Accelerated computing has taken the industry by storm—bringing dramatic changes to how software applications including AI and HPC are developed and tuned for maximum impact. GPGPU solutions, such as those from AMD, have been pivotal in enabling advances in both AI and HPC. In the AI space, their massive parallel processing capabilities have been critical in powering the AI revolution, by supporting key frameworks like PyTorch, TensorFlow, and JAX. At the same time, their raw computing power delivers the performance necessary to optimize HPC applications like GROMACS for molecular dynamics simulation, OpenFOAM for computational fluid dynamics, NAMD for molecular modeling, and Ansys Mechanical for computer-aided engineering. By providing a robust, unified software stack catering to both AI and traditional HPC workloads, these accelerated computing platforms are driving breakthroughs across scientific and industrial computing. The versatility of GPGPU architectures makes them the preferred solution for designing, developing, and deploying AI and HPC at scale. This unmatched capability is underpinned by robust software ecosystems, which include libraries and tools widely used in AI and machine learning. This comprehensive support makes the development and deployment process easier and more efficient. 

AMD continues to cornerstone the technology needed to enable the computational capabilities and possibilities of the world’s most powerful supercomputers across various sectors.

We are at a stage in our product ramp where we are consistently identifying new paths to unlock performance with our ROCM software and AMD Instinct MI300 accelerators. We have made a lot of progress since we recorded data in November that we used at our launch event and are delighted to share our latest results highlighting these gains.

These gains show that AMD Instinct MI300X with ROCm 6 continues to show leadership inference performance using the popular FP16 datatype and vLLM inference library compared to Nvidia H100 using TensorRT-LLM and FP16 or FP8 datatypes.

The newest family of AMD accelerators, the AMD Instinct™ MI300 Series, featuring the third-generation Compute DNA (AMD CDNA™ 3) architecture, offer two distinct variants designed to address AI and HPC markets.

Most Machine Learning (ML) engineers use single precision (FP32) datatype for developing ML models. TensorFloat32 (TF32) has recently become popular as a drop-in replacement for these FP32 based models. However, there is a pressing need to provide additional performance gains for these models by using faster datatypes (such as BFloat16 (BF16)) without requiring additional code changes.