Pinokio Users Report AMD GPU Not Utilized Despite Proper Drivers

Investigative Report: A growing number of users are encountering a critical performance bottleneck with Pinokio, an open-source AI automation framework, which appears to ignore dedicated AMD graphics hardware in favor of CPU processing—even when high-end Radeon RX 9070 XT GPUs are installed and fully updated. The issue, first detailed by Reddit user /u/JZKitty on the r/StableDiffusion forum, has sparked a wave of similar reports, highlighting a potential compatibility gap between Pinokio’s AI execution engine and AMD’s ROCm stack.

"Everything starts correctly, but when I try to process the request, it uses the CPU instead of the AMD GPU," wrote the user, who confirmed that drivers were current and the hardware—AMD’s latest consumer-grade GPU—is fully operational in other applications such as Blender and Stable Diffusion WebUI. The problem is particularly concerning given that GPU acceleration is essential for real-time text-to-speech (TTS) and generative AI workflows, where CPU-only processing can increase latency from seconds to minutes.

Pinokio, designed to simplify AI tool orchestration through a no-code interface, integrates multiple models including Ultimate TTS Studio, which relies heavily on tensor operations best handled by dedicated GPUs. According to Pinokio’s official documentation on processor optimization, the platform is engineered to detect and leverage available hardware accelerators, including Intel CPUs and NVIDIA GPUs via CUDA. However, the documentation makes no explicit mention of AMD GPU support, raising questions about whether ROCm (Radeon Open Compute) is even being tested or validated within the Pinokio ecosystem.

While Intel processors are referenced in Pinokio’s technical guides as viable for AI inference, there is no corresponding section detailing AMD GPU configuration steps, environment variables, or ROCm dependencies. This omission suggests that AMD GPU acceleration may be an untested or unsupported feature, despite the increasing market share of AMD graphics cards among hobbyists and creators who rely on open-source AI tools.

Community responses on Reddit have speculated that the issue may stem from Pinokio’s underlying AI runtime—potentially PyTorch or ONNX—failing to detect AMD hardware due to missing ROCm libraries or incorrect device enumeration. Users have attempted workarounds such as manually setting environment variables (e.g., ROCM_PATH, HIP_VISIBLE_DEVICES), reinstalling AMD drivers via Adrenalin, and forcing GPU selection via command-line flags—all without success.

Experts in AI infrastructure note that while NVIDIA’s CUDA platform dominates the AI ecosystem due to its maturity and widespread library support, AMD has made significant strides with ROCm, particularly in enterprise and cloud environments. However, consumer-facing tools like Pinokio often lag behind in cross-platform compatibility. "The absence of documented AMD support is not a technical impossibility—it’s a prioritization gap," said Dr. Lena Ruiz, an AI systems researcher at MIT. "When a platform markets itself as "universal AI automation," it must validate its claims across the full hardware spectrum, not just the most commercially dominant vendor."

As of this report, Pinokio’s development team has not issued an official statement regarding AMD GPU compatibility. Users are advised to monitor the project’s GitHub repository and official documentation for updates. In the interim, affected users may consider temporarily switching to NVIDIA hardware or utilizing cloud-based GPU instances for time-sensitive TTS and generative tasks.

The broader implication extends beyond Pinokio: as AI tools become more accessible to non-technical users, the burden of hardware compatibility must shift from end-users to platform developers. Without transparent, vendor-agnostic documentation and testing protocols, even the most powerful hardware risks becoming a silent bottleneck in the AI workflow.