Making AMD GPUs competitive for LLM inference

The article discusses the advancements in deploying large language models (LLMs) on AMD GPUs using ROCm, achieving competitive performance compared to NVIDIA GPUs. Specifically, the AMD Radeon RX 7900 XTX reaches 80% of the speed of the NVIDIA GeForce RTX 4090 and 94% of the RTX 3090 Ti for Llama2-7B/13B models. The article highlights the potential of AMD GPUs for LLM inference, emphasizing the importance of machine learning compilation (MLC) technology, which automates the optimization of machine learning workloads across various hardware backends, including ROCm, Vulkan, and OpenCL. This approach allows for high-performance universal deployment, making AMD GPUs a viable option for LLM inference.

The article also explores the use of MLC-LLM, a solution based on Apache TVM Unity, which facilitates Python-first development and universal deployment across different platforms. The authors note that while AMD GPUs have lagged due to a lack of software support, recent investments in the ROCm stack and emerging technologies like MLC are bridging the gap. The article concludes by discussing future work, including enabling batching, multi-GPU support, and integration with the PyTorch ecosystem, while emphasizing the importance of continuous innovation in machine learning system engineering to address hardware availability challenges.

Key takeaways:

MLC-LLM enables the deployment of LLMs on AMD GPUs using ROCm, achieving competitive performance compared to NVIDIA GPUs.
AMD's RX 7900 XTX offers similar memory and bandwidth specifications to NVIDIA's RTX 4090 and 3090 Ti, with a significant cost advantage.
Machine learning compilation (MLC) facilitates universal deployment across various hardware backends, including AMD and NVIDIA GPUs.
The study highlights the potential for AMD GPUs in LLM inference, with ongoing efforts to enhance support for diverse hardware and software ecosystems.

Making AMD GPUs competitive for LLM inference

Key takeaways:

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