LLM Latency: What It Is, Why It Matters, and How to Fix It

When you ask a large language model a question and it takes more than a second to reply, that delay is LLM latency, the time it takes for a large language model to generate a response after receiving input. It's not just a technical detail—it's what makes your AI feel sluggish, expensive, or unusable in real applications. Many people think faster models mean bigger models, but that’s backwards. A 70-billion-parameter model can be slower than a 7-billion one if it’s not optimized for speed. The real culprit? KV cache, a memory structure that stores past attention results during generation to avoid recomputing them. As conversations get longer, the KV cache grows, eating up memory and forcing the system to wait for data to move in and out of fast storage.

That’s why FlashAttention, a memory-efficient attention algorithm that reduces redundant computations and speeds up transformer inference has become essential. It cuts latency by 20-50% on the same hardware, without changing the model. But FlashAttention isn’t the only fix. INT8 quantization, reducing model precision from 32-bit to 8-bit numbers to shrink memory use and boost throughput cuts both cost and delay, especially for mobile or edge deployments. And if you’re running customer-facing bots, even a 300-millisecond delay can drop user satisfaction by 20%. Latency isn’t just a backend problem—it’s a UX problem.

What you’ll find below isn’t theory. These are real fixes used by teams shipping AI tools today: how to spot whether your bottleneck is memory or compute, which optimizations work for cloud vs. on-device models, and why some "speed hacks" actually make things worse. You’ll see how companies are trimming latency by 70% without losing accuracy—and why some teams are ditching giant models entirely for smaller, faster ones that do the job better.