2022 Mac Studio (20-core M1 Ultra) Review

http://hrtapps.com/blogs/20220427/

by avidphantasm • 4 years ago

276 173 4 years ago

https://en.wikipedia.org/wiki/Gustafson's_law

lupire4• 4 years ago

That's awesome. Get a law named after yourself by taking an old paper and just saying "nuh-uh, the naive analysis is better".

Tagbert2• 4 years ago

Congratulations. Announcing the new Lupire’s law.

Reason0772• 4 years ago

"Nano-texture glass gives up a little bit of the sharp vibrant look you get with a glossy screen, but it’s worth the trade in usability, to be able to see the screen without distractions all day long."

"Nano-texture glass" is pretty much just what all screens were like back in the days of CRTs and pre-glossy flat screens. Now Apple are charging $300 for it!

jjtheblunt• 4 years ago

the pixels are WAY smaller nowadays, though; perhaps that constrains the nanotexture fabrication process in an expensive way?

astrange• 4 years ago

"Nano-texture" is different from matte - matte LCDs don't have reflective glare, but they also have much lower contrast and you can see the grain if you look closely. Nanotexture doesn't have those issues, but it's expensive.

olliej2• 4 years ago

that was my thought as well ("yay marketing") but apparently it's actually structurally different so that it maintains contrast. I ordered one in early march and if it ever actually arrives I'll try to remember to reply on visible difference :D (not kidding, it was due late march, then slipped to Aripl 22-27, and today moved to May 23-?? )

sulam• 4 years ago

I have the nano textured XDR Pro and chose to order the Studio Display without it. I’m a data point of 1 obviously, but it diffuses the image quite a bit in my opinion.

numpad02• 4 years ago

It is a marketing name, but refers to a special procedure used to create the matte surface, not the fact that it’s matte. By the way, CRTs were glossy. We wiped them with wet towels.

ChrisMarshallNY2• 4 years ago

By the way, CRTs were glossy. We wiped them with wet towels.

I remember the crackling, if you wiped them, within a few minutes of last being on.

Nowadays, there's lots of folks that think "CRT" is a political hotbutton topic.

Reason0772• 4 years ago

Most CRTs weren't glossy in the same way that modern flat panels are. They diffused reflections: you couldn't see a crystal-clear mirror image of yourself on a dark screen like you can with today's screens.

cglong2• 4 years ago

Editorialized title. Original was "2022 Mac Studio (20-core M1 Ultra) Review".

pvg• 4 years ago

email these in

cglong2• 4 years ago

Done! Thank you for the reminder :)

avidphantasm2• 4 years ago

Could you explain what “these” are that need to be emailed in?

MBCook• 4 years ago

I didn’t submit it but in this case the original title is so generic no one would have looked at it so I’m kind of happy they put the important part in the headline here.

dang• 4 years ago

Changed now. (Submitted title was 'Near-linear speedup for CPU compute on 20-core Mac Studio'.) Thanks!

microtonal4• 4 years ago

Above, we’re looking at parallel performance of the NASA USM3D CFD solver as it computes flow over a classic NACA 0012 airfoil section at low speed conditions.

If this solver relies on matrix multiplication and uses the macOS Accelerate framework, you are seeing this speedup because M1 Macs have AMX matrix multiplication co-processors. In single precision GEMM, the M1 is faster than an 8 core Ryzen 3700X and a bit slower than a 12 core Ryzen 5900X. The M1 Pro doubles the GFLOPS of the M1 (due to having AMX co-processors for both performance core clusters). And the M1 Ultra again doubles the GFLOPs (4 performance core clusters, each with an AMX unit).

Single-precision matrix multiplication benchmark results for the Ryzen 3700X/3900X and Apple M1/M1 Pro/M1 Ultra are here:

https://twitter.com/danieldekok/status/1511348597215961093?s...

bee_rider• 4 years ago

Just for anyone as out of touch with the MacOS ecosystem as me: Accelerate includes a BLAS implementation, so at least seems plausible (depending on how this library was compiled) that their special instructions might have been used.

torginus• 4 years ago

But that makes me think, what prevents people from running these calculations on the GPU? Even the memory is shared - the few 100 gflops they get out of the M1 ultra is pocket change in GPU terms.

the_svd_doctor• 4 years ago

Well that particular NASA code is from the 90’s and in Fortran. Maybe with MPI. That’s probably part of the reason why.

CamperBob2• 4 years ago

If a CPU doesn't run the OS and software I need to run, it might as well be a GPU or a Cray 9 or something dug out of the wreckage at Area 51. All of which are good descriptions of a CPU available only on the increasingly-proprietary Mac platform.

So this entire thread is kind of pointless with regard to many real-world use cases.

my1232• 4 years ago

Apple GPUs don't support FP64 at all - and Apple only provides Metal as a (supported and not deprecated) API.

NVIDIA GPUs have much more ubiquitous programming models, from CUDA C++ to OpenMP (both C++ and Fortran), allowing them to be much more useful.

AMD ROCm (a CUDA clone, but not managed too well...) isn't in a great state today, but it definitely provides an OpenMP compiler, for C++ only though, Fortran is not covered.

Intel GPUs have a much better SW story on all fronts for GPGPU than AMD's, and OpenMP is supported for both C++ and Fortran there.

tldr: if you want to run this code on GPUs, the two vendors to look for are NVIDIA and Intel. The others just don't have the software stack to do so. (without more heavyweight porting)

Of course, optimising for GPUs also adds work. That said, an OpenMP version is a very good starting point.

whatever12• 4 years ago

Because GPUs calculations are the same type of calculations. If you have a problem where you need to add plus 1 in all of the elements of a matrix, then GPUs are great. Games are a great example because you can take one pixel and calculate its values over time without much worry of what happens around it. Similar story for neural nets.

For CFD it is a different problem. In each time-step you essentially solving a linear system Ax=b, so even if you are looking at element x(100,100,timeperiod2) you need to also know the value of the element x(1,1,timeperiod1).

There are some algorithms that decouple the problem by introducing residuals for each element and then trying to iteratively reduce them, but as you can see, it is not a linear increase in speed as someone would had expected by looking at the GPU specs.

TLDR: Yes you can do CFD with GPUs, but dont expect miracles.

stephencanon• 4 years ago

If it were taking advantage of Accelerate, the performance would be much higher, but also the scaling would be quite different. Look at the scaling in the tweet you linked--it's anything but linear in the number of cores used.

snapcore• 4 years ago

The scaling curve is different from the other cpus which implies the performance bottleneck is not in matrix multiplication (which would be nearly linear on the older cpus because it is embarrassingly parallel).

paulmd4• 4 years ago

CFD is highly memory-bandwidth-bottlenecked, it is in fact pretty much the prototypical memory-bandwidth-bottlenecked task.

The performance scaling you see between systems pretty much corresponds to the memory bandwidth in those configurations.

Note that on the M1, the CPU can only access a fraction (about 25% iirc) of the total memory bandwidth, you have to use the GPU to really get the full performance of the M1 here.

lupire4• 4 years ago

That's good info but omg why a tweet of a crop of screenshot of a document.

staticfloat• 4 years ago

This linear scaling per-core doesn't match my experience with using the AMX co-processor. In my experience, on the M1 and the M1 Pro, there are a limited number of AMX co-processors that is independent of the number of cores within the M1 chip. I wrote an SO answer exploring some of the performance implications of this [0], and since I wrote that another more knowledgable poster has added more information [1]. One of the key takeaways is that appears to be one AMX coprocessor per "complex", leading us to hypothesize that the M1 Pro contains 2 AMX co-processors.

This is supported by taking the code found in the gist [2] linked from my SO answer and running it on my M1 Pro. Compiling it, we get `dgesv_accelerate` which uses Accelerate to solve a medium-size linear algebra problem, that typically takes ~8s to finish on my M1 Pro. While running, `htop` reports that the process is pegging two cores (analogous to the result in my original SO answer on the M1 pegging one core; this supports the idea that the M1 Pro contains two AMX co-processors). If we run two `dgesv_accelerate` processes in parallel, we see that they take ~15 seconds to finish. So there is some small speedup, but it's very small. And if we run four processes in parallel, we see that they take ~32 seconds to finish.

All in all, the kind of linear scaling shown in the article doesn't map well to the limited number of AMX co-processors available in Apple hardware, as we would expect the M1 Max to contain maybe 8 co-processors at most. This means we should see parallelism step up in 8 steps, rather than 20 steps as was shown in the graph.

Everything I just said is true assuming that a single processor, running well-optimized code can completely saturate an AMX co-processor. That is consistent with the tests that I've run, and I'm assuming that the CFD solver he's running is well-written and making good use of the hardware (it does seem to be doing so from the shape of his graphs!). If this were not the case, one could argue that increasing the number of threads could allow multiple threads to more effectively share the underlying AMX coprocessor and we could get the kind of scaling seen in the article. However, in my experiments, I have found that Accelerate very nicely saturates the AMX resources and there is none left over for future sharing (as shown in the dgesv examples).

Finally, as a last note on performance, we have found that using OpenBLAS to run numerical workloads directly on the Performance cores (and not using the AMX instructions at all) is competitive on larger linear algebra workloads. So it's not too crazy to assume that these results are independent of the AMX's abilities!

[0] https://stackoverflow.com/a/67590869/230778 [1] https://stackoverflow.com/a/69459361/230778 [2] https://gist.github.com/staticfloat/2ca67593a92f77b1568c03ea...

JohnTHaller• 4 years ago

The 5900X can do 24 threads, so why artificially limit it to 16 threads? Unless that's because the M1 Ultra has 16 performance cores. Why include the 3 year old 3700X? Why not include the 32 thread 5950X which can be had in a complete system for 1/3 the price of the Mac Studio M1 Ultra?

threeseed• 4 years ago

They clearly just picked 16 threads because it is enough to demonstrate the point.

M1 only has 4 performance cores and they still ran it with 16 threads. So it's not like they are trying to skew the benchmark by not showing the potential of the 5900x.

Also the 5950x is 2 years old and still is an active product.

hedgehog• 4 years ago

The 5900X is a fine reference as a common chip. The peak performance should be at 12 threads, it has 12 cores, and that's what the table appears to show.

simonh• 4 years ago

This isn’t a scientific study, it’s one guy who reviews the pro Macs he gets to work on. Also he’s been using the same benchmark for years and the figures for previous Mac Pros are historical, so past tests weren’t conducted with any knowledge of the M1 architecture.

microtonal4• 4 years ago

The performance generally regresses when using more threads on the Ryzen CPUs.

Also, maybe I can only benchmark on the hardware that I actually have?

bebort45• 4 years ago

I'm curious why Geekbench haven't put the Mac Studio on their Mac leaderboard yet - https://browser.geekbench.com/mac-benchmarks. There are plenty of benchmarks submitted https://browser.geekbench.com/search?page=7&q=Apple+M1+Ultra...

jfpoole• 4 years ago

There’s a bug in the Browser that we haven’t been able to track down yet that’s preventing the Mac Studio from appearing on the leaderboard.

wonderbore• 4 years ago

How could a new computer be so different that it can’t be shown? Aren’t the results plain text? This makes no sense

ChrisMarshallNY2• 4 years ago

Theodolite is an awesome app (that I hardly ever need to use).

This guy def knows his math (and working with Apple apps).

ac29• 4 years ago

Doesnt this just suggest they are leaving some performance on the table then? The reason the Intel processors scale non-linearly is because they run each core faster when there are less cores under load.

sliken8• 4 years ago

Dunno, looks like classic memory bottleneck to me. The m1 ultra has 800GB/sec, but I believe a bit more than half of that is available to the CPUs. The rest is for the GPU and various on chip accelerators.

So with about half the cores (16 vs 28) and twice the bandwidth (say 420GB/sec vs 180 GB/sec) it manages twice the performance. Looks pretty impressive to me. Looks like the Apple is significantly less memory bottle necked than the 6 channel Xeon W.

ip26• 4 years ago

Only in a certain sense. You can only turn the voltage so high before things melt, and you can only turn the clocks so high until you crash, and then that's basically it, you can't go any faster.

The perfect core could be dialed from fanless to water cooling with linear performance, but it doesn't quite exist today. Intel chips have top end, Apple chips have low end, but an i7 fed 3W isn't going to perform and an M1 can't take any more voltage.

The tension is figuring out how to build very large structures to perform useful work with gobs of power, yet still scale down to low power budgets. Imagine a core that can dynamically morph from P core to E core & back on the fly.

mrjin• 4 years ago

So a new discovery of Amdahl's Law?

https://en.wikipedia.org/wiki/Amdahl%27s_law

sydthrowaway• 4 years ago

I bet you can build an AMD system that beats this handily and costs half as much.

wildrhythms• 4 years ago

And the power consumption?

sudosysgen3• 4 years ago

Do you actually care about power consumption in a desktop computer under very heavy workload?

By the way, the M1 Ultra has a 370W power supply, so really the question is do you really care about the difference between 300 and 400 watts in this use case?

fartcannon6• 4 years ago

Is an irrelevant metric, like battery life, when doing scientific computation.

lupire4• 4 years ago

Can you? how much will you charge to build one for me? Do you offer warranty support?

diffeomorphism2• 4 years ago

Not OP, but sure why not. Just buy, say, a $4000 workstation with support from one of the usual vendors and sell it to you for $4500.

Btw most of that price is for certification for industry software. Does the Mac have that? If not, it is just immediately not an option.

sliken8• 4 years ago

Not with 800GB/sec of memory bandwidth.

Or even the 440GB/sec of memory bandwidth available to the CPUs.

Sure if you are cache friendly enough and you get enough Zen3 or Intel cores you can win, but you end up spending a fair chunk of change, getting less memory bandwidth, and for a clear win you often need to spend more, like say getting a Lenovo Threadripper (and they have a exclusive rights to the chip for 6 months or something).

MPSimmons• 4 years ago

Can someone please project that curve out and estimate where it starts to flatten?

sliken8• 4 years ago

That's not very useful. That curve might be a combination of a few different bottlenecks, l1 cache misses, scheduling bubbles, efficient scheduling of instructions within a certain window, l2 cache misses, cache contention in L1 or L2, AMX utilization, memory utilization (latency or bandwidth limited), MMU contention/page misses, etc. etc. etc.

The percentage of all those can change as you change the number of cores, and the different levels of the memory hierarchy get different levels of contention, latency limits, or bandwidth limits. So in an ideal world you can draw a graph and extrapolate, but in the real world you might do significant better or worse. There are cases (admittedly rather rare) where performance increases by more than linear in relation to cores.

O_H_E• 4 years ago

There are cases (admittedly rather rare) where performance increases by more than linear in relation to cores.

Ooh that's interesting, do you have any leads or examples?

pleb_nz3• 4 years ago

I was running a Lenovo p15v with fedora before changing over to m1 pro MacBook. The hardware is nice, but to be honest it's not much of a speed up over my Lenovo so I'm giving it more time.

Docker is definitely a lot slower

I have read of massive performance improvement with Linux on m1pro though so it might be a swap to that when more distros are available.

Question is, has MacOS become bloated or not had attention to performance to make best use of the new hardware?

jitl2• 4 years ago

Are you running x86 containers or ARM containers? I’m on a 64GB M1 and my x86 container build takes 15 mins from scratch on the M1, 10 mins from scratch on my 2019 Intel MBP, but the ARM container build takes 6 mins on the M1. With or without the Docker pain the battery life is so worth it. Can spend 2 days working from the couch without plugging it in.

dmix• 4 years ago

The battery life really is amazing. This is the sort of thing that really takes a laptop’s UX to the next level, while still being highly performant.

Plus for PC gaming I’ve been using a cloud server via Paperspace and I play games like Cyberpunk with 4K on ultra settings with pretty good latency (thanks to 1.5 gigabit fibre) and even some VR games using Steam VR + Parsec on a Quest 2.

I really don’t see the point in building a PC anymore. And I was really really close to building a Ryzen one before I discovered Paperspace/https://shadow.tech/

rufugee• 4 years ago

Wow...I mean, I love the battery life in my 16" M1 Max Pro. Similar specs to yours...64GB of ram and 8TB ssd...but I've never seen two days of use. Not even really a full day...I can get around 6-8 hours on the couch without plugging in.

Huge, and I mean HUGE improvement over my previous 16" Intel, which seemed to make it around 3 hours before calling it a day...

pleb_nz3• 4 years ago

2 days? I'm like some of the others posters. I regularly get to around 30% after 4 hours so I would guess I would get 6 to 8 hours on a single charge.

smoldesu• 4 years ago

Question is, has MacOS become bloated or not had attention to performance to make best use of the new hardware?

Docker is just... faster on Linux. This has been the case for a while, and it's not just kernel-based stuff causing problems: APFS and MacOS' virtualization APIs play a pretty big role in weighing it down.

I'm kinda in the same boat, though. I got a work-issued Macbook that kinda just sits around, most of the time I'll use my desktop or reach for my T460s if I've got the choice. Mostly because I do sysops/devops/Docker stuff, but also because I never really felt like the Mac workflow was all that great. To each their own, I guess.

electroly5• 4 years ago

I don't think it's the virtualization API -- if you run docker in a Linux VM under macOS, it's vastly faster than running it via Docker Desktop. I use a Parallels VM for it. I honestly don't know what Docker Desktop is doing that makes it so much slower, even when you're not mounting anything from the host.

electroly5• 4 years ago

It's worth at least setting up a Linux VM for Docker. The performance improvement is huge and well worth the hassle. This works today; don't necessarily need to switch to running Linux on the metal. I don't think it's really about macOS, I think Docker Desktop just sucks.

topher515• 4 years ago

This is quite surprising and interesting to me. Can you provide links where I can read about this and/or try getting it set up for myself?

Nextgrid• 4 years ago

Docker on MacOS has always been a problem. On Linux it's essentially free, on MacOS you're running a Linux VM and have overheads of moving data between that and your main OS.

MacOS is somewhat bloated though. There's insane amounts of garbage running in the background.

rubyist5eva• 4 years ago

on MacOS you're running a Linux VM and have overheads of moving data between that and your main OS

MacOS is a special beast, because if I run a VM myself inside parallels using Docker (Ubuntu server with the docker snap, for example), I get nearly 5x the performance from that VM than I do just the "regular" docker for mac (including when all of the new mac specific experimental options are enabled for better performance).

It's pretty atrocious, I find it completely unusable and just stick to running my containers in Parallels and forwarding the ports.

jwilliams• 4 years ago

Docker is a lot slower on OSX. If you switch to ARM images that helps (especially stability).

A bigger impact is using the virtiofs (beta) file system mapping/sync. The existing one is horrendously slow to the point of being unusable.

ribit3• 4 years ago

Docker is definitely a lot slower

Docker on macOS is busted. I have heard that folks have had much better success with alternative implementation such as nerdctl. M1 virtualisation on itself is very fast and has almost no overhead.

I have read of massive performance improvement with Linux on m1pro though so it might be a swap to that when more distros are available.

I wouldn't hold my breath. Doubt that M1 distros will ever become more than an impressive tech demo.

Question is, has MacOS become bloated or not had attention to performance to make best use of the new hardware?

MacOS will always be "more bloated" than a basic Linux installation, it's an opinionated, fully features user OS that runs many more services in the background, e.g. code verification, filesystem event database, full-disk indexing etc. But the CPU/GPU performance is generally excellent. Of course, it boils down to the software you are running, if it is a half-assed port (like Docker on Mac) seems to be, it will eat up any advantage the hardware offers.

shp0ngle• 4 years ago

Docker is double slow on M1 mac compared to Intel linux.

First, you run virtual machine vs just running natively on Linux.

Second, you emulate x86; and, unlike other x86 that are emulated directly on M1, you emulate it in software through QEMU, because M1 cannot virtualize and run x86 at the same time

So it’s just really slow.

walrus013• 4 years ago

If we rescale (by double!) the vertical axis to fit here, a pretty amazing picture emerges:

That second chart with the "amazing" redline is comparing it to 2010 through 2017 era CPUs. Now compare it to a current generation zen3 based $599 AMD CPU in a $350 motherboard with $450 of RAM.

Or a $599 Intel 12th-gen "core" series.

For $3999 nevermind $7999 you can build one real beast of a workstation that fits in a normal midtower ATX case.

jachee• 4 years ago

Sure, but how many watts of power would those beasts need?

hu32• 4 years ago

As much as it needs. My time is worth a lot more.

ribit3• 4 years ago

The 28-core Xeon W-3275M is similar or even slightly better than either the 5950X or 12900KS on multicore workloads, so it won't change the picture much. Overall, M1 Ultra CPU is comparable to a Zen3 32-core Threadripper if you aggregate over various workloads, with the difference that the Ultra is a more flexible architecture and thus will be more capable in mixed scenarios.

Edit: I'd say that the Ultra is a much better bang for your buck than the Threadripper, unless you absolutely need humongous amounts of RAM or macOS does not work for you.

KennyBlanken2• 4 years ago

The other difference is that the M1 Ultra uses less than one quarter the power.

Over a few years of usage, that cost is not insignificant, especially if you are using air conditioning part or all of the year.

gigatexal3• 4 years ago

It's funny to read about folks on HN where the audience is likely in the top eschelons of earning percentiles complain about a bespoke vertically integrated solution like the Apple computers. Sure you can build a bespoke, quiet, watercooled or aircooled, system for a fraction of the Mac Studio but that's not the audience the thing was designed to target. Its existence does not prevent one from doing just that, finding parts, and building a pc oneself. This is for folks that need OSX, that like the build quality, and want ARM performance without the hassle. :Shrug:

LeifCarrotson2• 4 years ago

Or you can buy one off the shelf from, say, Puget Systems.

They're not vertically integrated, they're sourcing and assembling the same parts you and I could get (and some we can't: they have graphics cards in stock!)

That's for folks that don't need OSX, that like build quality in the form of sturdy, repairable, maintainable tools rather than glossy but glued-together disposable ones, and want Ryzen or Intel performance that will beat a Mac Studio without the hassle.

walrus013• 4 years ago

It's funny to read about folks on HN where the audience is likely in the top eschelons of earning percentiles complain about a bespoke vertically integrated solution like the Apple computers.

Just because you can afford an absurd $4000-8000 Apple computer doesn't mean you want to waste your money on it. I have a finite amount of money and choose to spend it on other things.

Apple is fine for like, a $1500 laptop. The macbook air is a fine product.

Not for an actual workstation.

supernova87a• 4 years ago

Could these processor leaps in performance inadvertently help us stop burning up so much coal in the quest for Bitcoin?

bigcheesegs• 4 years ago

No. The economics of Proof of Work mean that increases in compute per watt just lead to an increase in global hash rate. Total energy usage never goes down as long as it's sufficiently profitable to use the energy.

jillesvangurp• 4 years ago

It would be interesting to see a more serious effort by others to put together a high performance SOC system for gaming or workstations.

I'm writing this on a cheap core i5 system with intel iris xe graphics. Mediocrity is the name of the game for this type of laptop. Everything is mediocre. The screen, the performance, the touchpad, etc. The only good thing about this system: no blazing fans. That seems to be a thing with most SOC based PCs/laptops. Mediocre performance. Non SOC based solutions exist of course but they suck up a lot more power to the point where laptops start having cooling issues and have to do thermal throttling. I've experienced that with multiple intel based macs. You spend all that money and the system almost immediately starts overheating if you actually bother to use what you paid for.

I actually used to have a wooden plank that I used to insulate my legs from my mac book pro. It was simply too uncomfortable to actually use on my lap.

bodge50003• 4 years ago

I need a new computer. The smart move, especially considering the cost involved, is to go for what I know; a decent enough desktop build with 3 monitors, linux/windows dual boot. What I've used for years.

But it must be said, I've been really tempted by the Macs. I'm not sure why, the 3 main things I do with my personal computer (game dev, playing games, watching things) are things that linux/windows can do at least as good if not better than a Mac, and yet here I am, holding off for months just trying to be convinced into the apple ecosystem.

I think its probably just the simplicity of it. I really like the idea of replacing a load of bulk (3 monitors, the vesa mount to hold them, big keyboard, mouse, bulky tower and a metric tonne of cables) with just a single laptop that I can pickup and go with at a moments notice, though Im not sure of the practicalities of that, at least for me. I don't even travel much, it just seems nice.

Edit: I know that a lot of people have a mac and a desktop to fill all needs, but that somewhat defeats the simplicity of it for me. One bulky computer is simpler for me than one bulky computer for some things and another much smaller computer for others.

sofixa2• 4 years ago

But the screen estate isn't even close between three monitors and a laptop. A nice middle ground is a laptop with a dock with monitors, that way you have the external screens when you need them, and can also be on the move, on the same computer.

Of the three main things you do, i think only watching things is comparable between macOS and Windows/Linux. Gaming is nonexistent on macOS, unless you stream ( either a cloud service like Stadia/GeforceNow or locally from a PC with Steam in-house streaming/Parsec), and i can't imagine doing game dev somewhere you can't even test.

bodge50003• 4 years ago

But the screen estate isn't even close between three monitors and a laptop

Absolutely, which is a large part of the reason I'm not sure its practical for me, but its certainly true that one monitor is simpler than three. I suppose I could eventually get used to just having one monitor, it might even have some advantages, but I'm not sure I'd want to.

A nice middle ground is a laptop with a dock with monitors

This is what I've currently got (laptop is a razer blade). In my case at least it just ended up being more messy and difficult than a desktop. Most docks for example are designed for business use and therefore don't provide "clean" USB power, which I found causes issues with audio interfaces and DACs. I just ended up having a small, poorly cooled, difficult to upgrade desktop.

After that, I decided no more half measures. I either embrace the minimalism of a laptop alone, or return to a desktop. I know a dock works well for some people, sadly not for me.

Gaming is nonexistent on macOS... i can't imagine doing game dev somewhere you can't even test

This is exactly it (plus game dev for apple sillicon is still somewhat lagging behind), it doesnt seem to be a practical option, yet as I still, here I am.

Of course theres also non-apple laptops as well which would provide similar simplicity. I love my thinkpads, but I'm just not sure they're powerful enough for me to replace a desktop with, and as for the more powerful of the non-apple laptops, the blade has given me a mistrust of them (only had the thing for a year and the battery has bulged beyond being able to fit it in the chassis)

sjg007• 4 years ago

Are these things good for tensorflow and training 50GB AI models? Or is it better to stick with nvidia?

2022 Mac Studio (20-core M1 Ultra) Review

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