New RunLLM response generated: >>>*# Question* Hi all! I'm new to Flyte. My workloads consist of slow-running tasks, for which Flyte is perfectly suited, as well as fast-running tasks. The latter tasks might need some set-up time, however, such as loading a neural network to GPU (for model inference). Before moving to Flyte, we've been using a HTTP server to execute those tasks, e.g. with FastAPI. Running these as Flyte tasks seems to add unnecessary overhead. What would be the best/idiomatic way of handling this? I'm currently thinking of running a HTTP server, and then running a flyte task to make a HTTP request. This seems a little duplicious though. Thanks! # Answer Running fast tasks with Flyte can indeed introduce overhead due to containerization. A common approach is to use a service-based architecture for such tasks. According to Flyte's discussion forum, you can maintain a pool of pre-warmed containers or use a service like FastAPI to handle fast tasks. This allows you to avoid the overhead of spinning up new containers for each task. Would you like more detail on how to implement this approach? Sources: - Flyte's discussion forum Slack Link https://.slack.com/archives/CP2HDHKE1/p1731089398.229189 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.