Does AMD GPU Use AI?

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When people think of AI hardware, NVIDIA often comes to mind due to its dominance in machine learning and deep learning applications. However, AMD—traditionally known for CPUs and gaming GPUs—has steadily been expanding its footprint in the AI domain. This leads to a common question among developers and businesses: Does AMD GPU use AI?

The short answer is yes. AMD GPUs are increasingly being used for AI tasks such as model training, inference, and high-performance computing (HPC) applications. AMD’s hardware, paired with its open-source software stack ROCm (Radeon Open Compute), makes AI workloads not only possible but increasingly competitive.

In this article, we’ll explore how AMD GPUs enable AI, how they compare with industry leaders, and in which scenarios they’re most effective.

Understanding AMD’s AI-Capable Hardware

Radeon Instinct and MI-Series

AMD has introduced several GPUs explicitly designed for AI and HPC:

  • Radeon Instinct MI25, MI50, MI60: Designed for machine learning workloads and server environments.
  • MI100, MI200, and MI250X: Feature support for FP64, FP32, BF16, and INT8 precision, which are crucial for mixed-precision training and inference.
  • MI300: AMD’s latest APU combines CPU and GPU cores on the same chip, tailored for AI and HPC workloads.

These GPUs include Matrix Cores—similar to NVIDIA’s Tensor Cores—for accelerating matrix operations in neural networks.

Prosumer GPUs (RX Series)

While Radeon RX GPUs like the RX 7900 XTX are geared toward gaming, they can still be used for AI experiments and smaller training tasks. However, they are not as optimized for deep learning as AMD’s MI-series cards.

AMD ROCm: The AI Software Stack

ROCm (Radeon Open Compute) is AMD’s open-source software platform for GPU-based high-performance and AI computing. Designed to provide a CUDA alternative, ROCm empowers developers to run AI workloads on AMD hardware with increasing parity to NVIDIA systems. As artificial intelligence continues to expand across industries, ROCm is AMD’s strategic investment in building a capable and flexible software stack tailored for both research and enterprise-level AI applications.

Core Components of ROCm

ROCm includes several libraries and tools that cater to the unique demands of machine learning and deep learning:

  • MIOpen: AMD’s equivalent to NVIDIA’s cuDNN, offering GPU-accelerated routines for convolutional neural networks. It supports forward and backward passes for layers like convolution, normalization, pooling, and activation.
  • hipBLAS, hipDNN, hipFFT, rocRAND, and rocSPARSE: A collection of GPU-accelerated math libraries essential for numerical computations, random number generation, sparse matrix processing, and more.
  • HIP (Heterogeneous-compute Interface for Portability): This portability layer allows developers to write CUDA-like code that compiles on both NVIDIA and AMD GPUs. HIP translates CUDA kernels into equivalent ROCm-compatible code.
  • ROCm Compiler and Runtime: Based on LLVM, the ROCm compiler supports C++, OpenCL, and HIP, enabling low-level kernel optimization and integration with machine learning frameworks.

Framework and Toolchain Support

ROCm has made significant strides in supporting popular AI frameworks. While NVIDIA’s CUDA stack has a head start, ROCm now integrates with major libraries:

  • PyTorch: Official ROCm support begins from version 1.8 onward. AMD maintains optimized wheels, and installation guides are available for ROCm-enabled systems.
  • TensorFlow: Experimental and community-supported builds are available for ROCm. While not as seamless as NVIDIA’s stack, AMD continues improving compatibility.
  • ONNX Runtime: ROCm supports ONNX models for cross-framework inference on AMD GPUs.
  • JAX and MXNet: Not officially supported as of now, though discussions around ROCm backends are ongoing in open-source communities.

Developer Tools and Monitoring

ROCm provides a growing suite of development and monitoring tools:

  • rocProfiler and rocTracer: Help monitor GPU performance and trace kernel-level activities.
  • ROCm SMI: Similar to NVIDIA’s nvidia-smi, this tool provides information on memory usage, temperature, power draw, and workload distribution.
  • Debugger and Profiler: Though not yet as feature-rich as NVIDIA Nsight, AMD’s tools are improving rapidly to support deeper insights into kernel and model execution.

Platform Requirements and Compatibility

ROCm primarily supports Linux (Ubuntu, RHEL, CentOS) and targets AMD’s MI-series and select Radeon RX GPUs. However, not all GPUs are compatible, and ROCm installation can require kernel modifications or specific hardware configurations.

Limitations to note:

  • Windows support is currently limited to development previews
  • Consumer-grade GPUs may have partial support or reduced performance
  • Framework builds can lag behind official releases, requiring manual compilation

Open-Source and Ecosystem Growth

Unlike NVIDIA’s proprietary CUDA stack, ROCm is fully open source. This aligns with academic and research institutions’ goals of reproducibility, auditability, and customizability. AMD’s growing contributions to GitHub and collaboration with institutions like Lawrence Livermore National Laboratory and Oak Ridge National Laboratory highlight ROCm’s growing credibility.

As part of its roadmap, AMD aims to:

  • Simplify ROCm installation and ecosystem integration
  • Increase out-of-the-box support for frameworks
  • Expand documentation and onboarding resources for new developers

AMD vs. NVIDIA in AI

Performance

  • NVIDIA: Superior for deep learning due to Tensor Core acceleration and mature software.
  • AMD: Competitive in inference and memory-intensive tasks, especially with MI250X and MI300.

Software

  • NVIDIA: CUDA, cuDNN, and TensorRT dominate AI ecosystems.
  • AMD: ROCm is open-source and growing, but requires manual setup and community support.

Cost

  • AMD offers better performance per dollar in many cases, making it a cost-effective option for non-critical AI workloads.

Developer Experience

  • NVIDIA is easier to use for beginners due to extensive documentation and toolchain.
  • AMD is better suited for experienced developers comfortable with open-source platforms.

Challenges and Limitations

  1. Framework Compatibility: Some features in PyTorch and TensorFlow are limited or delayed on ROCm.
  2. Driver Support: ROCm is not fully compatible with all AMD GPUs or Windows OS.
  3. Ecosystem Fragmentation: Fewer prebuilt containers and cloud integrations compared to NVIDIA.

The Future of AI on AMD

AMD’s roadmap indicates serious intent in AI and HPC:

  • MI300 APU: Integrates GPU and CPU with unified memory for faster AI workflows
  • FP8 support: Expected in future architectures to enhance transformer-based models
  • Partnerships: Collaborations with Meta, Microsoft, and cloud providers suggest expanding AI hardware presence

Conclusion

So, does AMD GPU use AI? Absolutely. While AMD may not yet match NVIDIA in every aspect of deep learning, it offers a viable, cost-effective alternative for many AI workloads—especially in inference, HPC, and open-source-driven environments.

As ROCm continues to mature and AMD doubles down on AI-specific hardware, the gap is closing. For developers, researchers, and businesses looking to diversify their AI infrastructure or optimize cost, AMD is no longer just a backup plan—it’s an emerging leader worth considering.

AI isn’t just NVIDIA’s game anymore—AMD is in the arena.

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