LLM Latency Optimization: From 5s to 500ms (2026)

Interactive AI applications face significant user abandonment when response times exceed 2 seconds, emphasizing the importance of optimizing latency. The text differentiates between Time to First Token (TTFT), which is influenced by network latency, prompt processing, and queuing delays, and Inter-Token Latency (ITL), affected by memory bandwidth during token generation. Effective latency reduction involves distinct strategies for each, such as prefix caching and chunked prefill for TTFT, and quantization or speculative decoding for ITL. The document details a structured optimization approach, recommending prompt structuring, streaming, model selection, and quantization as initial steps before considering hardware upgrades. It underscores the importance of measuring latency baselines to identify bottlenecks and tailor optimization efforts effectively. Additionally, it suggests that while hardware improvements can enhance performance, software optimizations should be maximized first to avoid unnecessary costs. Finally, the text highlights the potential of model distillation for long-term latency improvements, especially in scenarios where inference optimization reaches its limits.

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