ein0p

ZFS sort of moved inside the NVMe controller - it also checksums and scrubs things all the time, you just don't see it. This does not, however, support multi-device redundant storage, but that is not a concern for Apple - the vast majority of their devices have only one storage device.

Strange specs table - it seems to ignore the tensor core FLOPs, which is what you'd be using most of the time if you're interested in computational throughput.

I think they can be, except of course the problems need to be much harder, and impossible to solve via vibe coding alone. Like it or not, AI assistance is going to stay with us. This is the "new baseline" against which engineers will be judged.

Hmm, generating royalty-free music on Suno, or getting involved with some pompous dude who strokes out at the sight of someone eating a burger and demands 51% for his "no strings attached" stuff that's 20 years old? Tough choice.

I woke up to this in 2016 when theretofore beloved public figure Donald Trump turned into literally Hitler immediately after he descended down that elevator in the Trump tower, all without changing a single opinion he'd ever held. And it's been unrelenting ever since.

At 192 cores you're way better off buying a Mac Studio, though.

Thanks for chiming in! How do you explain the top-most graph in Figure 5? Am I misreading it?

Did I say it wasn't? If your context is short and your model is small, it is possible to run LLMs on high-end CPUs able to support 12 channels of high-spec DDR5 RDIMMs. It's not possible to run them as fast as they'd run on a GPU equipped with HBM though. Nor would it be even remotely as energy efficient. Also, it's not possible to run LLMs quickly on CPU if your context is long, because CPUs do not have the requisite FLOPS to process long context quickly. And before you bring MoE into the conversation, MoE only affects the feedforward part of each transformer block, and full memory bandwidth and compute savings are only realized at batch size 1, sequence length 1, AKA the most inefficient mode that nobody other than Ollama users use in practice. Sequence length 8 (common for speculative decoding) could be using up to 8x37B parameters (assuming you want to run DeepSeek - the strongest available open weights model). Batch size of even 2 with sequence length 8 could use almost all parameters if you're particularly unlucky. Prompt will almost certainly use all parameters, and will slam into the FLOPS wall of your EPYC's ALUs. So can LLMs (with an emphasis on "Large") be run on CPUs? Yes. Are you going to have a good time running them this way? No.

Note that this is _way_ slower at small batch sizes you'd need for interactive use. At batch size 1 this seems to run at 1/3rd the speed of bf16 (so about 1/6th the speed of fp8 you'd realistically be using) if figure 5 is to be believed. This is actually a pretty impressive feat in itself if you know anything about GPU kernel programming, but it is much slower nevertheless. For this to work at "wire speed" it'd need hardware support, which takes years. Their "baseline" elsewhere in the paper is CPU offloading, which is dog slow and can't be made fast due to PCIe bottleneck.

Depends on what kind of exercise. A few heavy sets of barbell squats or deadlifts will put you out pretty good. Especially the deadlifts.

And all the drugs and treatments derived from those "studies" are going to continue to be prescribed for another couple of decades, much like they were cutting people up to "cure ulcers" long after it was proven that an antibiotic is all you really need to cure it. It took about a decade for that bulletproof, 100% reproducible study to make much of a difference in the field.

As far as I can tell, it's "$20K" the same way Cybertruck was "$39K". It's not available for purchase yet, and when it is, it'll be twice as much, because Bezos also likes money.

Why though? Does anyone use it? I tried to use it on my iPad Pro and couldn't get used to it. This would make sense if some iPhone model was a tri-fold foldable with a huge screen, but I don't think that's going to happen in the foreseeable future.