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

Together AI · 5 months ago

Together AI describes practical methods for reducing inference latency and cost through optimization techniques including quantization achieving 20-40% throughput improvement, distillation delivering 2-5× lower cost, speculative decoding providing 20-50% faster decoding, and dynamic GPU capacity shifting across endpoints. Teams can reduce TTFT by 50-100ms using regional inference proxies, eliminate GPU compute stalls through kernel fusion and better scheduling, and improve utilization on newer hardware like NVIDIA Blackwell through appropriate parallelism strategies. Organizations implementing these optimizations can achieve faster responses with lower cost per token and better predictability without requiring proportionally larger hardware clusters.