LocalLLaMA

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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.

founded 2 years ago

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pax@sh.itjust.works

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Smokeydope@lemmy.world

MonsterBug@sh.itjust.works

What are your LocalLLaMA "hot takes"? (lemdro.id)

submitted 2 weeks ago by mudkip@lemdro.id to c/localllama@sh.itjust.works

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Or something that goes against the general opinions of the community? Vibes are the only benchmark that counts after all.

I tend to agree with the flow on most things but my thoughts that I'd consider going against the grain:

QwQ was think-slop and was never that good
Qwen3-32B is still SOTA for 32GB and under. I cannot get anything to reliably beat it despite shiny benchmarks
Deepseek is still open-weight SotA. I've really tried Kimi, GLM, and Qwen3's larger variants but asking Deepseek still feels like asking the adult in the room. Caveat is GLM codes better
(proprietary bonus): Grok 4 handles news data better than GPT-5 or Gemini 2.5 and will always win if you ask it about something that happened that day.

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[–] domi@lemmy.secnd.me 2 points 2 weeks ago

I can’t buy salami in the supermarket and justify it by saying the cow is dead anyways and someone already sliced it up. It’s down to demand and that’s really complex.

You pay for the salami and thus entice them to make more. There is monetary value for them in making more salami.

Does Mark Zuckerberg really gift an open-weights model to me out of pure altruism?

I don't really know why they initially released their models but at least they kicked off a pissing contest in the open weight space on who can create the best open model.

Meta has not released anything worthwhile in quite a while. It's pretty much Chinese models flexing on American models nowadays.

Still, their main incentive to train those models lies with businesses subscribing to their paid plans.

However that’s not the entire story either, we still buy the graphics cards from Nvidia and we also set free some CO2 when doing inference, even if we didn’t pay for the training process.

True, I exclusively run inference on AMD hardware (I recently got a Strix Halo board) so at least I feel a little bit less bad and my inference runs almost purely on solar power. I expect that is not the norm in the local AI community though.

If I use some research mode from one of the big AI services, it’ll randomly google things, but some weird blog post or a wrong reddit comment will show up on the same level as a reputable source.

I rarely use the commercial AI services but also locally hosted the web search feature is not really that great.

It’s awesome to sift through documentation, though. Or a company’s knowledgebase. And I think those are the real use-cases for RAG.

Yes, I prefer to use RAG with information I provide. For example, ask a question about Godot and provide it the full Godot 4 documentation with it.

Still working on getting this automated though. I would love to have a RAG knowledge base of Wikipedia, Stackoverflow, C documentation, etc. that you can query an LLM against.