Revive Your Old Graphics Card: A Complete Guide to Running Modern Large Language Models Locally on RDNA1 GPUs

This article introduces an open-source project that enables AMD RDNA1 architecture GPUs to run modern large language models on ROCm 6/7 through fixes and optimizations, giving new AI capabilities to old hardware.

In today's era of booming AI large models, hardware requirements seem to be getting higher and higher. The latest models often require the newest GPUs to run smoothly, making many users with old graphics cards feel left behind by the times. Especially AMD's RDNA1 architecture GPUs (such as RX 5500 XT, RX 5600 XT, RX 5700, etc.), although released only a few years ago, are gradually being marginalized in the official ROCm support list.

However, the open-source community never lets hardware "retire" easily. The project rdna1-gfx101x-rocm-llama-fix we're introducing today was created exactly to address this pain point. It allows RDNA1 architecture GPUs to regain the ability to run modern large language models, even working properly on ROCm 6 and ROCm 7.

To understand the value of this project, we first need to understand the predicament faced by RDNA1 GPUs.

AMD's ROCm (Radeon Open Compute) platform is the main toolchain for running AI workloads. However, with architectural iterations, AMD has gradually shifted its development focus to CDNA (data center) and newer RDNA architectures. As the first-generation RDNA product, RDNA1's position in the official support list is becoming increasingly awkward.

Specifically, the gfx101x series instruction set architecture used by RDNA1 faces the following issues in newer ROCm versions:

• Missing compiler support: The new version of the HIP compiler no longer fully supports gfx101x
• Runtime compatibility: Some components of ROCm 6 and ROCm 7 assume newer hardware features
• Kernel launch issues: Some GPU kernels cannot start correctly or produce incorrect results on RDNA1

As a popular large model inference framework, llama.cpp is known for its efficient CPU and GPU inference capabilities. It supports multiple backends, including CUDA, Metal, Vulkan, and ROCm. However, to make llama.cpp work properly on RDNA1, we need to solve problems in multiple stages from compilation to execution.

The core of this project is a series of carefully designed fixes and adaptations that allow RDNA1 GPUs to "trick" ROCm and llama.cpp into thinking they are interacting with compatible hardware.

The project provides the following key fixes for the specificity of the gfx101x architecture:

1. Wavefront size adaptation: RDNA1 uses 64-thread wavefronts, while some ROCm components assume different configurations
2. Memory model adjustment: Fixed issues related to atomic operations and memory barriers
3. Register allocation optimization: Special optimization for the register file size of RDNA1

The project provides a complete set of compilation scripts that automatically handle the following steps:

• Detect the ROCm version in the system (supports ROCm 6.x and 7.x)
• Apply necessary source code patches
• Configure correct compiler flags (such as -march=gfx1010, etc.)
• Handle compatibility issues of dependent libraries

This automated approach greatly lowers the user's entry barrier, allowing even users unfamiliar with low-level GPU programming to complete the compilation smoothly.