Your LLM inference benchmark is lying to you

Estimated reading time: 7 minutes

Key takeaways:

• Benchmarks measure clean, steady conditions, but production LLM traffic is bursty and variable, which is exactly what exposes the latency and memory issues benchmarks hide.
• The right inference framework depends on your tradeoffs: throughput vs. latency needs for your LLM workload, your team’s operational capacity, and future model/hardware compatibility.
• Test with your own LLM traffic, not a leaderboard. Replay real workloads, run soak tests, and simulate failures before committing.

Most large language model (LLM) inference framework comparisons begin with a leaderboard. One framework posts the highest tokens per second on a standard benchmark, and that number quietly becomes the reason a team adopts it.

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