AMD has released a guide detailing how to run large language models (LLMs) on Radeon GPUs, emphasizing the use of open-source tools and GPU acceleration. The guide highlights the accessibility and efficiency of deploying state-of-the-art models on both integrated and discrete Radeon GPUs. Users can leverage a variety of frameworks and tools to run LLMs locally, ensuring privacy and offline operation.
The guide outlines several approaches, including Lemonade, LM Studio, Ollama, and llama.cpp. Each tool offers unique strengths, from user-friendly interfaces to deep configurability. For example, Lemonade provides a unified platform for running GGUF and ONNX models, while LM Studio allows users to download, serve, and interact with models. The guide also includes step-by-step instructions for converting PyTorch checkpoints into GGUF format for compatibility with these tools.
The guide emphasizes the importance of setting up the development environment with tools like Git, Git LFS, Conda, and CMake, along with hardware requirements such as an AMD Radeon GPU. It also details the process of building llama.cpp with the ROCm backend for optimal performance on Radeon hardware. The guide includes notes on setting environment variables for multi-GPU systems to specify which GPU to use.
Source: amd
