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Comment by azeemba | Hacker News Reader

Why weren't crypto gpus running at 100%?

To advertise the maximum possible performance numbers, CPUs and GPUs massively shoot up the power draw for that final few percentage points. If you are worried about actual calculations/$, cutting back on the power will save your energy bill for practically no loss.

When I tested "ECO" mode on my AMD CPU, performance was ~97% of regular mode, and temperatures dropped 5-10C. I think ECO mode drops TDP from something like 100W->65W. Cheaper and cooler to run for basically no cost.

If you are bitcoin mining or selling GPU capacity, you are incredibly conscious of your energy bill, so it only makes sense to optimize the power draw.

Last point should also be for llm. Not just crypto

Not true. A B200 "only" draws 1 kW. At 16¢ USD / kWh that's $1400 / year. When the hardware costs multiple tens of thousands you might be looking at a yearly electricity bill of ~3% of that. And that's before accounting for the datacenter itself.

The difference is that much of the hardware crypto was running on was dirt cheap in comparison. Add to that a lot of crypto was being run on discount gear in cobbled together setups. Some people were even stealing electricity which often involved installing large clusters of relatively cheap devices in places without proper cooling.

Capex != Opex

Your are measuring two very different things.

And you are only considering electricity bills for the GPU. Higher TDP also means more heat which means more cooling.

It's not really easy to make an off hand comment about costs. A 30-40% decreaae in electricity usage and heat output for a fleet of GPUs definitely makes a massive difference to your opex bottom line.

And the change in failure rate and technical advances are a de-facto change in actual depreciation vs. accounting depreciation.

The datacenter will already have been built with an explicit cooling budget. Moving 1 watt of heat requires substantially less than 1 watt to do the work. So you've got a constant multiplier against something like a 9% annual failure rate and an order of magnitude difference in cost.

Electricity gets converted to heat which has to be removed through the cooling system.

Performance per Watt is the key metric for data centers full of GPUs.

That just shows up as a constant factor, accounted for at build out. Consider if each watt dissipated required an additional watt. That's clearly an absurd overestimate yet it would "only" double power costs making the example $2800 / year. Underestimate the B200 at $30k USD and you've got 10.5 years of operation before you achieve parity as a fairly unrealistic worst case estimate. Now consider that at a 9% failure rate the average lifespan is only ~11 years. So electricity (both operation and cooling) is pretty much guaranteed to be less than half your total operating cost no matter how you slice it.

Most crypto mining on GPUs would use 100% of memory bandwidth, but only a fraction of the compute available. This is a consequence of ASIC resistance of their mining algorithms -- custom silicon can only offer a modest benefit over GPUs if the hard part is memory bandwidth.

Why is that true? Can’t you just make more stuff parallel and shrink the ASIC chips accordingly?

No - inability to do so is part of the design of a good cryptographic hash, quite explicitly.

https://en.wikipedia.org/wiki/Avalanche_effect

So does llm inference. You're lucky if you hit 40% of the advertised flops.

Right, but datacenter GPUs optimized for LLM training/inference would have a bandwidth:compute ratio scaled to that workload.

No they don't.

Why not? Seems like they would be poorly optimised?

Because llm inference is not the only workload a GPU can do and custom silicon cost $10b a chip.

AI optimised cards is the biggest cashcow for nvidia. They are definitely doing custom silicon. And Google et al have cards that don't even pretend to be able to do graphics.

There is more than one workload in AI. Inference for llms is memory constrained on even a single card. Training for llms is memory constrained on the level of racks. In both cases you hardly ever see more than 40% of advertised flops used.

So the question is: why are they designing AI cards so unbalanced?

Then where are the HDMI ports on Nvidia's current data center GPU product lines?

Most were powerlimited to a degree to get the most hash/watt out of them.

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