Background to the issue
In many application scenarios, such as financial transaction analysis or medical diagnosis, the latency of AI processing directly affects user experience and application effectiveness.
Specific solutions
- Selecting real-time processing mode: Kluster.ai provides real-time processing with sub-second latency for response time sensitive applications
- Optimize API calls: Ensure stable network connectivity and appropriately reduce the amount of data per request
- Monitoring Resource Usage: Real-time view of task execution status and identification of bottlenecks through the monitoring tools provided by the platform
- parameter tuning: Adjust parameters such as concurrency and batch size to find the optimal performance balance
advanced skill
For applications that are particularly latency-conscious, consider using Kluster.ai's Edge Computing feature (if supported by the platform) to deploy some of the computation tasks to nodes close to the user.
This answer comes from the articleKluster.ai: low-cost AI inference platform, sends 100$ DeepSeek-R1 credits, ~167 million tokens!The
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