Beyond GenAI Model Training: Reducing Cost and Latency and Improving Scalability of AI Inferencing Workloads in Production

The IDC Perspective explores the challenges and innovations in scaling generative AI (GenAI) inference workloads in production, emphasizing cost reduction, latency improvement, and scalability. It highlights techniques like model compression, batching, caching, and parallelization to optimize inference performance. Vendors such as AWS, DeepSeek, Google, IBM, Microsoft, NVIDIA, Red Hat, Snowflake, and WRITER are driving advancements to enhance GenAI inference efficiency and sustainability. The document advises organizations to align inference strategies with use cases, regularly review costs, and partner with experts to ensure reliable, scalable AI deployment."Optimizing AI inference isn't just about speed," says Kathy Lange, research director, AI Software, IDC. "It's about engineering the trade-offs between cost, scalability, and sustainability to unlock the potential of generative AI in production, where innovation meets business impact."