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If my gpu is sitting idle, and I mean idle with nothing loaded into its memory, it's sitting at about 18W. If I load in model that uses nearly all of the memory but that model is idle, it's at 36W. If that model is actively thinking, it's like 118W. I think this is likely due to the GPU being aware that there is real data loaded into memory and turning up the DRAM refresh rate whereas when nothing is loaded, the dynamic power is as low as possible.

Server cards are not optimized for idle power usage. They’re expected to be fully utilized.

For server gear it’s more common to have less dynamic power and voltage switching because it produces more predictable performance and latency.

I suspect the act of running nvidia-smi itself prevents the GPU from being put into a low-power state.

So I guess we'll all be applying a random rotation to our matrices now to obscure their contents, like TurboQuant does. https://arkaung.github.io/interactive-turboquant/#rotation

I expected a “torch is smart enough to keep track of cases where it just initialized the C in C <= A*B+C to zero, avoiding the add” type situation but I was wrong.

>I went in expecting to find 'branch prediction'[0]

GPUs do branch prediction? I thought they didn't bother and try to minimize wasted effort by using high amounts of concurrent threads?

That's exactly what I thought.

To be fair, the culprit in the article is _less complex_ than branch prediction: "with random data, bits are flipped often, and bit flips in transistors inherently draw power" is less mental gymnastics than "with random data, the cpu fails to predict the future, causing redundant speculative execution"

This is not observable from LLM inference, where you would not encounter uniform matrices.

Power limiting does not improve performance but it does improve efficiency. You might be able to get 90% of the performance for only 70% of the power usage, for example. It does not make the card go faster though.

In general, constraints require optimizations and rearchitectures. I'd also expect the ram shortage for instance to have a big impact on the software industry as a whole, specially in games. They will need to make do with what people have, a ps5/pro or similar in PC power.

Here is one: An adjustment to weight updates, that makes it more likely for weights to stay uniformly distributed.

~257.5 teraflops for normal distribution, versus ~268 teraflops uniform, reported on the first graph.

I would have liked to see a straight graph of performance vs. clock speed, for each type of data. Pick your data statistics, then pick the peak performance clock speed accordingly.

And for actual runs, from a pre-run sampled curve.

And there's at least one more level of inception at the data center level, where they use AI to optimize power usage (particularly by predictively controlling cooling, and adaptively rescheduling tasks).