Is Groq different from Grok?

NVIDIA chips are more versatile. During training, you might need to schedule things to the SFU(Special Function unit that does sin, cos, 1/sqrt(x), etc), you might need to run epilogues, save intermediary computations, save gradients, etc. When you train, you might need to collect data from various GPUs, so you need to support interconnects, remote SMEM writing, etc.

Once you have trained, you have frozen weights/feed-forward networks that consist out of frozen weights that you can just program in and run data over. These weights can be duplicated across any amount of devices and just sit there and run inference with new data.

If this turns out to be the future use-case for NNs(it is today), then Google are better set.

Covid

> could quickly surpass what humans are capable of and solve problems that have vexed society for millennia.

Bunk. Almost all of our vexatious problems are so because we lack the social and political tools to bring to bear existing technologies to address them. “AGI” will do nothing to address our social and political deficiencies. In fact, AI/AGI deployed by concentrated large corporations will only worsen our social and political problems.

IMO, if you're gaining a significant amount of productivity from LLMs in a technical field, it's because you were either very junior and lacked much of the basic knowledge required of your role, or you performed like you were.

These models do not appear from thin air. Add in the training cost in terms of power. Yes it's capex and not opex, but it's not free by any means.

Plus, not all these models run on optimized TPUs, but mostly on nVIDIA cards. None of them are that efficient.

Otherwise I can argue that running these models are essentially free since my camera can do face recognition and tracking at 30fps w/o a noticeable power draw since it uses a dedicated, purpose built DSP for that stuff.

Also out of curiosity, I did some quick math regarding that claim you read somewhere.

Cellphone battery charge: I have a 5000mAh cellphone battery. If we ignore charging losses (pretty low normally, but not sure at 67W fast charging)... That battery stores about 18.5 watt-hours of energy, or about 67 kilojoules.

Generating a single image at 1024x1024 resolution with Stable Diffusion on my PC takes somewhere under a minute at a maximum power draw under 500W. Lets cap that at 500*60 = 30 kilojoules.

So it seems plausible that for cellphones with smaller batteries, and/or using intense image generation settings, there could be overlap! For typical cases, I think that you could get multiple (but low single digit) of AI generated images for the power cost of a cellphone charge, maybe a bit better at scale.

So in other words, maybe "technically incorrect" but not a bad approximation to communicate power use in terms most people would understand. I've heard worse!

I don't know if this has been posted already, but here is his manifesto

https://archive.is/2024.12.09-230659/https://breloomlegacy.s...

Sounds like both he and his mother were suffering from chronic pain and UHC dicked them around and refused to act in good faith

Three are extensions and tools that reveal the dislikes - they are still there even if Youtube doesn't expose them by default.