Home rigs like that are no longer cost effective. You're better off buying an rtx pro 6000 outright. This holds both for the sticker price, the supporting hardware price, the electricity cost to run it and cooling the room that you use it in.
I was just watching this video about a Chinese piece of industrial equipment, designed for replacing BGA chips such as flash or RAM with a good deal of precision:
Eh, I don't see the risk, no pun intended. It's not collimated, and it's not going to be in focus anywhere but on-target. It's also probably in the long-wave range >>1000 nm that's not focused by the eye. At the end of the day it's no different from any other source of spot heating. I get more nervous around some of the LED flashlights you can buy these days.
It's 45w of lasing power. I have a scar on my hand that's 15 years old from running one of those at 10% power and getting a reflection from a bare metal sheet.
This will absolutely scar, if not char, your cornea faster than you can blink.
That's (again) less energy than a flashlight puts out these days, so the beam had to be tightly focused in your case. That isn't how these things work.
There is nothing special about "lasing power." It amounts to a 45-watt light bulb, nothing more and nothing less.
A 45 watt light bulb spreads the energy in all directions - at 1 meter away that's about 3 watts in every square meter or roughly 0.000003 watts per square millimeter. The laser is putting 45 watts into that same square millimeter at the same distance.
Of course the laser is tightly focused. That's pretty much one of the defining properties of laser devices. How else do you think the laser is heating the microprocessors in the video?
They will be using a beam spreader to conform to the size of the targeted IC, which is usually on the order of 5x5 mm and up. For smaller parts they will be reducing the power.
They shouldn't be focusing it to a point under any conditions. Whether it's as safe as it could be is a different question, of course. For instance, you'd like to think that the act of configuring it for a smaller beam footprint would reduce the power at the same time, as opposed to requiring a separate adjustment that might be overlooked by the operator. Would have been nice if the video had addressed that and other safety considerations, for sure.
A lot depends on the exact wavelength. 1400 nm and longer is much less worrisome than near-visible IR.
That's obviously not a good-faith or technically-accurate description of what's happening here, or else everybody in that video would be carrying a white cane, along with everybody who uses this type of equipment in the phone repair business.
About all we can agree on, I think, is that neither of us knows enough about the product to argue about it usefully.
Yeah, the pricing for the rtx pro 6000 is surprisingly competitive with the gamer cards (at actual prices, not MSRP). A 3x5090 rig will require significant tuning/downclocking to be run from a single North American 15A plug, and the cost of the higher powered supporting equipment (cooling, PSU, UPS, etc) needed will pay for the price difference, not to mention future expansion possibilities.
I run it all the time, token generation is pretty good. Just large contexts are slow but you can hook a DGX Spark via Exo Labs stack and outsource token prefill to it. Upcoming M5 Ultra should be faster than Spark in token prefill as well.
> I run it all the time, token generation is pretty good.
I feel like because you didn't actually talk about prompt processing speed or token/s, you aren't really giving the whole picture here. What is the prompt processing tok/s and the generation tok/s actually like?
I addressed both points - I mentioned you can offload token prefill (the slow part, 9t/s) to DGX Spark. Token generation is at 6t/s which is acceptable.
6 tok/sec might be acceptable for a dense model that doesn't do thinking, but for something like DeepSeek 3.2 that does do reasoning, 6 tok/sec isn't acceptable for anything else but async/batched stuff, sadly. Even for a response with just 100 tokens we're talking a minute for it to just write the response, for anything except the smallest of prompts you'll easily be hitting 1000 tokens (600 seconds!).
Maybe my 6000 Pro spoiled me, but for actual usage, 6 or even 9 tok/sec is too slow for a reasoning/thinking model. To be honest, kind of expected on CPU though. I guess it's cool that it can run on Apple hardware, but it isn't exactly a pleasant experience at least today.
Dunno, DeepSeek on MacStudio doesn't feel much slower than when using it directly on deepseek.com; 6t/s is still around 24 characters per second which is faster than many people could read. I also have 6000 Pro but you won't fit any large model there and to be able to run DeepSeek R1/3.1/3.2 671B at Q4 you'd need 5-6 of them depending on the communication overhead. MacStudio is the simplest solution to run it locally.
> 6t/s is still around 24 characters per second which is faster than many people could read.
But again, not if you're using thinking/reasoning, which if you want to use this specific model properly, you are. Then you have a huge delay before the actual response comes through.
> MacStudio is the simplest solution to run it locally.
Obviously, that's Apple's core value proposition after all :) One does not acquire a state-of-the-art GPU and then expect simple stuff, especially when it's a fairly uncommon and new one. You cannot really be afraid of diving into CUDA code and similar fun rabbit holes. Simply two very different audiences for the two alternatives, and the Apple way is the simpler one, no doubt about it.
