LLM Workflows Cut Inference Latency With 7 Strategies, From 4-Bit Quantization to 3x Speculative Decoding
Updated
Updated · KDnuggets · Aug 4
LLM Workflows Cut Inference Latency With 7 Strategies, From 4-Bit Quantization to 3x Speculative Decoding
2 articles · Updated · KDnuggets · Aug 4
Summary
Seven production tactics target the two main latency metrics in LLM serving—time to first token and time per output token—where delays can stretch from milliseconds in web apps to seconds in generative AI.
4-bit quantization, key-value caching and speculative decoding attack the model bottleneck directly: a 70B model in FP16 needs about 140 GB of VRAM, while draft-model verification can speed generation 2x to 3x.
Continuous batching and optimized engines such as vLLM, TGI and TensorRT-LLM reduce waiting by scheduling requests at the token level and improving memory handling and CUDA execution.
Pruning, distillation and prompt optimization trim unnecessary work before and during inference; distilling a 70B task into an 8B model can cut latency to tens of milliseconds, while prompt caching shortens prefill time.
The report frames latency reduction as a stack of trade-offs across quality, VRAM use, infrastructure cost and engineering complexity rather than a single fix.