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?
| 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: LANBench
Stop guessing. Start benchmarking. Run LANBench today. Have you used LANBench to optimize your AI server? Share your performance results and tuning tips in the comments below. is the reality check your infrastructure needs
Enter . While the AI world obsesses over public leaderboards like Chatbot Arena or MMLU, LANBench represents a paradigm shift toward localized, network-based, and hardware-accurate benchmarking. This article dives deep into what LANBench is, why it matters for on-premise AI, and how you can use it to optimize your infrastructure. What is LANBench? (Beyond the Hype) At its core, LANBench is a benchmarking framework designed to test Large Language Models (LLMs) and AI inference servers over a Local Area Network (LAN). Unlike traditional benchmarks that run on the same machine as the model (which can mask network latency and serialization overhead), LANBench simulates real-world client-server architectures. | Concurrency
git clone https://github.com/example/lanbench (Note: Replace with actual project URL) cd lanbench make build Create a benchmark.yaml file:
./lanbench run --config benchmark.yaml --output results.json LANBench will output critical metrics that hardware-only benchmarks ignore: