Posts by Claire Lee
Streamlining Recommendation Model Training on AMD Instinct™ GPUs
- 02 March 2026
Recommendation model training and inference workloads represent a significant portion of computational requirements across industries including e-commerce, social media and content streaming platforms. Unlike LLMs, recommendation models result in to complex and often imbalanced communication across GPUs, along with a higher load on the CPU-GPU interconnect. The ROCm training docker [1] now includes essential libraries for recommendation model training. This blog demonstrates the functionality and ease of training recommendation models using ROCm, along with suggestions for improved configuration of these workloads. We also highlight the inherent benefits of the large HBM size on AMD Instinct™ GPUs for recommendation workloads.
MaxText-Slurm: Production-Grade LLM Training with Built-In Observability
- 02 March 2026
Training large language models (LLMs) at scale on GPU clusters is not just a compute problem — it is an operations problem. Launching multi-node distributed training, keeping it running reliably, and diagnosing failures when they happen all require tooling that most training frameworks do not provide. MaxText-Slurm is an open-source launch system and observability stack that bridges this gap for MaxText on AMD Instinct GPU clusters managed by Slurm.