alt Hacker News> would use less electricity
Sorry to shatter your bubble, but this is patently false, LLMs are far more efficient on hardware that simultaneously serves many requests at once.
There's also the (environmental and monetary) cost of producing overpowered devices that sit idle when you're not using them, in contrast to a cloud GPU, which can be rented out to whoever needs it at a given moment, potentially at a lower cost during periods of lower demand.
Many LLM workloads aren't even that latency sensitive, so it's far easier to move them closer to renewable energy than to move that energy closer to you.
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I'm actually not sure that's true. Apart from people buying the device with or without the neural accelerator, the perf/watt could be on par or better with the big iron. The efficiency sweet-spot is usually below the peak performance point, see big.little architectures etc.
> Sorry to shatter your bubble, but this is patently false, LLMs are far more efficient on hardware that simultaneously serves many requests at once.
You might want to read this: https://arxiv.org/abs/2502.05317v2
Well this is an article about running on hardware I already have in my house. In the winter that’s just a little extra electricity that converts into “free” resistive heating.
> LLMs are far more efficient on hardware that simultaneously serves many requests at once.
The LLM inference itself may be more efficient (though this may be impacted by different throughput vs. latency tradeoffs; local inference makes it easier to run with higher latency) but making the hardware is not. The cost for datacenter-class hardware is orders of magnitude higher, and repurposing existing hardware is a real gain in efficiency.