{"id":491,"date":"2026-09-08T11:40:29","date_gmt":"2026-09-08T11:40:29","guid":{"rendered":"https:\/\/www.lynxseo.com\/blog\/?p=491"},"modified":"2026-09-05T11:41:04","modified_gmt":"2026-09-05T11:41:04","slug":"optimizing-llm-performance","status":"publish","type":"post","link":"https:\/\/www.lynxseo.com\/blog\/optimizing-llm-performance\/","title":{"rendered":"Tips for Optimizing LLM Performance: Cut Latency, Cost, and Wasted GPU"},"content":{"rendered":"

Quick Answer:<\/i><\/b> LLM performance optimization comes down to three numbers: time to first token, tokens per second, and how many concurrent requests one GPU can serve. The highest-return fixes in production are prompt caching, continuous batching, KV cache optimization, and quantization. Measure before you change anything, because most slow deployments are bottlenecked somewhere other than where the team assumes.<\/span><\/i><\/p>\n

Almost every conversation about improving LLM performance starts in the wrong place. Someone benchmarks a model, decides it feels slow, and starts pricing bigger GPUs. Two weeks later the bill has doubled and the p99 latency has barely moved.<\/span><\/p>\n

The reason is that “slow” is not one problem. A 4-second wait before the first token appears is a prefill problem. A response that trickles out at 8 tokens per second is a decode problem. A system that runs fine for one user and falls apart at fifty is a scheduling and memory problem. These have different causes and completely different fixes, and applying the wrong one is how teams end up paying for idle GPU capacity.<\/span><\/p>\n

We run LLM pipelines inside our own production systems at LYNX, mostly high-volume batch classification and content workflows, plus the AI visibility monitoring we operate for clients. The single biggest cost reduction we have made had nothing to do with hardware. It came from restructuring prompts so the static prefix stayed identical across calls and could be cached. That pattern repeats: the cheapest wins in LLM inference optimization are usually structural, not computational.<\/span><\/p>\n

This guide covers the techniques that actually move the numbers, in the order we would apply them.<\/span><\/p>\n

Key Takeaways<\/b><\/h2>\n