chessgecko

I think people might prefer the lower idle power consumption from lpddr over the better bandwidth in hbm in battery powered stuff. That said right now the price is definitely preventing us from finding out.

pretty sure its lpddr not hbm.

The real issue is that housing is heavily underweighted in the cpi basket. How many people do you know that are only spending 12.9% of their after tax take home on housing, water and fuel? Only people with paid off mortgages.

My girlfriend loves it. She’s an immigrant from Kazakhstan and apparently the Kazakh community on Threads is very active. Reminds her of home.

if you bought a nvidia h100 at wholesale prices (around $25k) and ran it 24/7 at commercial electric rates (lets say $0.1 per kwh), then it would take you over 40 years to spend the purchase price of the gpu in electricity. Maybe bump it down to 20 for data center cooling.

I don't think the cost of the ai is close to converging to the price of power yet. Right now its mostly the price of hardware and data center space minus subsidies.

Actually since the qe to fix 2008 foreign investors/governments cut down on bond buying. Treasury has foreign bond holdings up about 2T since 2014 and current account deficits sum to around 7T.

Nowadays we're mostly selling companies and real estate. (though there was a nice jump in bond buying a few years when rates went up)

Or maybe a lot of it can't deliver returns in line with the current price. The world realizes that and stops sending us stuff for those investments. Then we're stuck with limited manufacturing, high inflation and relatively low ownership of our existing high value exports.

If the pie gets bigger too slowly then I think we still lose.

1800 on the h100s is with 2/4 sparsity, it’s half of that without. Not sure if the tpu number is doing that too, but I don’t think 2/4 is used that heavily so I probably would compare without it.

I remember reading that it’s too hard to get good memory bandwidth/l2 utilization in the fancy algorithms, you need to read contiguous blocks and be able to use them repeatedly. But I also haven’t looked at the gpu blas implementations directly.

I guess it's hard to know how well this would compete with integrated gpus, especially at a reasonable pricepoint. If you wanted to spend $4000+ on it, it could be very competitive and might look something like nvidias grace-hopper superchip, but if you want the product to be under $1k I think it might be better just to buy separate cards for your graphics and ai stuff.

I think we don't really disagree, I just think that this shape isn't really a gpu its just a cpu because it isn't very good for graphics at that point.

For ai inference you definitely have other options, but for low end graphics? the lpddr that apple (and nvidia in grace) use would be super expensive to get a comparable bandwidth (think $3+/gb and to get 500GB/sec you need at least 128GB).

And that 500GB/sec is pretty low for a gpu, its like a 4070 but the memory alone would add $500+ to the cost of the inputs, not even counting the advanced packaging (getting those bandwidths out of lpddr needs organic substrate).

It's not that you can't, just when you start doing this it stops being like a graphics card and becomes like a cpu.

GDDR isnt like the ram that connects to cpu, it's much more difficult and expensive to add more. You can get up to 48GB with some expensive stacked gddr, but if you wanted to add more stacks you'd need to solve some serious signal timing related headaches that most users wouldn't benefit from.

I think the high memory local inference stuff is going to come from "AI enabled" cpus that share the memory in your computer. Apple is doing this now, but cheaper options are on the way. As a shape its just suboptimal for graphics, so it doesn't make sense for any of the gpu vendors to do it.