Best practices to accelerate inference for large-scale production workloads

Running large language models (LLMs) in production requires more than just deploying a model; it involves optimizing inference infrastructure to ensure scalability and efficiency. Key components include optimized kernels that maximize GPU utilization, quantization strategies that reduce costs while preserving quality, speculative decoding techniques to lower latency, and infrastructure designs that handle real-world traffic patterns. The competitive AI landscape demands rapid responses, reshaping infrastructure needs as inference costs become a significant part of operational expenses. Companies must optimize time to first token and throughput to maintain sustainable unit economics and reduce costs per request. Successful companies rethink how models, runtimes, and hardware interact, as off-the-shelf frameworks often leave performance potential untapped. Understanding inference economics is crucial, as it forms the foundation of a company's profit and loss statement. Meeting customer expectations for quick responses while maintaining business fundamentals is essential, as latency directly affects user experience and product feasibility. Speculative decoding allows models to generate text faster by proposing multiple tokens for verification, optimized kernels enhance GPU performance, and custom or adaptive speculators improve speed and cost-efficiency. These strategies collectively enhance AI product competitiveness by reducing latency and operational costs.