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Instead of time it should be energy. What is the best model you can train with a given budget in Joules. Then the MBP and the H100 are on a more even footing.
by zarzavat 1y ago
Instead of time it should be energy. What is the best model you can train with a given budget in Joules. Then the MBP and the H100 are on a more even footing.
- NooneAtAll3 1y agoit's not about efficiency - it's about availability H100 is not an everyday product. Laptop is
- KeplerBoy 1y agoStill, I don't think the m4 is going to be far off from the h100 in terms of energy efficiency. edit: fixed typo
- Der_Einzige 1y agoAt this point, given how many H100s there are in existence, it’s basically an everyday product.
- logicchains 1y agoI envy you if $25k is an everyday product cost.
- jeroenhd 1y agoFor what it's worth, most of the world can't afford an M4 Macbook either.
- wongarsu 1y agoAnd renting an H100 for an hour is a lot easier than renting an M4 MacBook for an hour.
- falcor84 1y agoMaybe not to buy one, but to rent one. Like how barista-made coffee is an everyday product even though most people can't afford a fancy professional coffee machine.
- bee_rider 1y agoReasonably high quality coffee machines are very widespread. Or you can do pour-over. I don’t think the cost of a machine is a limiting factor for many people, it is just convenience. Maybe an analogy could be made to espresso, nice espresso machines get costlier. But, you can still get quite good results out of a manual machine like a Flair. I think this is why the suggestion to rent a machine is not to helpful. In this analogy we’re on BaristaNews, we all know about the industrial machines, lots of folks use them at work. But, the topic of what sort of things you can do on your manual machine at home has come up.
- inetknght 1y ago> Reasonably high quality coffee machines are very widespread. Or you can do pour-over. I don’t think the cost of a machine is a limiting factor for many people No, reasonably-priced coffee machines is an enabling factor for many people. If coffee machines weren't reasonably priced, they would not be "very widespread".
- bee_rider 1y agoI’m not sure I follow your deeper meaning here, sorry.
- Sharlin 1y agoH100s are almost-instantly available to anyone with a credit card and access to the internet. Without even having to lift their butt from the seat. And you get plenty more than five minutes of compute for the price of an M4.
- jsperson 1y agoFor the orgs where I've worked the important thing isn't availability of compute it's security. Using what we have on our local network is much easier from a governance and approval standpoint than whatever is available on the internet.
- Sharlin 1y agoMany orgs have no problems using cloud envs for most things. The usual suspects offer just as secure compute envs as everything else. Anyway, I was assuming personal use, like the messing-around experimenting that the article is about. (Or who knows, maybe it was part of the author’s job.)
- potatolicious 1y agoAnd yet just about any intro-to-programming tutorial gets something running on your local machine, and local machine development continues to be the default for most people, even though devving on a cloud machine is eminently reasonable. "Pull out credit card, sign up for some thing and pay a bit of money" is a non-trivial bit of friction! Extremely non-trivial! Especially in a corporate context - you have to get the expense approved. It's not clear if you can put company data onto the machine. Whereas generally running local things on corporate laptops is far less controversial. "Download this tool and run it." is still an extremely powerful pitch. Pretty much the only thing that beats it is "go to this website which you can use without any signup or payment".
- Sharlin 1y agoSure, if you already have said local machine. Which I guess in HN’s context many/most do.
- nickpsecurity 1y agoAlso, my laptop running Linux and its outputs are probably mine and private. If I use cloud GPU's, I need to be a lawyer to be sure what they can or can't do with my data or models. There's also no overages or hidden charges with a laptop. Past simply breaking it. You know the replacement cost ahead of time, though.
- giancarlostoro 1y agoMac is more competitive on power consumption though since its not ever pulling as much as a Nvidia GPU is my understanding. On that note you can rent an H100 for an hour for under $10 which might make for a slightly more interesting test, whats the best model outcome you can train in under an hour.
- dtnewman 1y ago> you can rent an H100 for an hour for under $10 Far cheaper these days. More like $2-3 for a consumer to do this. For bulk deals, pricing is often < $2.
- giancarlostoro 1y agoI couldnt remember offhand the exact amount but figured noting that under $10 is still impressive for one high end GPU for an entire hour.
- bigyabai 1y agoIt depends. If you're bottlenecked by memeory speed, the Mac typically comes out on-top. In terms of conpute efficiency though, Nvidia still has Apple beat. Nvidia wouldn't have the datacenter market on a leash if Apple was putting up a real fight.
- giancarlostoro 1y agoYeah, this is correct. My 3080 will render quicker than my M4 but my M4 will outcompete on being able to load larger models.
- netcan 1y agoThey're all good. Being somewhat arbitrary isnt a bad thing.
- jvanderbot 1y agoBro por que no los dos We can / should benchmark and optimize this to death on all axes
- motorest 1y ago> Instead of time it should be energy (...) Then the MBP and H100 are on a more even footing. What exactly is your point? That instead of expressing workloads in terms of what a laptop could do, you prefer to express them in terms of what a MacBook Pro could do?
- zarzavat 1y agoThe point is that "best model you can train in 5 minutes" is hardware dependent, the answer will be different depending on the hardware available. So it's necessarily a single-player game. "Best model you can train with X joules" is a fairer contest that multiple people could take part in even if they have different hardware available. It's not completely fair, but it's fair enough to be interesting. Training models with an energy limit is an interesting constraint that might lead to advances. Currently LLMs implement online learning by having increasingly large contexts that we then jam "memories" into. So there is a strict demarcation between information learned during pre-training and during use. New more efficient approaches to training could perhaps inform new approaches to memory that are less heterogenous. tl;dr: more dimensionally correct
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