Can You Run Deep Live Cam on an AMD Radeon GPU? (Complete DirectML Guide)

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Can You Run Deep Live Cam on an AMD Radeon GPU? (Complete DirectML Guide)

Glowing red and black AMD Radeon silicon chip crackling with AI data energy

The artificial intelligence community has a long-standing bias towards NVIDIA hardware due to their proprietary CUDA architecture. For years, AMD users were entirely boxed out of the generative AI revolution, forced to run heavy computations on their CPUs. However, the open-source community has recently aggressively adapted frameworks like Deep Live Cam to run natively on "Team Red." Yes, you *can* use an AMD Radeon GPU, but navigating the software stack requires specific configurations.

The DirectML Breakthrough

Microsoft introduced DirectML (Direct Machine Learning), an API specifically engineered to tap into the raw computational power of any DirectX 12 compatible graphics card, completely bypassing the need for NVIDIA CUDA cores. This means AMD cards (from the RX 6000 and 7000 series) now possess a localized highway to process neural networks.

Installing the AMD Pipeline

If you launch the standard Deep Live Cam executable with an AMD card, it will default to CPU execution and run at a slide-show 2 FPS. To force the software to utilize your Radeon card:

  1. Ensure you download a repository explicitly labeled with `DirectML` support in its dependencies.
  2. You must install the specific `onnxruntime-directml` Python package instead of `onnxruntime-gpu`. The standard GPU package looks for CUDA; the DirectML package looks for DirectX 12 hardware.
  3. Inside the launcher UI, navigate to the "Execution Provider" dropdown and select `DML` (DirectML).

While an AMD RX 7900 XTX might lag slightly behind an RTX 4090 in isolated AI benchmarks, the gap is closing violently fast. With `DML` enabled, AMD users can officially join the real-time deepfake broadcasting arena without spending thousands on a new green card.

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