Sorry but could you please elaborate. I’ve been using nvidia forever in linux machines both at work and at home. I work in AI so using nvidia gpus is a must. Maybe there’s something that I missed but my experience has been pretty solid so far.
At home I am using openSUSE tumbleweed KDE wayland and at work ubuntu headless.
Yeah, Tumbleweed has a good track record with NVIDIA drivers in my experience. As with updates in general.
Although I still use X11 as Wayland still has graphical issues in some apps for me. Usually Flatpaks. That makes it unusable for me for the time being.
These days ROCm support is more common than a few years ago so you’re no longer entirely dependent on CUDA for machine learning. (Although I wish fewer tools required non-CUDA users to manually install Torch in their venv because the auto-installer assumes CUDA. At least take a parameter or something if you don’t want to implement autodetection.)
Nvidia’s Linux drivers generally are a bit behind AMD’s; e.g. driver versions before 555 tended not to play well with Wayland.
Also, Nvidia’s drivers tend not to give any meaningful information in case of a problem. There’s typically just an error code for “the driver has crashed”, no matter what reason it crashed for.
Personal anecdote for the last one: I had a wonky 4080 and tracing the problem to the card took months because the log (both on Linux and Windows) didn’t contain error information beyond “something bad happened” and the behavior had dozens of possible causes, ranging from “the 4080 is unstable if you use XMP on some mainboards” over “some BIOS setting might need to be changed” and “sometimes the card doesn’t like a specific CPU/PSU/RAM/mainboard” to “it’s a manufacturing defect”.
Sure, manufacturing defects can happen to anyone; I can’t fault Nvidia for that. But the combination of useless logs and 4000-series cards having so many things they can possibly (but rarely) get hung up on made error diagnosis incredibly painful. I finally just bought a 7900 XTX instead. It’s slower but I like the driver better.
Finally, thanks for the clear cut answer. I don’t have any experience with training on AMD but the errors from nvidia are usually very obscure.
As for using gpus other than nvidia, there’s a slew of problems. Mostly that on cloud where most of the projects are deployed, our options seem either limited to nvidia gpus, or cloud tpus.
Each AI experiment can cost usually in thousands of dollars and use a cluster of GPUs. We have built and modified our system for fully utilizing such an environment. I can’t even imagine shifting to Amd gpus at this point. The amount of work involved and the red tape shudder
Oh yeah, the equation completely changes for the cloud. I’m only familiar with local usage where you can’t easily scale out of your resource constraints (and into budgetary ones). It’s certainly easier to pivot to a different vendor/ecosystem locally.
By the way, AMD does have one additional edge locally: They tend to put more RAM into consumer GPUs at a comparable price point – for example, the 7900 XTX competes with the 4080 on price but has as much memory as a 4090. In systems with one or few GPUs (like a hobbyist mixed-use machine) those few extra gigabytes can make a real difference. Of course this leads to a trade-off between Nvidia’s superior speed and AMD’s superior capacity.
My experience (and many others’) has been contradictory to yours. AMD, on the other hand, pretty much always works without any fuss because they release first-party open source drivers.
Noted, but do you have any laptop model in mind that reasonably cheap and has a good AMD dGPU because it’s pretty rare and I can’t think of anything on top of my head
My advice would be to buy something cheap. Then if you have extra cash, get yourself a desktop gaming PC. A laptop just has too many sacrifices. Low power, poor thermals, and high cost.
AMD’s system-on-a-chips with RDNA2/3 pack almost the same punch as the discrete cards with the same architecture. See steamdeck as the prime example, but there’s quite a few boards, boxes and laptops with the same.
![[Solved] Sorry if this not related to this community because I don't know where to ask, but should I buy this laptop? and does it play nice with Linux.](https://lemmy.ca/pictrs/image/a4dfcf61-653f-46ae-bcc4-c3608b2bc33f.jpeg){kind=link}
Cant recommend anything with Nvidia.
Sorry but could you please elaborate. I’ve been using nvidia forever in linux machines both at work and at home. I work in AI so using nvidia gpus is a must. Maybe there’s something that I missed but my experience has been pretty solid so far.
At home I am using openSUSE tumbleweed KDE wayland and at work ubuntu headless.
Yeah, Tumbleweed has a good track record with NVIDIA drivers in my experience. As with updates in general.
Although I still use X11 as Wayland still has graphical issues in some apps for me. Usually Flatpaks. That makes it unusable for me for the time being.
These days ROCm support is more common than a few years ago so you’re no longer entirely dependent on CUDA for machine learning. (Although I wish fewer tools required non-CUDA users to manually install Torch in their venv because the auto-installer assumes CUDA. At least take a parameter or something if you don’t want to implement autodetection.)
Nvidia’s Linux drivers generally are a bit behind AMD’s; e.g. driver versions before 555 tended not to play well with Wayland.
Also, Nvidia’s drivers tend not to give any meaningful information in case of a problem. There’s typically just an error code for “the driver has crashed”, no matter what reason it crashed for.
Personal anecdote for the last one: I had a wonky 4080 and tracing the problem to the card took months because the log (both on Linux and Windows) didn’t contain error information beyond “something bad happened” and the behavior had dozens of possible causes, ranging from “the 4080 is unstable if you use XMP on some mainboards” over “some BIOS setting might need to be changed” and “sometimes the card doesn’t like a specific CPU/PSU/RAM/mainboard” to “it’s a manufacturing defect”.
Sure, manufacturing defects can happen to anyone; I can’t fault Nvidia for that. But the combination of useless logs and 4000-series cards having so many things they can possibly (but rarely) get hung up on made error diagnosis incredibly painful. I finally just bought a 7900 XTX instead. It’s slower but I like the driver better.
Finally, thanks for the clear cut answer. I don’t have any experience with training on AMD but the errors from nvidia are usually very obscure.
As for using gpus other than nvidia, there’s a slew of problems. Mostly that on cloud where most of the projects are deployed, our options seem either limited to nvidia gpus, or cloud tpus.
Each AI experiment can cost usually in thousands of dollars and use a cluster of GPUs. We have built and modified our system for fully utilizing such an environment. I can’t even imagine shifting to Amd gpus at this point. The amount of work involved and the red tape shudder
Oh yeah, the equation completely changes for the cloud. I’m only familiar with local usage where you can’t easily scale out of your resource constraints (and into budgetary ones). It’s certainly easier to pivot to a different vendor/ecosystem locally.
By the way, AMD does have one additional edge locally: They tend to put more RAM into consumer GPUs at a comparable price point – for example, the 7900 XTX competes with the 4080 on price but has as much memory as a 4090. In systems with one or few GPUs (like a hobbyist mixed-use machine) those few extra gigabytes can make a real difference. Of course this leads to a trade-off between Nvidia’s superior speed and AMD’s superior capacity.
The only two things that have ever been broken by an update for me are hyprland and Nvidia drivers, multiple times
Even then that seems to have stopped happening recently though they patched one of the reallg big issues this year
My experience (and many others’) has been contradictory to yours. AMD, on the other hand, pretty much always works without any fuss because they release first-party open source drivers.
Do you mean in terms of gaming? I admit that I don’t do much gaming on linux. Usually just development and browsing.
I also use proprietary nvidia drivers if that makes a difference.
No.
Noted, but do you have any laptop model in mind that reasonably cheap and has a good AMD dGPU because it’s pretty rare and I can’t think of anything on top of my head
What is “reasonably cheap”?
My advice would be to buy something cheap. Then if you have extra cash, get yourself a desktop gaming PC. A laptop just has too many sacrifices. Low power, poor thermals, and high cost.
Have you considered a Steam Deck?
Steamdeck is expensive like 2X the price, because they’re imported and not officially available in my country
Integrated GPU is not a dirty word anymore.
AMD’s system-on-a-chips with RDNA2/3 pack almost the same punch as the discrete cards with the same architecture. See steamdeck as the prime example, but there’s quite a few boards, boxes and laptops with the same.
If that’s the prime example you’re holding up to an RTX 2060, I’d hate to see a subprime example.