How to benchmark LLM inference before you commit to capacity

Reliable LLM inference benchmarking requires more than a single tokens-per-second figure, because performance depends heavily on request shapes, concurrency, latency requirements, endpoint behavior, and traffic variability. Useful tests separately measure time to first token, time per output token, aggregate throughput, and p95/p99 tail latency, using prompt and completion length distributions, streaming behavior, tool calls, structured outputs, and peak concurrency that resemble real production traffic. Benchmarks should sweep increasing concurrency to identify both the point where throughput stops improving and the lower point where latency exceeds product requirements, with the difference indicating operational headroom. Evaluations should also verify feature compatibility, error behavior under overload, long-context performance, and consistency across times of day, particularly for shared infrastructure. A basic harness can record request timing, token counts, statuses, percentiles, and throughput from the same region as users, while repeated tests at planned utilization help assess dedicated capacity. The appropriate capacity-planning metric is throughput achievable within the product’s latency budget, and this is especially important for agents, where tail delays accumulate across many sequential model calls.

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