211–220 of 672 posts
Now everyone that needs classical workstations can finally move on into Linux or Windows workloads. Believe t-shirts at WWDC were not enough. Thus the workstation market joins OS X Server.
Apple still sells a workstation-type machine: the Mac Studio.
Hardly workstation class.
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> Apple is counting on something else: model shrink The most powerful AI interactions I've had involved giving a model a task and then fucking off. At that point, I don't actually care if it takes 5 minutes or an hour. I've cued up a list of background tasks it can work on, and that I can circle back to when I have time. In that context, smaller isn't even the virtue at hand–user patience is. Having a machine that wo…
Cool? And it has nothing to do with what kind of consumer hardware Apple should sell. If your use cases are literally "bigger model better" then the you should always use cloud. No matter how much computing power Apple squeezes into their device it won't be a mighty data center.
Pour one out for John Siracusa
I bet there’s gonna be a banger of a Mac Studio announced in June. Apple really stumbled into making the perfect hardware for home inference machines. Does any hardware company come close to Apple in terms of unified memory and single machines for high throughput inference workloads? Or even any DIY build? When it comes to the previous “pro workloads,” like video rendering or software compilation, you’ve always been…
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> gone are the days of PCIe. My GPU, NVMe drives and motherboard might disagree.
- GPU is integrated into the SoC - Surprisingly, it is possible to plug a drive into a TB/USB port …so what do you actually need PCIe for?
Apple really dropped the ball here. They had every ability to make something competitive with Nvidia for AI training as well as inference, by selling high end multi GPU Mac Pro workstations as well as servers, but for some reason chose not to. They had the infrastructure and custom SoCs and everything. What a waste. It really could have been a bigger market for them than even the iPhone.
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> something competitive with Nvidia for AI training Apple is counting on something else: model shrink. Every one is now looking at "how do we make these smaller". At some point a beefy Mac Studio and the "right sized" model is going to be what people want. Apple dumped a 4 pack of them in the hands of a lot of tech influencers a few months back and they were fairly interesting (expensive tho).
> Apple is counting on something else: model shrink The most powerful AI interactions I've had involved giving a model a task and then fucking off. At that point, I don't actually care if it takes 5 minutes or an hour. I've cued up a list of background tasks it can work on, and that I can circle back to when I have time. In that context, smaller isn't even the virtue at hand–user patience is. Having a machine that wo…
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Sure, but it’s probably reflective of the fact that GPUs generally aren’t PCIe-bandwidth bound. Also, TB5 and Oculink2 both use PCI 4.0 x4 links.
Oculink is generally faster than TB5 despite them both using PCIe 4.0, because Oculink provides direct PCIe access whereas Thunderbolt has to route all PCIe traffic through its controller. The benchmarks show that the overhead introduced by the TB5 controller slows down GPU performance.