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Who is buying these at 70% of new pricing given the sky high likelihood of them being shot? Maybe it's safe to buy from small labs that went under quickly, but
by lightbendover 2mo ago
Who is buying these at 70% of new pricing given the sky high likelihood of them being shot? Maybe it's safe to buy from small labs that went under quickly, but I can't imagine a cluster that has been operating near its thermal limits for a couple years fetching that kind of resale.
- threetonesun 2mo agoIt was true of crypto GPUs too, although mostly people picking them up for gaming. Always seems high to me too but if you can get any guarantee of them not being on fire when they were pulled the bathtub curve keeps you pretty safe, thermal limits are limits for a reason.
- rxyz 2mo agocrypto gpus didnt run at 100% power draw so the strain wasn't that massive
- azeemba 2mo agoWhy weren't crypto gpus running at 100%?
- dantillberg 2mo agoMost 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.
- NuclearPM 2mo agoWhy is that true? Can’t you just make more stuff parallel and shrink the ASIC chips accordingly?
- jmalicki 2mo agoNo - inability to do so is part of the design of a good cryptographic hash, quite explicitly. https://en.wikipedia.org/wiki/Avalanche_effect https://en.wikipedia.org/wiki/Avalanche_effect
- noosphr 2mo agoSo does llm inference. You're lucky if you hit 40% of the advertised flops.
- fwipsy 2mo agoRight, but datacenter GPUs optimized for LLM training/inference would have a bandwidth:compute ratio scaled to that workload.
- noosphr 2mo agoNo they don't.
- eru 2mo agoWhy not? Seems like they would be poorly optimised?
- noosphr 2mo agoBecause llm inference is not the only workload a GPU can do and custom silicon cost $10b a chip.
- eru 2mo agoAI optimised cards is the biggest cashcow for nvidia. They are definitely doing custom silicon. And Google et al have cards that don't even pretend to be able to do graphics.
- noosphr 2mo agoThere is more than one workload in AI. Inference for llms is memory constrained on even a single card. Training for llms is memory constrained on the level of racks. In both cases you hardly ever see more than 40% of advertised flops used.
- fishgoesblub 2mo agoMost were powerlimited to a degree to get the most hash/watt out of them.
- 3eb7988a1663 2mo agoTo advertise the maximum possible performance numbers, CPUs and GPUs massively shoot up the power draw for that final few percentage points. If you are worried about actual calculations/$, 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.
- skeptic_ai 2mo agoLast point should also be for llm. Not just crypto
- fc417fc802 2mo agoNot 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.
- jpc0 2mo agoCapex != 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.
- dapperdrake 2mo ago
- metalliqaz 2mo agoI bought a RTX 3070 off a miner when Eth went to proof of stake. It was clean, cheap, and is still going strong for daily gaming. In its working life it was undervolted and probably cooled better than in my rig.
- BizarroLand 2mo agoI got an entire Prebuilt PC when POW ended from a miner. AMD 5950x, 64gb ram, 3090, 2tb SSD, all for $1700. This was 4 years ago when the 3090 was $1500 by itself, and with minor upgrades it's still going strong today. It's wild to think that the system now is worth at least as much as I paid for it then if not much more than that. I saw a similar one going for $2500.
- latchkey 2mo agowe over clocked and under volted... the goal was to lower power and run the clocks as fast as possible.
- Arbortheus 2mo agoI feel like this is not as important as people make it out to be.
- christina97 2mo agoWhy would they be shot? Unlike the consumer cards that are basically factory overclocked to look good on benchmarks, the datacenter GPUs are designed to run at full tilt 24/7 and survive for years.
- Chaosvex 2mo agoGiven the decades of consumer and enterprise GPUs often being identical or near identical hardware, it'd be interesting to see if there's any evidence of this actually being true.
- eru 2mo agoBinning can take in a supposedly uniform stream of chips and produce different tiers on the output.
- bigbuppo 2mo agoAccording to the article these cards have a 9% annual failure rate.
- htrp 2mo ago>The number traces to Meta’s Llama 3 technical report, which documented 419 unforeseen disruptions across 16,384 H100s over 54 days of training, of which 148 were GPU failures and 72 were HBM3 memory failures. From an annualized number on the llama 3 training report. would be interesting to see if we have a better idea given that we're already on rubin.
- christina97 2mo agoIt depends entirely on the time-to-failure distribution though whether used cards are a good deal or not. Often this kind of hardware has a bathtub shaped hazard rate, actually getting burned in cards may mean you get the weeded out solid specimens, and forgo the lemons.
- lmm 2mo agoMost hardware has an exponential (memoryless) failure distribution in practice. The bathtub curve is a myth.
- fwipsy 2mo agoHonest question. Does silicon wear out due to high temperatures, or is it more like lightbulbs where it wears out from thermal cycles? Does it really wear out at all?
- deleted 2mo ago[deleted]
- blobcode 2mo agoIt’s mostly due to higher temps resulting in faster ion migration, which can cause a breakdown in the structure of transistors, as well as increased wear on the conductors (though this is rarely a dominating factor). Thermal cycling can also cause cracking, which is also a problem. Though there are lots of reason chips fail due to heat, from the wire bonds on pads getting too hot to increased leakage current at high temps.
- Xalutiono 2mo agoFor a long time it was assumed it wouldn't wear out and tbh if you look how long it takes, how rare it is, its not a real issue... besides what intel did with Raptor Lake. This series had massive issues with oxidiation. Was the first time ever i became aware of this issue on scale. Nontheless there are papers out there that silicon can degenerate and does.
- high_na_euv 2mo agohttps://research.google/pubs/cores-that-dont-count/ https://research.google/pubs/cores-that-dont-count/