ZLUDA, the famous "code porting" library, has now seen its revival and is now being developed to support "multi-GPU" compatibility, especially for AI workloads.
ZLUDA Is Now Being Developed Under An "Anonymous" Sponsor, Likely To Take AI Compute To a New Level
For those unaware, the ZLUDA library made headlines a few months ago, and it was initially designed to support Intel GPUs on NVIDIA's software stack, but eventually, AMD took care of the project and, together with multiple developers, molded it in a way that allowed them to break boundaries and access NVIDIA's CUDA onto their own AI hardware, which was seen as a massive breakthrough for the open-source community. However, AMD decided to scrap the project due to legal concerns, but ZLUDA is back, this time with a bang.
A new report by Phoronix claims that ZLUDA's original developer, Andrzej Janik, has announced that ZLUDA is back to its development phase under an anonymous sponsor, but there is pretty interesting stuff to come ahead for ZLUDA, which we'll talk about next. It is claimed that ZLUDA is now being tuned to allow multi-GPU support, which means that the library will be compatible with any architecture, whether AMD or NVIDIA. Now, instead of optimizing for professional workloads, ZLUDA will focus on AI/ML workloads.
This means that the ZLUDA will now support libraries such as Llama.cpp, PyTorch, and TensorFlow, and the special focus is reworking NVIDIA code paths to make them compatible with other GPU vendors. The involved developers have already started testing too with AMD's RDNA GPUs, and it is said that ZLUDA will support RDNA1+ architectures along with ROCm 6.1+ compute stack support; hence, for the AMD compute portfolio, ZLUDA will prove to be a game-changer.
Now, in terms of when the library will appear back in the markets, the developer Janik claims that it will take around a year before the library comes back in shape. If the project proves to be a success, we might see the exclusivity boundaries present in AI software stacks break, allowing architectures to leverage each other's capabilities for an optimal end result.
About the author: Muhammad Zuhair is a hardware and technology reporter for Wccftech, specializing in the semiconductor industry and the complex interplay between technology, manufacturing, and geopolitics. His coverage focuses on the corporate strategies and technological roadmaps of industry giants like TSMC, NVIDIA, Samsung, and Intel. Zuhair's expertise lies in deconstructing complex topics such as fabrication nodes (e.g., 2nm process), the economic impact of policies like the CHIPS Act, and the strategic development of AI infrastructure from NVIDIA, AMD and Intel.
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