1: https://pytorch.org/docs/stable/generated/torch.use_determin...

There's no data on this, at the very least you could mention that it's only your personal impression ?

IMO (and this is clearly a personal take) there are two competing effects: - higher bandwidth and easier to align face to face - more distractions, interruptions, more complicated to get things done

If you're in a business or position where you have no IP or nothing hard to do per say, you'll see the first one dominate. If you're somewhere with IP and competitive advantages through smarts then I'd say (personal again) the second effect can come to dominate.

Google pulling a "no remote" move means to me that their competitive advantage in terms of engineering and smarts is not a priority + using the fact that the market swung back towards employers vs. employees. But not general comment about "this take is obviously so much better", this is just intellectual lazyness I believe

It's really strange to me though, and it's probably a culture thing, because for this use case literally everybody I know would use a bowline knot (https://www.animatedknots.com/bowling-knot), possibly with an extra lock to make sure it does not untie if there's no tension. This observation comes from a French sailing background, hence the probable cultural bias.

Could you educate me as of where the trucker's hitch is better? (to me the bowtie feels simpler, no possible slippage and easy to untie after a load)

On the contrary, a 3080 laptop does reach 400GB/s, I'm personally seeing this routinely on AI workloads, so that's part of the explanation for subpar perf here (the other ones being probably matrix math and mixed precision)

To complement other points:

- you can put the nvidia card in “compute” or “hybrid” mode, which removes the need for X restarts. Compute is really nice, the computer runs on IGP (which is vey capable) and all cuda workloads seamlessly wake up the nvidia card, no question asked

- the above means that the pc is nearly silent, maybe helped by the AMD cpu, while being pretty capable with the 8 real zen3 cores

- no issues really on Linux, and the Asus-laptop tools allow you to switch off the leds or cap the battery charge. The wifi card was an issue initially, quickly fixed with a newer kernel

- the screen is 120Hz, and this is really appreciated actually

I would buy a newer version in a pinch

- the workloads in games can vary a lot, vertex/fragment shaders imbalance, parallel compute pipelines, mixed precision (which the M1 gpu does not do), .. So another explanation is that you can get some 3070 parity on a cherry picked game, like a broken clock is right twice a day, but that does not make it generally true. Objective benchmarks have put the M1 gpus way slower than 3070 on average, and software support seems like an easy but false distraction given the Proton tax on Linux (which is not 30/50%)

- the M1 gpus are lacking a ton of hardware, matrix mul, fp16 again, ray tracing, VRR probably (not sure about this last one). These are used by modern games or applications, you may find a benchmark which skip them, but in the grand scheme of things it's something that the M1 gpu will have to emulate more often than not, and this has a cost

Waving all that as "the GPU is about the same speed" is technically wrong, or not really backed by facts at the very least

Else yield obviously counts, that's what stands in the way of this CPU having more cache or 160 cores, for what it's worth, so it has to count for something obviously. The multiple tiers in every cpu manufacturer line up is also a consequence of yield, so it's very much not a minor element of the equation

Not saying that the future has to be multi-die, but if it is not, then it has to be way faster than the cheaper-to-manufacture competition.