LocalLLaMA

localllama@sh.itjust.works

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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.

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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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OpenAI banned a user who reverse engineered his Samsung TV (discuss.tchncs.de)

submitted 2 weeks ago by Shatur@discuss.tchncs.de to c/localllama@sh.itjust.works

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Hacker reverse engineers his Samsung TV and finds on-device AI listening for your dog and baby, watching what you watch, and borrowing your soundbar's mic if the TV doesn't have one. Then OpenAI banned him for reverse-engineering it.

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[–] BeefAndPoultry@lemmus.org 3 points 2 weeks ago

With Strata, things have gotten more flexible. Similar optimizations are coming to llama.cpp eventually.

https://github.com/Niko1221/Strata/blob/main/docs/MODELS.md#pick-by-memory

It says Qwen 3.8 Flash Next can run with just 8GB VRAM and 96GB RAM for a good quant of it. This is a really good model, and the new version 4 should be coming in the next month or so, probably the same size but smarter.

DeepSeek v4.1 Flash is probably even smarter, says it needs 12+GB VRAM and 64GB of RAM. For decent speed I think you'll want at least 32GB VRAM and 256GB RAM for this one. 96GB VRAM should be very good.

You're gonna have big diminishing returns chasing for even bigger models, partly because Strata doesn't support anything bigger and llama.cpp is currently not nearly as optimized. There might be an inference engine I don't know about for the biggest models.

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