Thread

1. N_Lens · 1789618282 · focus · HN ↗

   The big companies are insulated because they've locked in multi-year supply contracts with ram manufacturers - Microsoft, Google, Meta, Amazon (All on 3-5 yr contracts). OAI's deal with Samsung & SK Hynix is also colossal - locking up roughly 900,000 DRAM wafers per month, roughly 40% of world output (Stargate project).

   Apple got caught out because it had a shorter contract that ended in the beginning of Q3, and they've tried to source their RAM from Chinese CXMT who declined because all capacity was already locked in contracts. They've gone with a lesser known company Kioxia.

   Overall the consumer segment is completely neglected, companies don't care about end users in the current market conditions.

   1. josephg · 1789618707 · focus · HN ↗

      > OAI's deal with Samsung & SK Hynix is also colossal - locking up roughly 900,000 DRAM wafers per month, roughly 40% of world output (Stargate project).

      I wonder if they'll, at some point, have enough RAM? Or is this is the new normal? Will models keep scaling with the amount of ram chips openai and anthropic own?

      1. bionhoward · 1789619444 · focus · HN ↗

         Even with efficiency breakthroughs, it would just afford packing more agents per unit of memory. Scaling compute and data keeps paying off, leading to smarter models, and smarter models have more demand even at higher prices because they can accomplish more work at higher quality. Sovereign AI hasn’t even really taken off yet to anywhere near the level it could. That’s going to dramatically increase the number of massive-scale users of AI agents. So no, IMHO they will “never” have “enough.”

         1. epicureanideal · 1789620733 · focus · HN ↗

            At some point though, won't someone be able to extrapolate the demand growth curve, and invest some colossal amount of money into making and selling more RAM chips?

            1. fragmede · 1789621449 · focus · HN ↗

               Yes. China's done just that. Expect those factories to come online within 2-3 years.
         2. cyanydeez · 1789648531 · focus · HN ↗

            Weve hit the sigmoid. Whats scalling is ancillary to the model. The cry for a slowdown is because the open weight models demonstrate the cost of parameter pacling is not work neither inference nor training.

            The assumption about the singularity simply is a delusion with LLMs.

            However, the models do provide a means to improve the harness universe, so that residual will continue to improve perception. Parameter cpunt will stagnate and training wont be justifiable from every angle.

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