A strategic approach to AI inference performance

A strategic approach to AI inference performance Optimizing the inference runtime Optimizing the AI model Red Hat AI: Putting the strategy into practice Get started with AI for enterprise: A beginner’s guide About the author Carlos Condado 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 Training large language models (LLMs) is a significant undertaking, but a more pervasive and often overlooked cost challenge is AI inference. Inference is the procedure by which a trained AI model processes new input data and generates an output. As organizations deploy these models in production, the costs can quickly become substantial, especially with high token volumes, long prompts, and growing usage demands. To run LLMs in a cost-effective and high-performing way, a comprehensive strategy is essential. This approach addresses two critical areas: optimizing the inference runtime and optimizing the model itself. Basic serving methods often struggle with inefficient GPU memory usage, suboptimal batch processing, and slow token generation. This is where a high-performance inference runtime becomes critical. vLLM, is the de facto, open source library that helps LLMs perform calculations more efficiently and at scale. vLLM addresses these runtime challenges with advanced techniques, including: Continuous batching : Instead of processing requests one by one, vLLM groups tokens from multiple sequences into batches. This minimizes GPU idle time and significantly improves GPU utilization and inference throughput. PagedAttention : This memory management strategy efficiently handles large key-value (KV) caches. By dynamically allocating and managing GPU memory pages, PagedAttention greatly increases the number of concurrent requests and supports longer sequences without memory bottlenecks.