What are best practices to ensure fast agent responses and minimize system load

To ensure fast agent responses and minimize system load, consider the following best practices: 1. Geographical Deployment: Deploy your agents closer to where the model servers are located. For instance, if most models are deployed in the US, running your agents in US-Central or US-East can reduce latency significantly (source). 2. Optimize Model Parameters: Adjust model parameters such as temperature and maximum tokens to fine-tune the assistant’s responses. Lowering the temperature can make responses more deterministic, which might speed up processing (source). 3. Prewarm Function: Use the prewarm function to initialize models before they are needed, reducing latency when a session starts (source). 4. Load Balancing and Autoscaling: Implement load balancing and autoscaling to manage system load effectively. Ensure that your autoscaler is configured to handle spikes in demand without overcommitting resources (source). Would you like more detailed information on any of these points? Sources: - Livekit Slack Discussion - LiveKit Docs - Model Parameters - LiveKit Docs - Deployment and Scaling 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

Does pre warming slow down the initial conversation

It seems likely that prewarming is designed to improve the initial conversation flow by loading necessary models and resources before they are needed, rather than causing delays. According to the LiveKit documentation, the prewarm function is intended to initialize models and resources in advance, which should reduce latency when the session starts. However, if not configured properly, it might consume resources that could otherwise be used for active sessions, potentially affecting performance. Would you like more detailed information on how to configure prewarming effectively? Sources: - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1729256671.789729 - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1729017873.318869 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@millions-hair-24956 yes can you show how to effectively pre warm without consuming resources

My understanding is that effectively prewarming without consuming excessive resources involves careful configuration of the prewarm function and managing the number of idle processes. According to the LiveKit documentation, you can use the prewarm function to load models and resources in advance. However, it's important to balance the number of prewarmed processes to avoid unnecessary resource consumption. Here are some steps to consider: 1. Limit Idle Processes: Set the

num_idle_processes

to a reasonable number to ensure that only a necessary number of processes are prewarmed. This prevents excessive resource usage (source). 2. Efficient Prewarm Function: Ensure that the prewarm function is optimized to load only essential resources. Avoid loading large models or data that are not immediately needed for the initial conversation. 3. Monitor Resource Usage: Use monitoring tools to track CPU and memory usage during the prewarm phase to adjust configurations as needed. Would you like more detailed guidance on implementing these steps? Sources: - https://livekit-users.slack.com/archives/C07FY8WHGPM/p1729017873.318869 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.