Perplexity shows how to run monster AI models more efficiently on aging GPUs, AWS networks

Perplexity's development of software optimizations to run trillion parameter models efficiently on older, cheaper hardware using a variety of networking hacks, including AWS's Elastic Fabric Adapter (EFA), marks a significant breakthrough in reducing the costs associated with running large AI models. This development has significant implications for website owners who rely on AI-powered services, as it could lead to a reduction in the costs associated with running AI models, making it more feasible for them to integrate AI-powered features into their websites. With the ability to run large models on older hardware, website owners may see a decrease in the latency and increase in the efficiency of AI-powered services such as chatbots, content generation, and search functionality. To take advantage of these developments, website owners can consider the following actionable tips: monitor their AI bot traffic to identify areas where optimized models can be integrated, review their llms.txt files to ensure they are up-to-date with the latest optimizations, and explore cost-effective hardware options that can support the efficient running of large AI models, such as older GPUs and AWS networks with EFA.