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 con...| AMD ROCm Blogs
Modern AI workloads often don’t utilize the full capacity of advanced GPU hardware, especially when running smaller models or during development phases. The AMD GPU partitioning feature addresses this challenge by allowing you to divide physical GPUs into multiple virtual GPUs, dramatically improving resource utilization and cost efficiency.| AMD ROCm Blogs
FlashInfer is an innovative framework designed to accelerate inference of large language models (LLMs). Given the explosive growth and adoption of models like DeepSeek R1, Llama 3, and Qwen 3, efficient inference is critical to meet the demands of real-world deployment. However, challenges such as GPU memory bottlenecks, throughput limitations, and latency remain significant hurdles for deploying these models at scale.| AMD ROCm Blogs