What is inference engineering? Deepdive (newsletter.pragmaticengineer.com)

Inference engineering is emerging as a critical area in the AI/ML landscape, as highlighted by Philip Kiely's new book, “Inference Engineering.” This discipline focuses on optimizing the inference phase of large language models (LLMs), where models generate outputs based on inputs. With the proliferation of open LLMs, such as Kimi 2.5 and others, software engineers now have the ability to enhance model performance at scale. This shift is significant because it democratizes access to sophisticated AI capabilities, enabling more engineers to implement and refine inference techniques in their applications, thereby accelerating innovation across the industry. Key techniques within inference engineering include batching, caching, quantization, and leveraging high-performance GPU infrastructure. Companies are increasingly adopting autoscaling solutions like Kubernetes to manage model serving efficiently, while also exploring approaches like speculative decoding and disaggregation to enhance execution speed. As organizations transition from closed models, which are costly and less flexible, to open models with easier access and lower operational costs, the demand for skilled inference engineers is on the rise. This growing field presents vast opportunities for innovation and career advancement, as the AI/ML community increasingly embraces inference as a cornerstone of competitive advantage in developing AI-driven products.