Posts tagged Recommendation Systems
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.
Accelerating Vector Search: hipVS and hipRAFT on AMD
- 13 November 2025
In this blog, you’ll get an introductory look at hipVS, AMD’s GPU-accelerated vector search library, and its relationship to hipRAFT, a foundational library used by hipVS and other ROCmDS projects. Using an interactive Jupyter notebook, you’ll explore four major vector search methods available in hipVS: Brute-Force KNN, IVF-Flat, IVF-PQ, and CAGRA—each illustrating different trade-offs in accuracy, performance, and memory. You’ll see how to build and query vector search indexes using the hipVS API for applications such as semantic search, recommendation systems, and RAG pipelines. Since the API is compatible with NVIDIA’s cuVS, migrating workflows to AMD hardware is seamless and requires minimal changes.
Training a Neural Collaborative Filtering (NCF) Recommender on an AMD GPU
- 30 April 2024
30, Apr 2024 by .