Inference Optimization Techniques for Real-Time Generative AI Applications

Abstract

Real-time generative AI applications -- interactive chatbots, voice assistants, live code completion, real-time translation, and interactive content generation -- impose strict latency requirements (typically < 500ms first-token latency, < 50ms inter-token latency) that current large language models cannot satisfy at full model quality without optimisation. The inference efficiency of generative models is fundamentally constrained by sequential autoregressive decoding -- each token must be generated before the next can begin -- and by the key-value (KV) cache memory requirements that grow linearly with sequence length. This paper proposes the Real-Time Generative Inference (RTGI) optimisation framework, an integrated suite of six inference acceleration techniques designed for sub-500ms end-to-end latency at production quality: continuous batching with dynamic request scheduling (CB-DRS) that maximises GPU utilisation across concurrent requests; speculative decoding with adaptive draft selection (SD-ADS) that uses context-appropriate draft models for maximum acceptance rate; flash attention with sliding window attention (FA-SWA) for memory-efficient long-context inference; quantised model serving with per-layer sensitivity adaptation (QMS-PSA) that applies optimal quantisation per layer; KV cache compression with semantic eviction (KC-SE) that retains semantically important context while compressing the KV cache; and token budget allocation with dynamic stopping (TBA-DS) that predicts and enforces response length to bound latency. RTGI is evaluated on four real-time application benchmarks -- interactive dialogue, live code completion, real-time translation, and voice response -- measuring time-to-first-token (TTFT), throughput (tokens/second), and quality retention. RTGI achieves mean TTFT = 284ms (SD = 42ms) -- meeting the 500ms threshold -- at 96.2% quality retention versus full-model inference, and 4.8x throughput improvement enabling substantially higher concurrent user capacity. The study contributes the RTGI specification, a Real-Time Inference Quality Index (RTIQ), and empirical guidance for deployment architects balancing latency, throughput, and quality for generative AI serving.