AIPerf: A Comprehensive Evaluation Tool for Generative AI Inference Performance

AIPerf is an open-source generative AI model performance benchmarking tool by NVIDIA. It supports multi-process architecture, various endpoint protocols, and rich evaluation modes, enabling accurate assessment of large model inference performance. It provides detailed performance metric analysis to help developers optimize model deployment strategies.

With the rapid development of generative AI technology, LLM deployment optimization has become a core challenge. However, traditional performance testing tools cannot fully cover the unique metrics of generative AI (such as first-token latency, streaming output throughput, concurrent processing capability, etc.). NVIDIA launched AIPerf to address this issue, providing comprehensive performance evaluation capabilities specifically designed for generative AI.

• Multi-process architecture: 9 independent services communicate via ZeroMQ, enabling high-concurrency testing and loose coupling;
• Three UI modes: Dashboard (real-time TUI monitoring), Simple (progress bar), None (headless mode, suitable for automation);
• Multiple evaluation modes: concurrency, request rate, trace replay, etc.;
• Endpoint support: OpenAI-compatible, NVIDIA NIM, Hugging Face TGI;
• Datasets: Built-in public datasets like ShareGPT, with support for custom data.

Quick Start:

1. Start the Ollama service and pull the model;
2. Install AIPerf and run the benchmark test (example command includes parameters like model, streaming, endpoint type, etc.). Key Metrics: TTFT (First Token Latency), Request Latency (Full Request Latency), Output Token Throughput, etc., covering core dimensions of inference performance.

• Traffic simulation: Supports real traffic patterns like constant rate, Poisson/Gamma distribution, etc.;
• Warm-up phase: Eliminates cold start effects;
• User-centric timing: Evaluates KV cache performance in long conversation scenarios;
• Multi-URL load balancing: Tests distributed inference clusters;
• Request cancellation and timeout: Evaluates system robustness.

• Model selection: Fairly compare different models under the same conditions;
• Deployment optimization: Identify bottlenecks through metrics (e.g., high TTFT requires pre-filling optimization);
• Capacity planning: Determine system capacity limits via stress testing;
• Regression testing: Ensure version updates do not introduce performance degradation.

AIPerf is a professional tool for generative AI performance evaluation, suitable for R&D and production scenarios. In the future, it will continue to iterate, adding support for new models, protocols, and evaluation dimensions to provide reliable support for LLM deployment optimization teams.