TNG: Prefill and Decode for Concurrent LLM Requests - Latency Optimization

AI Impact Summary

The TNG team is optimizing LLM performance by implementing prefill and decode strategies to handle concurrent requests efficiently. This approach leverages the KV cache to parallelize prompt token calculations during the prefill phase, while maintaining sequential processing for output tokens during the decode phase. By understanding the distinct latency characteristics of these phases – particularly the longer prefill time and the memory-bandwidth bottleneck in the decode phase – the team can target latency goals of 100-300ms per output token and a time to first token of 3 seconds or less, crucial for interactive applications like chatbots.