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.…