Inference Performance: How to Make Large Language Models Faster and Cheaper

When you ask a large language model a question, inference performance, the speed and efficiency with which a trained model generates responses during real-world use. Also known as LLM inference speed, it’s what separates a model that feels instant from one that makes you wait—and it’s often the difference between a tool people use and one they abandon. Most people think bigger models are better, but if they take 10 seconds to answer, no one cares how smart they are. Real-world AI needs to be fast, cheap, and reliable—and that’s all about inference performance.

What actually slows down inference? It’s not just the model size. The real bottleneck is the KV cache, a memory structure that stores past attention outputs to avoid recalculating them on every new token. Key-value cache now takes up more memory than the model weights themselves in production systems. Then there’s transformer compute cost, the total processing power needed to run attention layers during text generation. attention computation scales poorly with longer inputs, making long conversations or document summaries expensive. And if you’re not using FlashAttention, an optimized attention algorithm that reduces memory bandwidth and speeds up computation. FlashAttention v2, you’re leaving speed and savings on the table.

Teams that get this right don’t just throw more GPUs at the problem. They optimize at the software level: using INT8 quantization to shrink memory use, swapping out slow attention layers with efficient alternatives like SwiftKV, or pruning unnecessary tokens before they even hit the model. Some cut costs by 80% just by compressing prompts without losing accuracy. Others restructure workflows so the model only runs when it’s truly needed—like delaying responses until a user pauses typing. These aren’t theoretical tricks. They’re what companies like Unilever and Microsoft use daily to keep AI running at scale without breaking the bank.

What you’ll find below isn’t a list of buzzwords. It’s a real collection of guides, benchmarks, and fixes—every post here tackles a piece of the inference performance puzzle. Whether you’re trying to reduce latency for customer chatbots, cut cloud bills for internal tools, or make a small model feel as fast as a big one, you’ll find practical steps that work today—not tomorrow’s hype.