How does latency optimization differ between training and inference in ChatGPT systems?

Training optimizes throughput and stability, while inference optimizes latency and responsiveness.

Training ChatGPT focuses on maximizing GPU utilization across large datasets using data parallelism, mixed precision, and distributed optimization. Latency is not critical during training.

Inference, however, prioritizes real-time response. Techniques like KV caching, batching, speculative decoding, and quantization are used to minimize latency per request.

Thus, training is compute-heavy and throughput-driven, while inference is latency-sensitive and user-facing.