There is no popping. We cannot have enough compute for the forseeable future.

It's been funny to see people move from overclocking to underclocking. Especially for the older AMD gpus. On the RX480 a slight underclock would cut the power usage almost in half!

> Overclocking long ago was an amazing saintly act, milking a lot of extra performance that was just there waiting, without major downsides to take.

Back when you bought a 233 Mhz chip with ram at 66 Mhz, ran the bus at 100 Mhz which also increased your ram speed if it could handle it, and everything was faster.

> But these days, chips are usually already well tuned. You can feed double or tripple the power into the chip with adequate cooling, but the gain is so unremarkable. +10% +15% +20% is almost never going to be a make or break difference for your work

20% in synthetic benchmarks maybe, or very particular loads. Because you only overclock the CPU these days so anything hitting the ram won't even go to 20%.

Oh, we're largely on the same page there.

I was actually looking for benchmarks earlier this week along those lines - ideally covering the whole slate of Arrow Lake processors running at various TDPs. Not much available on the web though.

I learned a lot about underclocking, undervolting, and computational power efficiency during my brief time in the ethereum mining[1] shenanigans. The best ROI was with the most-numerous stable computations at the lowest energy expense.

I'd tweak individual GPUs' various clocks and volts to optimize this. I'd even go so far as to tweak fan speed ramps on the cards themselves (those fans don't power themselves! There's whole Watts to save there!).

I worked to optimize the efficiency of even the power from the wall.

But that was a system that ran, balls-out, 24/7/365.

Or at least it ran that way until it got warmer outside, and warmer inside, and I started to think about ways to scale mining eth in the basement vs. cooling the living space of the house to optimize returns. (And I never quite got that sorted before they pulled the rug on mining.)

And that story is about power efficiency, but: Power efficiency isn't always the most-sensible goal. Sometimes, maximum performance is a better goal. We aren't always mining Ethereum.

Jeff's (quite lovely) video and associated article is a story about just one man using a stack of consumer-oriented-ish hardware in amusing -- to him -- ways, with local LLM bots.

That stack of gear is a personal computer. (A mighty-expensive one on any inflation-adjusted timeline, but what was constructed was definitely used as a personal computer.)

Like most of our personal computers (almost certainly including the one you're reading this on), it doesn't need to be optimized for a 24/7 100% workload. It spends a huge portion of its time waiting for the next human input. And unlike mining Eth in the winter in Ohio: Its compute cycles are bursty, not constant, and are ultimately limited by the input of one human.

So sure: I, like Jeff, would also like to see how it would work when running with the balls[2] running further out. For as long as he gets to keep it, the whole rig is going to spend most of its time either idling or off, anyway. So it might as well get some work done when a human is in front of it, even if each token costs more in that configuration than it does OOTB.

It theoretically can even clock up when being actively-used (and suck all the power), and clock back down when idle (and resume being all sleepy and stuff).

That's a well-established concept that [eg] Intel has variously called SpeedStep and/or Turbo Boost -- and those things work for bursty workloads, and have worked in that way for a very long time now.

[1]: Y'all can hate me for being a small part of that problem. It's allowed.

[2]: https://en.wikipedia.org/wiki/Centrifugal_governor

>> people saying they want insane power density are, in my personal view, deluded ridiculous individuals who understand very very little.

Or they are simply not-rich people who cannot afford to purchase extra hardware to run in parallel. Electricity is cheap. GPUs are not. So i want to get every ounce of power out of the precious few GPUs i can afford to own.

(And dont point at clouds. Running AI on someone else's cloud is like telling a shadetree mechanic to rent a car instead of fixing his owm.)

> He just didn't want to buy another one.

Wasn’t it loaned ie didn’t buy any at all?

Apple should have loaned enough to flex.

From my brief discussion with Exo/Apple, it sounds like that is just a limitation of this initial rollout, but it's not a hardware limitation.

Though, I am always leery to recommend any decisions be made over something that's not already proven to work, so I would say don't bet on all ports being able to be used. They very well may be able to though.

I bet there is one piece of silicon per two ports.

Can’t you make bandwidth reservations and optimise data location to prefer comms between directly connected nodes over one or two-hop paths?

onnx supports CoreML, is that how?

Apple already paid software engineers to add Tensorflow support for the ANE hardware.

Apple already ships an MLX5 driver for ConnectX NICs.

Also given that Apple are using these in their datacenters I think they will ship much more server like hw.

Seems like I remember the main reason Macs survived as a product at all was because you needed one to develop for iOS. That may be an exaggeration but there certainly was a time when Macs were few and far between outside of creative shops. Certainly they were almost unseen in the corporate world, where now they are fairly common at least in laptops.

Considering that Apple is moving away from Linux in the datacenter to its own devices, I'm not sure that's the case. The apple machines aren't available to the consumer (they're rack-mounted, dozens of chips per PCB board, custom-made machines) but they're much less power-hungry, just as fast (or more so), much cheaper for them to make rather than buy, and natively support their own ecosystem.

Some of the machine-designs that consumers are able to buy seem to have a marked resemblance to the feature-set that the datacenter people were clamouring for. Just saying...

> i'm not sure why anyone would buy a mac studio instead of a gb10

For an AI-only use case, the GB10s make sense, but they are only OK as desktop workstations, and I’m not sure for how long DGX OS will be updated, as dedicated AI machines have somewhat short lives. Apple computers, OTOH, have much longer lives, and desktops live the longest. I retired my Mac Mini a year after the machine was no longer getting OS updates, and it was still going strong.

it's just people looking to do experiments locally on the main machine rather than just get a dedicated spark, which can be used properly as a headless box than a Mac of which you are at the mercy of system shenanigans albiet still bearable compared to windows

Wow, this switch (MikroTik CRS812) is scary good for the price point. A quick Google search fails to find any online vendors with stock. I guess it is very popular! Retail price will be <= 1300 USD.

I did some digging to find the switching chip: Marvell 98DX7335

Seems confirmed here: https://cdn.mikrotik.com/web-assets/product_files/CRS812-8DS...

And here: https://cdn.mikrotik.com/web-assets/product_files/CRS812-8DS...

    > Switch chip model 98DX7335

    > Description: 32x50G / 16x100G-R2 / 8x100G-R4 / 8x200G-R4 / 4x400G-R8
    > Bandwidth: 1600Gbps

That switch appears to have 2x 400G ports, 2x 200G ports, 8x 50G ports, and a pair of 10G ports. So unless it allows bonding together the 50G ports (which the switch silicon probably supports at some level), it's not going to get you more than four machines connected at 200+ Gbps.

Cool! So for marginally less in cost and power usage than the numbers I quoted, you can get 2 more machines than with the RDMA setup. And you’ve still not solved the thing that I called out as the most important drawback.

RDMA for new AI/HPC clusters is moving toward ethernet (the keyword to look for is RoCE). Ethernet gear is so much cheaper that you can massively over-provision to make up for some of the disadvantages of asynchronous networking, and it lets your run jobs on hyperscalers (only Azure ever supported actual IB). Most HPC is not latency-sensitive enough that it needs Infiniband’s lower jitter/median, and vendors have mostly caught up on the hardware acceleration front.