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Open source generally wins once the state of the art has stopped moving. When a field is still experiencing rapid change closed source solutions generally do be
by tormeh 3y ago
Open source generally wins once the state of the art has stopped moving. When a field is still experiencing rapid change closed source solutions generally do better than open source ones. Until we somehow figure out a relatively static set of requirements for running and training LLMs I wouldn’t expect any open source solution to win.
- kelipso 3y agoThat doesn't really make sense. Pretty much all LLMs are trained in pytorch, which is open source. LLMs only reached the state it is now because many academic conferences insisted that paper submissions have open source code attached to it. So much of the ML/AI ecosystem is open source. Pretty much only CUDA is not open source.
- deleted 3y ago[deleted]
- DeathArrow 3y ago>Pretty much only CUDA is not open source. What stops Intel to make their own CUDA and plug in into pytorch?
- mschuster91 3y agoThe same shit as always, patents and copyright.
- nemothekid 3y agoCUDA is huge and nvidia spent a ton in a lot of "dead end" use cases optimizing it. There have been experiments with CUDA translation layers with decent performance[1]. There are two things that most projects hit: 1. The CUDA API is huge; I'm sure Intel/AMD will focus on what they need to implement pytorch and ignore every other use case ensuring that CUDA always has the leg up in any new frontier 2. Nvidia actually cares about developer experience. The most prominent example is Geohotz with tinygrad - where AMD examples didn't even work or had glaring compiler bugs. You will find nvidia engineer in github issues for CUDA projects. Intel/AMD hasn't made that level of investment and thats important because GPUs tend to be more fickle than CPUs. [1] https://github.com/vosen/ZLUDA https://github.com/vosen/ZLUDA