Key Techniques to Cut LLM Inference Costs and Latency at Scale

As large language models grow in adoption, their inference costs and response latency have become significant challenges for developers and businesses. Quantization reduces model weight precision to formats like INT4 or INT8, delivering up to 8x speed improvements with only minor accuracy trade-offs. KV Cache and speculative decoding offer additional gains, with the latter achieving 2–3x throughput by using a smaller model to draft tokens that a larger model then verifies. Prompt optimization and dynamic batching further reduce token usage and computational overhead. Experts recommend combining these techniques based on deployment priorities, whether speed, cost efficiency, or output quality.