opus etc. Still a good local model when it released

Galaxy AI 3.8-Flash-Plus Max (xhigh)