Using Statistical Methods to Reliably Compare Algorithm Performance in Large Generative AI Models with JAX Profiler on AMD GPUs

Accelerate PyTorch Models using torch.compile on AMD GPUs with ROCm

DBRX Instruct on AMD GPUs

Accelerating models on ROCm using PyTorch TunableOp

A Guide to Implementing and Training Generative Pre-trained Transformers (GPT) in JAX on AMD GPUs

Deep Learning Recommendation Model on AMD GPU

Best practices of using Mamba on AMD GPUs with ROCm

This blog post demonstrates how to fine-tune and test three state-of-the-art machine learning Automatic Speech Recognition (ASR) models, running on AMD GPUs using ROCm.

TensorFlow Profiler measures resource use and performance of models, helping identify bottlenecks for optimization. This blog demonstrates the use of the TensorFlow Profiler tool on AMD hardware.

Stone Ridge Technology (SRT) pioneered the use of GPUs for high performance reservoir simulation (HPC) nearly a decade ago with ECHELON, its flagship software product. ECHELON, the first of its kind, engineered from the outset to harness the full potential of massively parallel GPUs, stands apart in the industry for its power, efficiency, and accuracy. Now, ECHELON has added support for AMDInstinct accelerators into its simulation engine, offering new flexibility and optionality to its clients.

Segment Anything with AMD GPUs

SmoothQuant model inference on AMD Instinct MI300X using Composable Kernel