Optimizing inference speed and costs: Lessons learned from large-scale deployments

To reduce inference latency without incurring massive costs, teams can adopt several strategies that optimize GPU usage and computing processes. These include maximizing work extraction from GPUs, eliminating compute stalls, and carefully selecting decoding techniques tailored to specific traffic patterns. Performance tuning should be an ongoing process rather than a one-time setup, allowing for faster responses and reduced cost per token. Key optimization techniques involve model-level adjustments like quantization and distillation, which lower memory usage and increase speed without compromising quality. Additionally, reducing network latency through regional inference proxies, addressing memory stalls, and improving decoding speed with methods like multi-token prediction and speculative decoding are crucial. Hardware choices, particularly with new options like NVIDIA Blackwell GPUs, and parallelism strategies play a significant role in enhancing efficiency. Dynamically shifting GPU capacity across endpoints based on real-time demand helps manage uneven traffic distribution effectively. Implementing these optimization strategies leads to lower costs, improved predictability, and enhanced user experience for interactive and real-time AI products.