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Today's Cheap AI Services Won't Last

vincentschmalbach.com

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Re: Today's Cheap AI Services Won't Last

#21

Post by vincent_s »

londons_explore wrote: post #2

> Price increases: As VC funding dries up and companies face pressure to turn a profit, we'll likely see sharp increases in the cost of AI services. I disagree. Yes, the VC funding will dry up, but hardware and algorithmic advances will decrease running costs by equal amounts. Lack of a moat will prevent companies recouping past expenses, since any who try will be outcompeted by new market entrants who don't have tho…

1. These companies have burned through billions in VC funding. Just breaking even won't be enough - they'll need significant profits to justify those investments.

2. We have at least one real-world example suggesting profitability is challenging. Microsoft is reportedly still losing money on GitHub Copilot, even after years of operation and many paying customers.

Re: Today's Cheap AI Services Won't Last

#22

Post by hexo »

Ukv wrote: post #20
hexo wrote: post #15

Of course. At current trend its going to consume ridiculous amount of electricity for no good reason (and make some more CO2). Funny that 60W old school light bulb is bad bad bad, but 400W gfx card is ok and armies of 3kW servers to produce bs responses to queries are also ok. Consuming ridiculous kilowatts to produce even more bs video is also "no problemo". Hm. Poor artists, I can see why they're furious about this…

> At current trend its going to consume ridiculous amount of electricity for no good reason (and make some more CO2). 5 joules per token[0] * 200 tokens per query * 10 queries per day * 365 days per year = 1.014kWh Which, going by the US's energy mix[1], means a year of someone's LLM usage would be about 0.4kg of CO2 - the same as a single cup of coffee[2]. Worth double-checking, since these are just napkin calculati…

I've read somewhere that half of googles datacenter energy consumption is ai. So I think the real numbers are probably somewhere else. Its not just about one query - its about everything that come with it, including production of hw including price manipulation (that means we [better say companies] have to produce more money [ie. make some more CO2] to even afford the hw in the first place) and soon also energy plants construction - and we can only hope thats not a coal one.

Also, there's that pesky training that is hugely expensive on electricity.

Oh dont get me wrong - there are good uses of neural networks, but the stuff that pushed so much is not that. An example of good one was nvidias sw (rtx voice) to clear up voice during calls, dunno if it exists or not.

Re: Today's Cheap AI Services Won't Last

#23

Post by Ukv »

hexo wrote: post #22
Ukv wrote: post #20

Earlier quoted context omitted.

> At current trend its going to consume ridiculous amount of electricity for no good reason (and make some more CO2). 5 joules per token[0] * 200 tokens per query * 10 queries per day * 365 days per year = 1.014kWh Which, going by the US's energy mix[1], means a year of someone's LLM usage would be about 0.4kg of CO2 - the same as a single cup of coffee[2]. Worth double-checking, since these are just napkin calculati…

I've read somewhere that half of googles datacenter energy consumption is ai. So I think the real numbers are probably somewhere else. Its not just about one query - its about everything that come with it, including production of hw including price manipulation (that means we [better say companies] have to produce more money [ie. make some more CO2] to even afford the hw in the first place) and soon also energy plant…

> I've read somewhere that half of googles datacenter energy consumption is ai

I can't find that figure, or what's being included in it: major Google products like Search and Translate are AI (since at least ~2018, depending on your definition of AI) and have huge numbers of users - I think it'd be hard to deny their utility.

> Its not just about one query [...]

To be clear, I didn't calculate that "one query" was equivalent to a cup of coffee - it was a year's worth of someone's LLM usage.

> Also, there's that pesky training that is hugely expensive on electricity.

I don't believe the coffee comparison took into consideration the one-off costs either, like manufacturing of your coffee machine, but let's consider the amortized emissions of training anyway:

500 tons of CO2[0] over 200,000,000 users[1] means 0.0025kg of CO2 per user, barely shifting from the figure of inference alone. Again, rough calculation - would be lower per-user if taking into account API/3rd-party users, but then higher taking into account more model versions.

Realistically, I don't think someone's CO2 emissions on the order of 0.4kg, whether that's coffee, video streaming/rendering, LLM usage, or so on, should take so much of the focus while we have, for instance, celebrities emitting 8,000,000kg a year[2] from private jets.

[0]: https://foundation.mozilla.org/en/blog/ai-internet-carbon-fo...

[1]: https://a16z.com/how-are-consumers-using-generative-ai/

[2]: https://i.imgur.com/EP0Gbtk.jpeg

Re: Today's Cheap AI Services Won't Last

#24

Post by leereeves »

Ukv wrote: post #20
hexo wrote: post #15

Of course. At current trend its going to consume ridiculous amount of electricity for no good reason (and make some more CO2). Funny that 60W old school light bulb is bad bad bad, but 400W gfx card is ok and armies of 3kW servers to produce bs responses to queries are also ok. Consuming ridiculous kilowatts to produce even more bs video is also "no problemo". Hm. Poor artists, I can see why they're furious about this…

> At current trend its going to consume ridiculous amount of electricity for no good reason (and make some more CO2). 5 joules per token[0] * 200 tokens per query * 10 queries per day * 365 days per year = 1.014kWh Which, going by the US's energy mix[1], means a year of someone's LLM usage would be about 0.4kg of CO2 - the same as a single cup of coffee[2]. Worth double-checking, since these are just napkin calculati…

Both 200 tokens per query and 10 queries per day seem far too low.

Re: Today's Cheap AI Services Won't Last

#25

Post by Ukv »

leereeves wrote: post #24
Ukv wrote: post #20

Earlier quoted context omitted.

> At current trend its going to consume ridiculous amount of electricity for no good reason (and make some more CO2). 5 joules per token[0] * 200 tokens per query * 10 queries per day * 365 days per year = 1.014kWh Which, going by the US's energy mix[1], means a year of someone's LLM usage would be about 0.4kg of CO2 - the same as a single cup of coffee[2]. Worth double-checking, since these are just napkin calculati…

Both 200 tokens per query and 10 queries per day seem far too low.

This entire article is under 500 tokens, and I think over the course of a year most people will balance out occasional long conversations with days where they don't touch it at all.

Can tweak the numbers, but even if you think - say - that the average response length is 2000 tokens (~8000 characters), a year of usage would still just be on the level of a few days' worth of coffee for me.

Unless I've made some major error in the calculations, ~0.4kg just doesn't seem like all that much when in the same timeframe a celebrity will be emitting 8,000,000kg from their private jet.

Re: Today's Cheap AI Services Won't Last

#26

Post by mysterydip »

Ukv wrote: post #25
leereeves wrote: post #24

Earlier quoted context omitted.

Both 200 tokens per query and 10 queries per day seem far too low.

This entire article is under 500 tokens, and I think over the course of a year most people will balance out occasional long conversations with days where they don't touch it at all. Can tweak the numbers, but even if you think - say - that the average response length is 2000 tokens (~8000 characters), a year of usage would still just be on the level of a few days' worth of coffee for me. Unless I've made some major e…

Sounds like the solution is to replace the celebrity with an AI :)

Re: Today's Cheap AI Services Won't Last

#27

Post by karmakurtisaani »

hexo wrote: post #15

Of course. At current trend its going to consume ridiculous amount of electricity for no good reason (and make some more CO2). Funny that 60W old school light bulb is bad bad bad, but 400W gfx card is ok and armies of 3kW servers to produce bs responses to queries are also ok. Consuming ridiculous kilowatts to produce even more bs video is also "no problemo". Hm. Poor artists, I can see why they're furious about this…

> Funny that 60W old school light bulb is bad bad bad, but 400W gfx card is ok

Could be because there actually exists a 10x more efficient alternative for the light bulb.

Re: Today's Cheap AI Services Won't Last

#28

Post by [deleted] »

Ukv wrote: post #25
leereeves wrote: post #24

Earlier quoted context omitted.

Both 200 tokens per query and 10 queries per day seem far too low.

This entire article is under 500 tokens, and I think over the course of a year most people will balance out occasional long conversations with days where they don't touch it at all. Can tweak the numbers, but even if you think - say - that the average response length is 2000 tokens (~8000 characters), a year of usage would still just be on the level of a few days' worth of coffee for me. Unless I've made some major e…

[deleted]

Re: Today's Cheap AI Services Won't Last

#29

Post by leereeves »

Ukv wrote: post #25
leereeves wrote: post #24

Earlier quoted context omitted.

Both 200 tokens per query and 10 queries per day seem far too low.

This entire article is under 500 tokens, and I think over the course of a year most people will balance out occasional long conversations with days where they don't touch it at all. Can tweak the numbers, but even if you think - say - that the average response length is 2000 tokens (~8000 characters), a year of usage would still just be on the level of a few days' worth of coffee for me. Unless I've made some major e…

I see that the paper that reported 5J per token used LLaMA 65B, a relatively small model. ChatGPT 4 reportedly has trillions of parameters

They also didn't measure power use vs context window size, and used single prompts that would be much smaller than conversations (because in a conversation with an LLM, every prompt entered sends the entire conversation to the LLM again.)

Or the much larger context windows of RAG, or programmers sending entire source code archives with every prompt.

As for the number of interactions, a professional using the tool forty hours a week to support their work could easily have a hundred conversations per week (hundreds of prompts total).

There are enough factors here with enough uncertainty that the result could be off by a lot.

That said, I agree that it's unlikely to be worse than a celebrity with a private jet. I can't take anyone who discusses climate change seriously if they don't demand banning private jets.

Re: Today's Cheap AI Services Won't Last

#30

Post by Ukv »

leereeves wrote: post #29
Ukv wrote: post #25

Earlier quoted context omitted.

This entire article is under 500 tokens, and I think over the course of a year most people will balance out occasional long conversations with days where they don't touch it at all. Can tweak the numbers, but even if you think - say - that the average response length is 2000 tokens (~8000 characters), a year of usage would still just be on the level of a few days' worth of coffee for me. Unless I've made some major e…

I see that the paper that reported 5J per token used LLaMA 65B, a relatively small model. ChatGPT 4 reportedly has trillions of parameters They also didn't measure power use vs context window size, and used single prompts that would be much smaller than conversations (because in a conversation with an LLM, every prompt entered sends the entire conversation to the LLM again.) Or the much larger context windows of RAG,…

> I see that the paper that reported 5J per token used LLaMA 65B, a relatively small model. ChatGPT 4 reportedly has trillions of parameters

GPT-4 uses a MoE architecture, composed of many smaller models - (speculated) total parameter count is not particuarly meaningful when the vast majority of its parameters will be in submodels that aren't active for a given query.

In terms of capability, LLaMA-3 is about on par with GPT-4 (in LMSYS arena[0] the earliest GPT-4-0613 has 1161 ELO, Llama-3-70b-Instruct has 1207 ELO, and the latest GPT-4o-2024-05-13 has 1287 ELO).

You could expect LLaMA-3 to reach the same capability with greater efficiency than the earlier GPT-4 versions, as it has the benefit of time leading to algorithmic advances. However, not only does that advantage now fall on the side of the more recent GPT-4 versions, but also OpenAI's inference efficiency will presumably benefit massively from scale and large amounts of infrastructure-specific engineering - opposed to researchers spinning up a model on GPUs from 2017.

So I still believe 5 Joules is approximately fair (especially since many smaller models still see use). But really, even tweaking it upwards by an order of magnitude, it just doesn't seem to amount to all that much.

> and used single prompts that would be much smaller than conversations

I only took their Joules per token estimation.

> As for the number of interactions, a professional using the tool forty hours a week to support their work could easily have a hundred conversations per week (hundreds of prompts total).

So, lets say, 500 prompts total in a week (consulting the LLM every few minutes for the full duration of their work). That's about 3kg of CO2 per year, likely less than the coffee they consumed in one week, or one drive into work.

Initial calculation was intended as a rough average user. A professional may well use an LLM more often than average, and hopefully get higher than average utility from it.

> That said, I agree that it's unlikely to be worse than a celebrity with a private jet.

"unlikely to be worse" is a bit of an understatement. You could use it for 10 million years and still not catch up to half of Taylor Swift's annual private jet usage.

[0]: https://chat.lmsys.org/?leaderboard

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