NVIDIA 推出 AIPerf 进行大规模 LLM 测试
NVIDIA представляет AIPerf для масштабного тестирования LLM.
- 01.NVIDIA представляет AIPerf для масштабного тестирования LLM.
As NVIDIA Developer reports, when deploying a model to a system, an important question arises: how fast does it run? Many approaches, such as using curl commands or writing your own scripts, can lead to performance limitations due to Python's single-process architecture and GIL. To solve this problem, NVIDIA developed AIPerf, a tool that allows you to efficiently test server performance without bottlenecks. AIPerf is the successor to GenAI-Perf and is a complete overhaul taking into account the experience of testing LLMs at large scale. It does not work on top of Perf Analyzer like GenAI-Perf did. This allows AIPerf to scale more efficiently. The tool supports over 15 types of endpoints, including chat, image generation and more, and also uses public datasets such as ShareGPT. AIPerf allows you to control the shape of the load, supporting various patterns such as constant, Poisson and gamma patterns. This allows the user to customize the load parameters, rather than just manage its volume. When testing using AIPerf, you can easily change models and, of course,
ed points, which simplifies the process. When running AIPerf, users can obtain metrics such as time to first token (TTFT) and inter-token latency (ITL), allowing them to more accurately evaluate model performance. These metrics are displayed in a table on the console and saved in CSV and JSON format for further analysis. AIPerf also provides capabilities for more dynamic traffic testing, allowing variability to be introduced into queries to better match real-world operating conditions. The tool can be used for complex scenarios such as multi-node Kubernetes deployments and replaying traffic from a production environment. AIPerf is a collaboration between NVIDIA and external developers to improve functionality and testing standards. The material was prepared by a neural network based on the original publication. AI may make mistakes in details (numbers, names, dates, quotes) - for important information, check with the original source. PrismML Unveils High-Performance Ternary Bonsai 2 27B Researchers Discuss AI Slowdown to Improve Security FakeGit Campaign Shows How Artificial Agents Become a Conduit for Malware
a lot of software. NVIDIA Topograph optimizes GPU load placement in AI factories. Tata Elxsi has developed the IRIS platform to detect risks in factories in seconds.
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