AMD ROCm 6.3

An open-source platform from AMD that enhances performance for AI, ML, and HPC workloads.

CommonProductProductivityHPCGPU Acceleration

AMD ROCm™ 6.3 is a significant milestone for AMD's open-source platform, introducing advanced tools and optimizations to boost AI, machine learning (ML), and high-performance computing (HPC) workloads on AMD Instinct GPU accelerators. ROCm 6.3 aims to enhance developer productivity for a wide range of customers, from innovative AI startups to industry-driven HPC sectors.

SGLang Integration: A new generation runtime optimized for AMD Instinct GPUs to enhance inference performance of generative models.
FlashAttention-2: Optimized for ROCm 6.3
enabling faster and more efficient training and inference for Transformer models.
AMD Fortran Compiler: Provides GPU acceleration capabilities for Fortran-based HPC applications.
Multi-node FFT Support: Introduced in rocFFT for high-performance FFT computation in distributed computing.
Enhanced Computer Vision Libraries: Including support for AV1 codecs and GPU-accelerated JPEG decoding.

The target audience includes AI developers
data scientists
HPC researchers
and enterprise IT professionals. These users require a high-performance computing platform to handle complex AI and HPC workloads. ROCm 6.3 provides the necessary tools and optimizations to enhance their productivity and application performance.

AI startups use SGLang on ROCm 6.3 to deploy LLMs and VLMs
achieving a sixfold increase in inference performance.
The HPC industry leverages Transformer models optimized with FlashAttention-2 to accelerate model training and inference processes.
Enterprise IT professionals migrate legacy Fortran code to GPU-accelerated platforms using the AMD Fortran compiler without the need to rewrite complex code.

1. Visit the AMD ROCm documentation center to understand the installation and configuration guidelines for ROCm 6.3.
2. Follow the guidelines to install ROCm 6.3 and ensure the system environment meets the requirements.
3. Utilize the tools and libraries provided in ROCm 6.3
such as SGLang and FlashAttention-2
to develop and optimize AI models.

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