AMD has made a significant effort to promote "AI on Radeon", as the firm has now pushed out support for ML development on RDNA 3 architectures in the latest ROCm update.
AMD Takes A Massive Step Making AI Development Accessible For An Average Radeon Consumer Through ROCm, Ultimately Increasing Adoption
It won't be wrong for an average consumer to think about AI applications or large-scale workloads being confined to data center architectures, but the industry is eventually evolving in a way that allows consumer GPUs to get a taste of AI computation as well. Systems such as TinyBox are built around AMD's RDNA GPUs to leverage the availability and save up some costs as well, but in the matter of software support, such systems lag severely.
Now AMD has made a huge effort, expanding support for AI/ML workloads so that they can be done on RDNA 3 GPUs for Linux 24.10.3 and ROCm 6.1.3. And interestingly, this change was anticipated months ago, following initial updates by AMD, which we discussed in a previous coverage.
Team Red says that researchers and developers working on environments such as PyTorch, ONNX Runtime, or TensorFlow can now leverage the newest ROCm 6.1.3 on Linux, allowing them to tap the performance of AMD's Radeon RX 7000 series GPUs or the workstation Radeon W7000 series GPUs, for their respective use case. The firm says that a solution based on the RDNA 3 architecture is cost-effective and provides a localized system, hence removing all the flaws present in a cloud-based service. Here's how AMD "pitches" the change:
More ML performance for your desktop
- With today’s models easily exceeding the capabilities of standard hardware and software not designed for AI, ML engineers are looking for cost-effective solutions to develop and train their ML-powered applications. Due to the availability of significantly large GPU memory sizes of 24GB or 48GB, utilization of a local PC or workstation equipped with the latest high-end AMD Radeon 7000 series GPU offers a robust/potent yet economical option to meet these expanding ML workflow challenges.
- Latest high-end AMD Radeon 7000 series GPUs are built on the RDNA 3 GPU architecture,
- featuring more than 2x higher AI performance per Compute Unit (CU) compared to the previous generation
- now comes with up to 192 AI accelerators
- offers up to 24GB or 48GB of GPU memory to handle large ML models
Other changes included in the latest ROCm 6.1.3 support for PyTorch, TensorFlow, and a wider range of source data for ONNX Runtime. This is a huge update for AMD's software stack, given that it adds in the in-demand AI libraries, eventually allowing for a wider adoption. The addition of support for RDNA 3 GPUs is certainly a step towards promoting an "edge AI environment," but there are performance constraints present with this move, and we'll be looking forward to potential benchmarks if they surface.
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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