Use ROCm™ on Radeon™ GPUs#

Turn your desktop into a Machine Learning platform with the latest AMD Radeon™ 7900 series GPUs

AMD has expanded support for PyTorch Machine Learning Development on RDNA™ 3 GPUs with Radeon Software for Linux with ROCm 5.7!

Researchers and developers working with Machine Learning (ML) models and algorithms using PyTorch can now also use ROCm 5.7 on Linux® to tap into the parallel computing power of the latest AMD Radeon 7900 series desktop GPUs which are based on the AMD RDNA 3 GPU architecture.

A client solution built on powerful high-end AMD GPUs enables a local, private and often more cost-effective workflow to develop ROCm and train ML (PyTorch) for the users who previously relied solely on cloud-based solutions.

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 Radeon 7900 series GPU offers a robust/potent yet economical option to meet these expanding ML workflow challenges.

  • Radeon 7900 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.

AMD ROCm 5.7 ML Development on the Desktop


Based on AMD internal measurements, November 2022, comparing the Radeon RX 7900 XTX at 2.505 GHz boost clock with 96 CUs issuing 2X the Bfloat16 math operations per clock vs. the Radeon RX 6900 XT GPU at 2.25 GHz boost clock and 80 CUs issue 1X the Bfloat16 math operations per clock. Results may vary. RX-821.

Migrate your application from the desktop to the datacenter

  • ROCm is the open-source software stack for Graphics Processing Unit (GPU) programming. ROCm spans several domains: General-Purpose computing on GPUs (GPGPU), High Performance Computing (HPC) and heterogeneous computing.

  • The latest AMD ROCm 5.7 software stack for GPU programming unlocks the massively parallel compute power of these RDNA 3 GPUs for use with PyTorch 2.0.1, one of the leading ML frameworks. The same software stack also supports the AMD CDNA™ GPU architecture so developers can migrate their PyTorch-based applications into the datacenter.

Freedom to customize

ROCm is primarily Open-Source Software (OSS) that allows developers the freedom to customize and tailor their GPU software for their own needs while collaborating with a community of other developers, and helping each other find solutions in an agile, flexible, rapid and secure manner. AMD ROCm allows users to maximize their GPU hardware investment. ROCm is designed to help develop, test and deploy GPU accelerated HPC, AI, scientific computing, CAD, and other applications in a free, open-source, integrated and secure software ecosystem.


Visit AMD ROCm Documentation for the latest on ROCm.