LLM Deployment Benchmarking

Benchmarking is not something you do at the end of an LLM deployment.

It is what stops you making expensive mistakes at every stage.

Here are the 4 moments where it matters most:

𝟭. 𝗣𝗿𝗲-𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁
Don’t pick the model first and hope the hardware works.
Benchmark first to check whether the model can hit your latency target on the hardware you actually have.

For example: can an NVIDIA H200 serve Llama 3.1 70B within your latency budget, or do you need to drop to 8B?

𝟮. 𝗖𝗼𝘀𝘁 𝗮𝗻𝗱 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴
Once the model is chosen, benchmark throughput per server.
That tells you how many servers you need, what peak traffic you can handle, and what your infrastructure will really cost.

𝟯. 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗮𝗻𝗱 𝗔/𝗕 𝘁𝗲𝘀𝘁𝗶𝗻𝗴
Models change. Quantisation changes. Serving engines change.
Benchmarking is how you catch performance regressions before users do.

A new INT8 setup might handle more traffic than your baseline, or it might quietly make things worse.

𝟰. 𝗛𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
Every system has a breaking point.
Benchmarking shows you where latency starts to climb and where performance drops off.

That gives you honest capacity limits and better autoscaling rules.

If you only benchmark once, you’re already too late.

Benchmark early. Benchmark often.

What’s the first benchmark you run before deploying an LLM?