Next Generation AI Development with the AMD MI300X

Simultaneous with the race to develop the most powerful AI models, is the race to create and deploy sufficiently capable machines to handle the required data workloads demanded by the ever-growing AI development apparatus. At the forefront of this race are two noteworthy competitors: NVIDIA and AMD. While we have covered NVIDIA GPUs such as the H100 extensively on this blog, development with AMD GPUs is an area we haven’t covered in detail. Starting with the launch of AMD Instinct MI300X Accelerators on DigitalOceans Bare Metal GPU services, it’s now time to change that.

In this article, we will look at these new offerings for DigitalOcean in detail. We will start with a brief overview of how AMD GPUs function and what makes AMD GPUs so effective for training and inference of Deep Learning models. We will conclude with a deep dive on the MI300X accelerators themselves now available on the platform. We hope this piece encourages everyone to consider AMD GPUs for their Deep Learning projects.

AMD GPUs are incredibly powerful tools for deep learning, leveraging their unique accelerator architecture and software stack, notably ROCm, to accelerate AI workloads. In recent years, their Instinct line of accelerators has particularly been turning heads.

AMD Instinct GPUs are built on the AMD CDNA architecture. This design leverages its Matrix Core Technologies, the basic computational accelerator unit of an AMD GPU, to support a broad range of precision capabilities. This versatility makes these machines ideal for handling HPC and Deep Learning workloads at scale. This precision capability ranges from the incredibly efficient INT8 and FP8 to the more demanding FP64 for High Performance Computing (HPC). To facilitate this, AMD uses top of the line interconnectivity. “The AMD Infinity Architecture, along with AMD Infinity Fabric technology, enables coherent, high-throughput unification of AMD GPU and CPU chiplet technologies with stacked HBM3 memory in single devices and across multi-device platforms.” (Source)

Making all this possible is the software from AMD: ROCm. ROCm is comparable to NVIDIA’s CUDA, including drivers, development tools, and APIs that enable GPU programming from low-level kernel to end-user applications for AMD development. There is a robust ecosystem of ROCm packages growing as AMD GPUs become more and more prevalent for development, especially around Large Language Models.

First released in late 2023, one of AMD’s most powerful GPUs is the MI300X. The AMD Instinct MI300X GPU is based on next-generation AMD CDNA 3 architecture, delivering high level efficiency and performance for the most demanding AI and HPC applications. These GPUs were especially optimized for the training and inference of LLM technologies.

The MI300X is designed with 304 high-throughput compute units, each responsible for executing a user’s kernels on CDNA-based accelerators. These allow for the concurrent processing of data across the GPU. This is in part thanks to the unique chip design and interconnectivity, allowing a 5.3 TB/s max. peak theoretical throughput. To facilitate this, the MI300X also features several AI-specific functions, including the new data-type support for INT8, specialized photo and video decoding, and an unmatched 192 GB of HBM3 memory. Furthermore, these GPUs feature state-of-the-art die stacking and chiplet technology in a multi-chip package which propels generative AI, machine learning, and inferencing.

The MI300X offers outstanding performance to the prior generation of releases from AMD. According to AMD, the MI300X offers up to 13.7x the peak AI/ML workload performance using FP8 with sparsity compared to prior AMD MI250X accelerators using FP16, and a 3.4x peak advantage for HPC workloads on FP32 calculations. This unprecedented jump in performance shows the exponential development happening to improve the machines put out by AMD.

The MI3000X boasts several advantages over comparable machines from competitors. As we can see from the graphic above, comparing performance on the NVIDIA H100 and the MI300X GPU on tasks using different precision formats. At each level, the MI300X has a higher processing rate, as measured by TFLOPs. Based on this information, as well as what we know about what is generally available using ROCm, it is evident that the MI300X GPU is a superior machine for both training and inference for Large Language Modeling to the NVIDIA H100.

On the other hand, there are far fewer packages written for Python and ROCm than there are for CUDA. As a result, a wider variety of ML operations will require kernel level programming to proceed, as there has just been less work done on the ecosystem in general. When doing tasks like video model training and inference, then NVIDIA will often just prove simpler to set up and use. That being said, the incredibly high memory for the MI300X may make this worth doing.

In our research, we have found AMD GPUs to offer a powerful alternative to NVIDIA GPUs for both AI and High Performance Computing. In particular, the MI300X is an incredibly powerful machine for training and inference of language models thanks to its incredibly high GPU memory.

We encourage all interested parties to reach out to our sales team about accessing the MI300X on DigitalOcean’s state of the art cloud infrastructure!