Optimizing LLM Inference Latency in Production Environments
Tactical ways to reduce time-to-first-token (TTFT) and token generation times in private cloud deployments.
User satisfaction correlates with performance. If your AI takes 5 seconds to reply, conversion rates drop. Optimizing LLM inference latency is critical for enterprise applications.
Key Levers for Optimization
1. Quantization: Compressing weights from 16-bit float (FP16) to 4-bit or 8-bit integers decreases memory usage, allowing models to fit on cheaper GPUs and run faster.
2. Speculative Decoding: Using a tiny draft model to guess tokens alongside the target model can speed up generation by up to 2x.
3. Serving Engines: Swap basic APIs for optimized engines like vLLM or TensorRT-LLM, which use PagedAttention to maximize throughput.