Idle Capacity to Agentic AI: Scaling Inference on AMD EPYC

Product Portfolio and Applications - AMD provides a comprehensive product portfolio ranging from training solutions like Helios MI450 servers and Instinct GPUs to inference workloads on EPYC processors[1][2] - EPYC servers efficiently handle various workloads including generative AI models for chatbots and summarizations (such as small 70 billion parameter models), recommendation systems, and classical machine learning like fraud detection and risk assessment[3][4][5] - AMD CPUs eliminate the need to transfer data to another compute engine by running AI applications directly alongside standard applications[8] Software Optimization and Performance - ZenDNN software library delivers up to 2x performance uplift over native non-ZenDNN software stacks for large language models and recommendation systems[10] - ZenDNN 6.0 supports the new Gen 6 processor Venice, providing native FP16 support and integration with Red Hat OpenShift AI[17][18][19] - ZenDNN combined with vLLM and 4-bit/8-bit quantization achieves up to 2.5x faster performance compared to native vLLM[14][15] - Venice processor delivers a 3x performance improvement in TPC-XAI benchmarks using a single-node scale factor 30 gigabytes configuration compared to previous generations[27][28][29] Market Trends and Architecture Shift - The industry is transitioning from GPU-dominated chatbot models to agentic AI workflows, shifting the CPU-to-GPU attach rate closer to a 1-to-1 ratio where CPUs handle a diverse set of application cluster workloads[40][41][44] - AMD open-source software approach and optimized libraries like AOCL drive performance improvements across k-means clustering, PCA, regression workloads, and similarity search on datasets like Cohere Wikipedia and SIFT 1 million dataset[33][34][35][38]