LocalLLaMA
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
Get support from the community! Ask questions, share prompts, discuss benchmarks, get hyped at the latest and greatest model releases! Enjoy talking about our awesome hobby.
As ambassadors of the self-hosting machine learning community, we strive to support each other and share our enthusiasm in a positive constructive way.
Rules:
Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.
Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.
Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.
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I still vastly prefer Qwen3-30B thinking because it answers pretty fast. The speed was really most interesting thing compared to R1 32B. Now that Ollama supports Vulkan it runs even faster (~ 2/3 CPU & 1/3 GPU).
I use it with Page Assist to search the web via DDG, but it would also support SearXNG.
I have Qwen3-Coder 30B for code generation.
I actually mostly use it with Page Assist as well. I have the Continue plugin installed in VSCodium.
The rest I don’t use as much. I have installed
I only have 32GB RAM so I ran those 4B models especially if Firefox and/or other things used to much RAM already. Dunno how much that will change with Vulkan support. It probably will only shift a bit since they can run 100% on my 6GB VRAM GPU now. At least now I can run 4B without checking RAM usage first.
After all all this stuff is nice to run this 100% open source, even when the models aren’t. Especially use them for questions that involve personal information.
I’ve just started to play around with Qwen3-VL 4B since Ollama support was just added the yesterday. It certainly can read my handwriting.
Only other AIs I used recently are:
My hottest take is probably that I hate the use of T for trillion parameters, even though short scale trillion is the same as Terra. I could somewhat live with the B for billion, though it’s already not great. But the larger the numbers become the more ridiculous it gets. I dunno what they’ll use after trillion but it’ll get ugly fast since quadrillion (10¹⁵) and quintillion (10¹⁸) both start with Q. SI-Prefixes have an unambiguous single character for up to quetta (Q; 10³⁰) right now. (Though SI-Prefixes definitively have some old prefixes which break their system of everything >0 having an uppercase single letter: deca, hecto, kilo) Or it’s because it’s an English, but not an international, notation.