There is also ZLUDA, which is open source and works on pre-compiled binaries. https://github.com/vosen/ZLUDA
this is closest thing we have to "cuda on non-nvidia" hardware
Alternative(s) to run CUDA on non-Nvidia hardware
21–30 of 82 posts
Re: Alternative(s) to run CUDA on non-Nvidia hardware
#22every CUDA alternative follows the same arc: bold launch, works for 3 operations, then a Discord server where the last message is 'any updates?' from 2024
Actually we launched in 2024 and the last message in our discord is definitely not that: https://discord.gg/KNpgGbTc38
Re: Alternative(s) to run CUDA on non-Nvidia hardware
#23Earlier quoted context omitted.
We're actually targeting all of it, and not just CUDA C++.
Ambitious but neat, good luck if nothing else :) If you were to guess, when do you think your Nsight Compute alternative might be ready with your own toolchain?
A guess would be some time next year — since our public launch our focus has generally been on API coverage and increasingly recently, on performance.
While performance improvements will always remain a target, we're soon at full coverage of the core CUDA APIs and will be shifting an increasing amount of effort towards developer tooling.
Re: Alternative(s) to run CUDA on non-Nvidia hardware
#24There is also ZLUDA, which is open source and works on pre-compiled binaries. https://github.com/vosen/ZLUDA
this is closest thing we have to "cuda on non-nvidia" hardware
We have a comparison page: https://docs.scale-lang.com/stable/manual/comparison/#zluda
Re: Alternative(s) to run CUDA on non-Nvidia hardware
#25Its easier to just get rid of your legacy code entirely and use Vulkan for compute, or have your compiler emit SPIR-V directly. No reason to tie yourself to Nvidia's moat.
Ports are very often incredibly difficult and very time consuming.
One of the biggest complaints we hear from the industry is "we tried to port to X and we could never complete it".
An established codebase can have years of refinement. It will take time to achieve the same with the port.
And with our compiler, just using cuda is no longer putting urself inside the moat :)
Re: Alternative(s) to run CUDA on non-Nvidia hardware
#26Its easier to just get rid of your legacy code entirely and use Vulkan for compute, or have your compiler emit SPIR-V directly. No reason to tie yourself to Nvidia's moat.
Vulkan tooling is light years behind what CUDA offers in 2026, across programming languages, IDE tooling, graphical debuggers and libraries.
Re: Alternative(s) to run CUDA on non-Nvidia hardware
#27Its easier to just get rid of your legacy code entirely and use Vulkan for compute, or have your compiler emit SPIR-V directly. No reason to tie yourself to Nvidia's moat.
A couple of years ago I evaluated both Vulkan and Cuda as a choice for future projects. I couldnt get anything done after a week in Vulkan, but had the test prototype project working after just a day in Cuda. Needless to say, I'd never ever pick Vulkan for any project after that experience. It's just way to needlessly overengineered and bloated.
I used to be big into Khronos API camp, even did my project thesis in OpenGL, up to the famous Long Peaks fail.
Vulkan ended up being the same extension spaghetti as its predecessor, and Khronos was only able to come up with something thanks to AMD offering Mantle, C++ bindings and a GLSL successor only came to be thanks to NVidia (Vulkan-hpp and Slang started at NVidia).
The "we build the specification", and then "the community builds the tools", leads to very poor experiences, and if it wasn't for LunarG own interests, there wouldn't even exist any kind of Vulkan SDK.
What they have going is naturally the vendor independence, however we can achieve the same with middleware with the benefit of much better developer experience.
Re: Alternative(s) to run CUDA on non-Nvidia hardware
#28Earlier quoted context omitted.
Unfortunately, Vulkan Compute doesn’t to all the things that OpenCL, SYCL, HIP or CUDA do.
Yep, there are inference stacks where it just does not work without cuda in any meaningful performance
Weird, since the most used open source inference engine is faster on Vulkan on platforms that offer multiple options, with the sole exception being Nvidia, due to poor Nvidia driver quality (which I am forced to assume is intentional, Nvidia wishes to maintain their moat after all).
Re: Alternative(s) to run CUDA on non-Nvidia hardware
#29Its easier to just get rid of your legacy code entirely and use Vulkan for compute, or have your compiler emit SPIR-V directly. No reason to tie yourself to Nvidia's moat.
A couple of years ago I evaluated both Vulkan and Cuda as a choice for future projects. I couldnt get anything done after a week in Vulkan, but had the test prototype project working after just a day in Cuda. Needless to say, I'd never ever pick Vulkan for any project after that experience. It's just way to needlessly overengineered and bloated.
Weird, most people have the exact opposite experience.
Having to deal with closed source opaque poorly documented stacks sucks.
Re: Alternative(s) to run CUDA on non-Nvidia hardware
#30Why should I not just port my kernel to Triton? What's the appeal of Scale?