Any perceived difference isn't due to any technical superiority of vinyl though, more often it's the other way around: you can't dynamically compress audio as much and still have it playable from vinyl, so there's often a different, more dynamic, master/mix going onto the vinyl.
Sighted testing and different audio levels are other common sources of perceived vinyl superiority.
Many people enjoy the ritual of grabbing a vinyl and setting it up for playback, having physical access to coverart and maybe lyrics, which are entirely valid reasons to prefer vinyl, but that isn't audio quality.
I found the deficiency that bothered me most was the analog portion of the ADC. Once that has muddled through the capture process there's not much that can be done regardless of DAC upon playback.
Besides that, the original engineers, working closely with the classic rock musicians (or scrutinizing conductor) during the original recording & mastering of their potential smash hit record, were probably paid about 10x relative to a digital mastering re-engineer decades later. Or if the artists were not established hitmakers, worked about 10x as hard to try and make it come true. Sometimes both, since you need to improve the odds all you can technically if you want to better leverage the fundamental popularity contest.
Naturally the analog section on playback can also be a nasty issue for both digital and vinyl sources.
> Heretic is a tool that removes censorship (aka "safety alignment") from transformer-based language models without expensive post-training. It combines an advanced implementation of directional ablation, also known as "abliteration", with a TPE-based parameter optimizer powered by Optuna.
> This approach enables Heretic to work completely automatically. Heretic finds high-quality abliteration parameters by co-minimizing the number of refusals and the KL divergence from the original model. This results in a decensored model that retains as much of the original model's intelligence as possible. Using Heretic does not require an understanding of transformer internals. In fact, anyone who knows how to run a command-line program can use Heretic to decensor language models.
Abliteration seems to be what Heretic does?
> Now these "abliterated" models all suffer from catastrophic breakage because they are not as simple anymore.
I'm not knowledgeable about each step in the process of making these abliterated models, but some more popular ones with steps after Heretic, seem to improve on the benchmarks tried of the base model:
I'm not seeing any similar benchmarks of the HauhauCS models, at least the ones I checked, so I assumed the opinion is based on your own trials, but then you argue in favour Heretic. Is the based on pre-Heretic abliteration techniques? Which might then not be appliable to this "Proprietary weight-level abliteration developed by the dealignai research team."?
Heretic is an awesome tool and I'd prefer Heretic over the "Proprietary weight-level abliteration developed by the dealignai research team" or manual labor model surgery. Since Heretic models are not often called "Abiterated" on HF but are called "Uncensored" or "Heretic" I may have gotten a bit confused there. From what I know, HauhauCS also uses Heretic so it should be the same as Heretic ones in benchmarks.
I see hacspec using secret-integers: "wrapper around integer types for constant-timedness". Does Hacspec/Bertie have assurances about constant-timedness too?
> It is perfectly good enough for the error code enumeration to be statically randomized into hard coded constants.
A comment points out that they aren't randomized:
> The values used were chosen such that it takes a large number of bit flips to change from allowed to denied. Using random values doesn't really protect against this attack.
> Nvidia needs 65% more 5nm wafers to produce the same number of GPUs. Basically all else being equal and scaled, AMD has 65% more capacity than Nvidia, when it comes to the most critical part of the production.
> 5nm dies are the most expensive part of the whole solution, meaning there is also a 65% pricing advantage (though some of this advantage is offset by more complex packaging and other cheaper dies that go into mi300x as well as more HBM chips).
> 5nm dies are the most expensive part of the whole solution, meaning there is also a 65% pricing advantage (though some of this advantage is offset by more complex packaging and other cheaper dies that go into mi300x as well as more HBM chips).
That doesn't say anything about the fraction of the cost on the finished product though? Those numbers just say it could be something like 2% vs 6% of the cost of the card.
Pricing breakdowns are going to be hard to come by, as they're negotiated and confidential. But I think it would be safe to say that leading-edge core dies are going to be a sizable plurality (if not majority) of the cost. Only memory would come close (and some of those Reddit comments suggest more, due to it being HBM), as power delivery and other small components like resistors just aren't that expensive.
The trickier bit to measure is R&D, as it's both a fixed cost and one that can be spread across several products.
The 608.5 mm² 4090 with 76.3 B transistors isn't too far off from the 814 mm² H100 w 80 B transistors. Seems like mostly more area for memory interfacing?
Assuming they're not selling the 4090 at a loss and being generous that the whole cost is the chip cost, that makes for a $1600 chip. Scaled to the H100 we get $2140. That's a pretty minuscule fraction of a $35k card.
Good point... at 35ish chips per wafer (although I would -hope- nvidia provisioned things sanely to help yields or otherwise bin) an increase in fully good Chips is still better than binning.
Checking nvidia-smi it stalls at ~130W (out of ~470 W max) power usage, ~25% GPU usage and ~10% memory bandwidth usage. There's fairly much traffic on the pci-bus though, and the python process is stable at 100% usage of one core. GPU possibly limited by some thing handled in python?
Pausing the GPU-accelerated video-decoding of a twitch stream it get a surprisingly large boost:
https://news.ycombinator.com/item?id=49552520
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