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

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This post details lessons learned from large-scale deployments on optimizing inference speed and costs. Key strategies discussed include: maximizing GPU utilization through quantization (FP8/FP4) and distillation (e.g., DeepSeek-R1 variants), reducing network latency with regional inference proxies, minimizing unnecessary compute by optimizing kernel efficiency, MoE layer execution, prefill paths, and batching/scheduling, leveraging decoding optimizations like MTP and speculative decoding (e.g., ATLAS), selecting appropriate hardware (Blackwell, GB200) and parallelism strategies (tensor, expert), and dynamically shifting GPU capacity across endpoints based on real-time demand. It emphasizes that cost efficiency is achieved through optimizing inference execution rather than solely increasing hardware.