Overcoming the cost and complexity of AI inference at scale

Overcoming the cost and complexity of AI inference at scale The full-stack approach to AI performance Optimizing the AI model Optimizing the inferece runtime Enabling distributed, large-scale AI How Red Hat simplifies AI at scale Red Hat AI Learn more Get started with AI Inference About the author Brian Stevens More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share Operationalizing AI models at scale is a critical challenge for IT leaders. While the initial cost of training a large language model (LLM) can be significant, the real and often underestimated expense is tied to inference. AI inference —the process of using a trained model to generate an output—is the most resource-intensive and costly part of an AI application, especially because it happens constantly during production. Inefficient inference can compromise an AI project's potential return on investment (ROI) and negatively impact customer experience due to high latency. Effectively serving LLMs at scale requires a strategic, full-stack approach that addresses both the model itself and the serving runtime. A single approach is not enough. Achieving high performance and cost efficiency requires a dual focus—managing resource consumption and maximizing throughput. A strategic part of this approach is model compression, which reduces a model's size and resource requirements without compromising accuracy. Quantization is a key technique for model optimization. It reduces the precision of a model's numerical values—like its weights and activations—from standard 16-bit to lower formats such as 8-bit or 4-bit. This significantly shrinks the model’s memory footprint, allowing it to run on less hardware. Sparsity is another effective method, which makes models more efficient by removing unnecessary connections (weights).