Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
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Re: Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
#2Stupid metric. It's not because a model is better performing that it necessarily requires more energy or compute.
Re: Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
#3> We propose intelligence per watt (IPW), task accuracy per unit of power Stupid metric. It's not because a model is better performing that it necessarily requires more energy or compute.
Re: Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
#4> We propose intelligence per watt (IPW), task accuracy per unit of power Stupid metric. It's not because a model is better performing that it necessarily requires more energy or compute.
Stupid metric. It‘s not because you spend more time that you travel farther.
s/
Re: Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
#5That’s surprising, almost unbelievable, due to batching. Local is usually not batched.
Re: Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
#6Unless I misread it, are they saying local GPUs use less energy? That’s surprising, almost unbelievable, due to batching. Local is usually not batched.
Re: Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
#7Re: Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
#8> We propose intelligence per watt (IPW), task accuracy per unit of power Stupid metric. It's not because a model is better performing that it necessarily requires more energy or compute.
I don't think that's what they are saying. In fact if they did the metric would be pointless. Rather they are saying by estimating that value on different architectures, one can find more efficient ones. They use open model to be able to remove unknowns. They aren't advocating for one model or another, only more efficient architectures.
Re: Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
#9Re: Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
#10Unless I misread it, are they saying local GPUs use less energy? That’s surprising, almost unbelievable, due to batching. Local is usually not batched.
You misread it. From the abstract:
> local accelerators achieve at least 1.4× lower IPW than cloud accelerators running identical models
That's "intelligence per watt". They also have IPJ, per Joule.
So, they find local is 40% "dumber" than cloud for the same power or 40% more power for the same "intelligence".
Tables 13 and 14 summarize their IPW and IPJ metrics.
But, to your actual point, I think the "local is 40% dumber per watt than cloud" message is still an understatement. And maybe this is something I failed to find in the paper but they seem to ignore the "idle baseline" costs and talks about explicitly focusing on the power consumption of just the accelerator under load.
There is a large baseline power consumption just to support the accelerator. CPUs, memory, PS losses, network, fans, general environment cooling. This "cost floor" is different for data centers and a "random local computer" and I think must be in favor of data centers which are designed and built with efficiency in mind.
Idleness should also be considered. My local GPUs at $WORK and home are idle more than they are used. Idle time energy in real world scenarios should be somehow attributed to those brief, punctuated times when LLM functions are actually active on the accelerator. Actual, local LLM usage of a GPU is brief (assuming one user per PC). Even with my heavy usage developing s/w I'd guess I heat up a GPU about one hour per day total, sometimes much less. If that is local then one must pay 23 hours of idleness for that 1 hour of "intelligence". Of course a local PC is used for other things and the idleness penalty must somehow account for that. OTOH, data centers try to maximize utilization so their idle time penalty would be much less, perhaps close to zero, by construction.