Google's AI-powered search summaries use 10x more energy than a standard Google search | The Hidden Environmental Impact of AI
Google's AI-powered search summaries use 10x more energy than a standard Google search | The Hidden Environmental Impact of AI
While the mass adoption of AI has transformed digital life seemingly overnight, regulators have fallen asleep on the job in curtailing AI data centers’ drain on energy and water resources.
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I skimmed the article, but it seems to be assuming that Google's LLM is using the same architecture as everyone else. I'm pretty sure Google uses their TPU chips instead of a regular GPU like everyone else. Those are generally pretty energy efficient.
That and they don't seem to be considering how much data is just being cached for questions that are the same. And a lot of Google searches are going to be identical just because of the search suggestions funneling people into the same form of a question.
Exactly. The difference between a cached response and a live one even for non-AI queries is an OOM difference.
At this point, a lot of people just care about the 'feel' of anti-AI articles even if the substance is BS though.
And then people just feed whatever gets clicks and shares.
Googles tpu can't handle llm's lol. What do you mean "exactly"?
In fact, Gemini was trained on, and is served, using TPUs.
Google said its TPUs allow Gemini to run “significantly faster” than earlier, less-capable models.
Did you think Google's only TPUs are the ones in the Pixel phones, and didn't know that they have server TPUs?
I hadn't really heard of the TPU chips until a couple weeks ago when my boss told me about how he uses USB versions for at-home ML processing of his closed network camera feeds. At first I thought he was using NVIDIA GPUs in some sort of desktop unit and just burning energy...but I looked the USB things up and they're wildly efficient and he says they work just fine for his applications. I was impressed.
Yeah they're pretty impressive for some at home stuff and they're not even that costly.
The Coral is fantastic for use cases that don't need large models. Object recognition for security cameras (using Blue Iris or Frigate) is a common use case, but you can also do things like object tracking (track where individual objects move in a video), pose estimation, keyphrase detection, sound classification, and more.
It runs Tensorflow Lite, so you can also build your own models.
Pretty good for a $25 device!