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Joined 8 months ago
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Cake day: March 22nd, 2024

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  • My level of worry hasn’t lowered in years…

    But honestly? Low on the totem pole. Even with Trumpy governments.

    Things like engagement optimized social media warping people’s minds for profit, the internet outside of apps dying before our eyes, Sam Altman/OpenAI trying to squelch open source generative models so we’re dependent on their Earth burning plans, blatant, open collusion with the govt, everything turning into echo chambers… There are just too many disasters for me to even worry about the government spying on me.

    If I lived in China or Russia, the story would be different. I know, I know. But even now, I’m confident I can given the U.S. president the middle finger in my country, but I’d really be more scared for my life in more authoritarian strongman regions.







  • Basically the only thing that matters for LLM hosting is VRAM capacity. Hence AMD GPUs can be OK for LLM running, especially if a used 3090/P40 isn’t an option for you. It works fine, and the 7900/6700 are like the only sanely priced 24GB/16GB cards out there.

    I have a 3090, and it’s still a giant pain with wayland, so much that I use my AMD IGP for display output and Nvidia still somehow breaks things. Hence I just do all my gaming in Windows TBH.

    CPU doesn’t matter for llm running, cheap out with a 12600K, 5600, 5700x3d or whatever. And the single-ccd x3d chips are still king for gaming AFAIK.






  • 8GB or 4GB?

    Yeah you should get kobold.cpp’s rocm fork working if you can manage it, otherwise use their vulkan build.

    llama 8b at shorter context is probably good for your machine, as it can fit on the 8GB GPU at shorter context, or at least be partially offloaded if its a 4GB one.

    I wouldn’t recommend deepseek for your machine. It’s a better fit for older CPUs, as it’s not as smart as llama 8B, and its bigger than llama 8B, but it just runs super fast because its an MoE.


  • Oh I got you mixed up with the other commenter, apologies.

    I’m not sure when llama 8b starts to degrade at long context, but I wanna say its well before 128K, and where other “long context” models start to look much more attractive depending on the task. Right now I am testing Amazon’s mistral finetune, and it seems to be much better than Nemo or llama 3.1 out there.




  • Heres a tip, most software has the models default context size set at 512, 2048, or 4092. Part of what makes llama 3.1 so special is that it was trained with 128k context so bump that up to 131072 in the settings so it isnt recalculating context every few minutes…

    Some caveats, this massively increases memory usage (unless you quantize the cache with FA) and it also massively slows down CPU generation once the context gets long.

    TBH you just need to not keep a long chat history unless you need it,.