Latency, the delay between sending a request to an LLM API and receiving a response, is a critical factor in the user experience of LLM-powered applications.
High latency can make our app appear slow or unresponsive, negatively impacting user satisfaction and perceived reliability.
In this article, we will explore several strategies to mitigate latency issues in LLM deployment, ensuring our application remains responsive and efficient.
1. Stream Responses as They Are Generated
LLMs are autoregressive models, meaning they generate text one token at a time, rerunning the model for each new token. This can result in long wait times if we hold off displaying the response until it is fully generated.
Streaming the response as it is generated can significantly improve the user experience. By displaying partial results as they become available, users see that the application is working, reducing the perceived latency.
2. Choose Low-Latency API Providers
Selecting an API provider that prioritizes low latency can make a significant difference. Some providers, like Groq, use custom hardware to optimize latency and throughput, offering much faster response times. With certain…
