Latency

In Depth

Latency is a critical performance metric for production AI systems. For LLMs, two latency measures matter: time-to-first-token (TTFT, how long before the first word appears) and inter-token latency (the delay between subsequent tokens during streaming). Acceptable latency varies by application — real-time voice assistants need sub-200ms TTFT, while batch document processing can tolerate seconds. Factors affecting latency include model size, hardware (GPU type), batch size, input length, network distance, and optimization techniques. Reducing latency often involves trade-offs with model quality or cost. Techniques include model distillation, quantization, geographic distribution (placing models closer to users), KV-cache optimization, and speculative decoding. Companies like Groq and Cerebras compete on offering the lowest-latency inference.

Latency infrastructure underpins the AI industry, enabling training and deployment of models at scale. Major providers including NVIDIA, AWS, Google Cloud, and Azure offer specialized infrastructure optimized for Latency workloads. Demand for infrastructure has driven a global chip shortage and billions of dollars in capital expenditure.

Understanding Latency is essential for anyone working in artificial intelligence, whether as a researcher, engineer, investor, or business leader. As AI systems become more sophisticated and widely deployed, concepts like latency increasingly influence product development decisions, investment theses, and regulatory frameworks. The rapid pace of innovation in this area means that today best practices may evolve significantly within months, making continuous learning a requirement for AI practitioners.

The continued evolution of Latency reflects the broader trajectory of artificial intelligence from research curiosity to production-critical technology. Industry analysts project that investments in latency capabilities and related infrastructure will accelerate as organizations across sectors recognize the competitive advantages offered by AI-native approaches to long-standing business challenges.