The raw model scale is not increasing by much lately. AI companies are constrained by what fits in this generation of hardware, and waiting for the next generation to become available. Models that are much larger than the current frontier are still too expensive to train, and far too expensive to serve them en masse.

In the meanwhile, "better data", "better training methods" and "more training compute" are the main ways you can squeeze out more performance juice without increasing the scale. And there are obvious gains to be had there.

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robwwilliams 3 days ago | parent | next [-]

The jump to 1 million token length context for Sonnet 4 plus access to internet has been a game-changer for me. And somebody should remind Anthropic leadership to at least mirror Wikipedia; better yet support Wikipedia actively.

All of the big AI players have profited from Wikipedia, but have they given anything back, or are they just parasites on FOSS and free data?

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xnx 3 days ago | parent | prev [-]

> AI companies are constrained by what fits in this generation of hardware, and waiting for the next generation to become available.

Does this apply to Google that is using custom built TPUs while everyone else uses stock Nvidia?

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ACCount37 3 days ago | parent [-]

By all accounts, what's in Google's racks right now (TPU v5e, v6e) is vaguely H100-adjacent, in both raw performance and supported model size.

If Google wants anything better than that? They, too, have to wait for the new hardware to arrive. Chips have a lead time - they may be your own designs, but you can't just wish them into existence.

	▲	xxpor 3 days ago \| parent [-]
		Aren't chips + memory constrained by process + reticle size? And therefore, how much HBM you can stuff around the compute chip? I'd expect everyone to more or less support the same model size at the same time because of this, without a very fundamentally different architecture.