Posts by Anuya Welling

From Ingestion to Inference: RAG Pipelines on AMD GPUs

Retrieval-Augmented Generation (RAG) is a machine learning architecture that enhances Large Language Models (LLMs) by combining generation with information retrieval from external sources. It was introduced to address the limitations of traditional LLMs by allowing them to access and utilize up-to-date information from internal and/or external knowledge bases. When a query is received, RAG first retrieves relevant documents or information from its knowledge bases, then uses this retrieved context alongside the query to generate more accurate and informed responses. This approach helps reduce hallucinations (making up information) common in standard LLMs, while also enabling the model to access current information not present in its original training data. RAG has become particularly valuable in enterprise applications, such as customer support systems, research assistants, and documentation tools, where accuracy and verifiable information are crucial.

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DGL in the Real World: Running GNNs on Real Use Cases

In our previous blog post, we introduced the Deep Graph Library (DGL) and highlighted how its support on the AMD ROCm platform unlocks scalable, performant graph neural networks (GNNs) on AMD GPUs. That post focused on the why — the growing relevance of graph workloads and what it means to bring that capability to AMD’s accelerated computing ecosystem.

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Graph Neural Networks at Scale: DGL with ROCm on AMD Hardware

This blog introduces the Deep Graph Library (DGL) and explores its significance on AMD hardware for enabling scalable, performant graph neural networks.

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