Lightning AI has introduced a new PyTorch model compiler named Thunder, which offers significant improvements in AI model training efficiency. Thunder, highlighted for its ability to work alongside torch.compile, promises over a 10% improvement in training throughput compared to existing methods and supports mixed hardware executors. This innovation has been met with enthusiasm in the tech community, with comparisons being drawn to the transformative impact of Visual Basic and Hypercard in their eras. Additionally, Lightning Studios, associated with Lightning AI, has been lauded for providing a comprehensive development environment that simplifies AI development, likened to an 'iOS for AI developers'. The introduction of Thunder is seen as a potential milestone in software engineering, akin to an Industrial Revolution, with its capacity to make PyTorch models up to 40% faster, significantly accelerating the development process.
Lightning AI launches next-gen AI compiler ‘Thunder’ to accelerate model training https://t.co/GTGSFfHw7T https://t.co/fWxORwnpfa
Meet Thunder, the new compiler for PyTorch! Make PyTorch models up to 40% faster (it's still early days 😳) https://t.co/YRAkFSG5m5
Lightning Studios is being called the "iOS" for AI developers 🤯🤯🤯 All the tools you need to develop and ship AI in a single, unified experience. https://t.co/XzFJzOMchJ https://t.co/MzKQJYwysv
Lightning AI debuts source-to-source Thunder compiler to accelerate AI training https://t.co/qMXBZO6M9q
I’m very sus of AI startups without demos, but… Today I met a co where the founder literally made me describe an app in text and 20mins later, the entire app, backend and frontend was made, and fully functional. Feels like the Industrial Revolution of software engineering.
This is a company to watch. I got a demo of its development environment and I instantly thought "this is the Visual Basic or Hypercard of the AI age." https://t.co/T9yZUZ1CbB
Share posts from Facebook of @algo_diver https://t.co/9USaqExz5O has introduced Thunder, a PyTorch model compiler. It can be used alongside torch.compile and offers over 10% improvement in training throughput compared to existing methods, supporting mixed hardware executors. https://t.co/EamtwX7JqU