Originally published on TechSaaS Cloud Originally published on TechSaaS Cloud LLM Inference Optimization: Cut Costs 80% Without Cutting Quality If you're serving LLM inference in production, you're probably paying 5-10x more than you need to. The default configurations of most serving frameworks optimize for simplicity, not efficiency. Three techniques — continuous batching, quantization, and speculative decoding — can cut your inference costs by 80% and latency by 60%. Here's how each works and when to use them. Technique 1: Continuous Batching The Problem with Naive Batching Traditional batching waits for N requests to arrive, then processes them together. This creates a latency-throughput tradeoff: small batches waste GPU cycles, large batches add waiting time. Continuous Batching (Iteration-Level Scheduling) Instead of batching at the request level, continuous batching schedules at the token level. New requests can join a running batch between token generations, and completed requests leave immediately.…