What is the best strategy for optimizing latency and compute utilization during real-time inference of LLMs?

The optimal strategy for improving latency and compute utilization during real-time inference of large language models (LLMs) lies in setting up a distributed model architecture. This approach allows the workload to be divided across multiple computational resources, which can significantly enhance the responsiveness of the system. In real-time scenarios, distributed architectures can facilitate parallel processing of requests, thereby reducing bottlenecks and improving overall throughput.

By leveraging distributed systems, organizations can not only optimize for faster response times but also make better use of their computational resources. This is crucial in applications requiring immediate feedback, as waiting for a single instance of a model to process data could lead to delays. Distributed systems can dynamically allocate resources based on demand, ensuring that the models scale effectively and maintain high performance during peak usage periods.

In comparison to other options, local deployments might limit flexibility and could lead to higher latency since they are constrained to single machine processing. Utilizing static models often implies working with fixed architectures that may not fully utilize available resources or adapt to varying loads. Limiting model complexity may reduce computation needs but could sacrifice performance and the richness of outputs that more complex models can provide. Thus, a distributed model architecture emerges as the most robust solution for real-time inference challenges in LLMs.