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Joined 5 months ago
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Cake day: March 24th, 2026

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  • any actually useful real world tasks you need at least 24GB you can dedicate to the LLM alone, and if this is fully GPU vRAM

    Agreed. For anyone who doesn’t have a 24GB card, the AMD 7900 XTX is basically the only sanely priced 24GB GPU left in 2026 and can be had for under a grand new, whereas even a used 3090 is $1200 and $2000 new.

    Qwen 3.8 27B runs nicely on my XTX. I have to use Claude at work, and Qwen 3.8 in llama.cpp with the OpenCode desktop app leaves nothing to be desired in comparison. 3.8 27B is about on par with Sonnet 5 according to benchmarks, though I even prefer it over Opus because Qwen writes in clear, understandable language. We are at the point now where anyone with a 24GB GPU has little reason to use data center LLMs.











  • melfie@lemmy.ziptoTechnology@lemmy.worldAI;DR (AI; Didn’t Read)
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    10 days ago

    It makes sense that if you can’t be bothered to write it, then I can’t be bothered to read it.

    Also, the term “AI slop” puts the blame on a machine instead of the lazy human who doesn’t give a shit and can’t be bothered to so much as proofread a machine’s output based on their half-assed input before subjecting other humans to it and wasting their time.






  • The emergent behavior in huge models where it can “reason” instead of simply predicting the next token is fascinating. Artificial “neurons” built on statistics and linear algebra emerge to create something that legitimately has artificial intelligence. ANNs were conceived in the 1940s, building on centuries of development in statistical modeling and only now do we have the compute power to make this vision a reality.

    Yes, the “intelligence” has significant limitations and won’t be replacing human intelligence anytime soon, but it can actually be a useful tool if its limitations are kept in mind.

    The problem is of course the tech bros turning centuries of innovation they had no part in developing into a massive Ponzi scheme for their own profit.