LLM speed benchmarks: metrics & infrastructure guide

LLM speed benchmarks are crucial for optimizing user experience by predicting and preventing delays in production environments, as the process of auto-regressive, token-by-token generation involves distinct performance phases each with unique bottlenecks. Key metrics that influence LLM inference speed include time to first token (TTFT), output speed, inter-token latency (ITL), end-to-end latency, and system throughput, all of which cater to different user experiences and application needs. Hardware and software optimizations, such as memory bandwidth and quantization, significantly impact inference speed, while model architecture like Mixture of Experts (MoE) offers trade-offs between capability and efficiency. Semantic caching, which bypasses inference by using vector embeddings to return cached responses, can dramatically reduce latency and costs for workloads with semantic repetition. Redis serves as a real-time data platform supporting retrieval, semantic caching, and operational data layers, providing a comprehensive solution for optimizing LLM inference in production GenAI applications.