In recent years, generative artificial intelligence has revolutionized the field of conversational assistants, thanks to the implementation of base models that allow real-time interactions through text and voice. These technologies have found applications in numerous sectors, including customer service, healthcare, and education, where they facilitate natural conversations with users.
Most of these solutions operate on local devices, such as smartphones and computers, enabling fast processing of voice or text inputs. However, the intelligence behind these interactions resides in the cloud, where complex models are executed on powerful graphic processing units. This process begins with the user’s device locally processing the input, transforming it into text (in the case of voice interactions), and sending a notification to the cloud for the model to generate an appropriate response. This system aims to balance the powerful capabilities of cloud models with the agility enabled by local processing.
Despite progress, one of the biggest challenges continues to be reducing response latency, meaning the time elapsed from when the user finishes their input until they receive a response from the assistant. This period is divided into processing latency and time to first token, which measures the interval between the notification being sent and the arrival of the first response. Decreasing this time is essential to ensure smooth and natural interactions.
To address this issue, a hybrid architecture is proposed that extends Amazon Web Services (AWS) services from central regions to locations closer to users. This includes deploying additional entry points for edge inference, using dynamic routing to optimize traffic between the cloud and local zones, promising faster response times by adapting to network conditions and user proximity.
Local AWS zones, installed in densely populated areas, allow for more efficient data identification and low-latency processing, making them ideal for critical applications like artificial intelligence assistants. Tests have shown that implementing artificial intelligence models in these local zones can significantly reduce latency, an improvement crucial for ensuring smooth and natural interactions regardless of the user’s location.
Lastly, proper management of resources generated during this process is essential to prevent unnecessary charges and adopt best practices in cloud solution architecture. The local AWS zones undoubtedly represent a significant advancement in enhancing user experience and optimizing the performance of conversational artificial intelligence applications.
Source: MiMub in Spanish