• Tattorack@lemmy.world
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    2 hours ago

    The Linux driver support for Crescent Island has been coming along well

    What about Alchemist? I got a A770 and I’m starting to feel left behind now that the A series is having issues that don’t exist in the B series. :(

    The A770 is supposed to be still more power spec-wise on paper than what’s currently available for Battlemage…

  • IchNichtenLichten@lemmy.wtf
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    4 hours ago

    Are these going to sit in a Taiwanese warehouse next to all the Nvidia compute that’s been bought and not installed?

  • Die4Ever@retrolemmy.com
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    7 hours ago

    yay!

    enterprise-focused, inference-optimized GPU accelerators

    The Intel GPU D Series is geared for agentic AI with 480GB of LPDDR5X per card

    Oh, dammit

    • gravitas_deficiency@sh.itjust.works
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      5 hours ago

      Jesus that is bonkers. 480GB on one GPU? I get why (model size and context headroom)… but that’s gonna be closing in on $100k (MSRP of an NVDA B300 - iirc their current top-end - is $50-60k, and that only (hah) brings 288GB).

      Tangentially: I really wish the big US LLM players would stop fucking brute forcing their development by throwing silicon at everything. It seems embedded/austere/efficient coding techniques and algorithms are just being straight up discarded and forgotten. Tbh, this is a pattern I’ve observed as a slow creep since the advent of AWS and The Cloud a couple decades ago - the “AI” bubble is just accelerating the trend. The narrative (irritatingly parroted by my manager) is always “make it work, then make it efficient later” - but it’s always put in the tech debt bucket, so “later” is actually “never”. China is absolutely running way ahead of the US these days in terms of nurturing incisive and efficient engineering culture - it’s not even close. The fact that it’s essentially borne out of ITAR restrictions on the most advanced GPU tech is incidental; really, in a pretty significant way, the US kinda unintentionally did them a favor by constraining their hardware tier, because it forced them to write better code to make up for it. And they have been writing better code for a while now, especially in the LLM domain.