Lanbench May 2026

./lanbench run --config benchmark.yaml --output results.json LANBench will output critical metrics that hardware-only benchmarks ignore:

In the rapidly evolving landscape of artificial intelligence, the race to build the fastest, most efficient large language model (LLM) is relentless. However, for developers, data scientists, and on-premise AI engineers, a crucial question remains: How do we measure real-world performance on our own hardware? LANBench

| Tool | Focus | Network Aware? | Concurrency? | Best For | | :--- | :--- | :--- | :--- | :--- | | | Accuracy (MMLU, HellaSwag) | No | No | Model capability | | llama-bench | CPU/GPU compute speed | No | No | Hardware optimization | | Artillery / k6 | General HTTP load | Yes | Yes | Not AI-native (no token streaming metrics) | | LANBench | LLM-specific LAN perf | Yes | Yes | Production AI servers | Common Pitfalls and How to Fix Them When you first run LANBench, you will likely see disappointing numbers. Here is how to fix them: | Concurrency

git clone https://github.com/example/lanbench (Note: Replace with actual project URL) cd lanbench make build Create a benchmark.yaml file: It answers the only question that matters in

is the reality check your infrastructure needs. It answers the only question that matters in production: How fast is my LLM when it actually matters, across my real network, under real load?