Sizing the infrastructure for LLM inference is essential due to the computational demands, high memory requirements, and unique characteristics of these models. Properly sizing the infrastructure ensures efficient handling of LLMs' massive parameter count during inference, avoiding out-of-memory errors and achieving low latency. It enables resource efficiency, scalability, and cost optimization by accommodating varying workloads and selecting optimal configurations. Overall, proper infrastructure sizing for LLM inference ensures optimal performance, scalability, and user experience while managing operational costs effectively and removing any resource bottlenecks.
To characterize various GPT models and gain insights into their inference performance, our goal is to provide sizing guidelines. Consider several critical factors when sizing an infrastructure for LLM model inference: