Measuring Generative AI Model Performance with NVIDIA GenAI-Perf and OpenAI API

Measuring Generative AI Model Performance: A Guide to NVIDIA GenAI-Perf

Summary: NVIDIA GenAI-Perf is a powerful tool designed to measure and optimize the performance of generative AI models, particularly large language models (LLMs). This article explores the key features and benefits of GenAI-Perf, including its ability to accurately measure critical performance metrics such as time to first token, output token throughput, and inter-token latency. We will delve into how GenAI-Perf can help machine learning engineers find the optimal balance between latency and throughput, making it an essential tool for applications where quick and consistent performance is paramount.

Understanding Generative AI Model Performance

Generative AI models, especially large language models (LLMs), require precise performance measurement and optimization. Traditional metrics like latency and throughput are not enough; specific metrics such as time to first token, output token throughput, and inter-token latency are crucial for applications where quick and consistent performance is vital.

Key Performance Metrics

• Time to First Token: The time between sending a request and receiving the first response.
• Output Token Throughput: The number of output tokens generated per second.
• Inter-Token Latency: The time between intermediate responses divided by the number of generated tokens.

These metrics are essential for ensuring that applications can deliver quick and consistent responses, with time to first token often being the highest priority.

Introducing NVIDIA GenAI-Perf

NVIDIA GenAI-Perf is a comprehensive tool designed to accurately measure these specific metrics, helping users determine optimal configurations for peak performance and cost-effectiveness. It supports industry-standard datasets like OpenOrca and CNN_dailymail and facilitates standardized performance evaluations across various inference engines through an OpenAI-compatible API.

Key Features of GenAI-Perf

• Industry-Standard Datasets: Supports datasets like OpenOrca and CNN_dailymail for standardized performance evaluations.
• OpenAI-Compatible API: Facilitates comparisons among different serving solutions that support the OpenAI-compatible API.
• Supported Endpoints: Currently supports three OpenAI endpoint APIs: Chat, Chat Completions, and Embeddings.
• Open Source: Accepts community contributions for continuous improvement and adaptation to new model types and requirements.

Using GenAI-Perf

To get started with GenAI-Perf, users can install the latest Triton Inference Server SDK container from NVIDIA GPU Cloud. Running the container and server involves specific commands tailored to the type of model being used, such as GPT2 for chat and chat-completion endpoints, and intfloat/e5-mistral-7b-instruct for embeddings.

Profiling and Results

For profiling OpenAI chat-compatible models, users can run specific commands to measure performance metrics such as request latency, output sequence length, and input sequence length. Sample results for GPT2 show metrics like:

Similarly, for profiling OpenAI embeddings-compatible models, users can generate a JSONL file with sample texts and run GenAI-Perf to obtain metrics such as request latency and request throughput.

Additional Tips for Optimizing Performance

• Lower Max Tokens: Reducing the max token parameter can significantly reduce latency.
• Lower Total Tokens Generated: Generating fewer tokens improves overall response time.
• Streaming: Enabling streaming can manage user expectations by showing model responses as they are generated.
• Content Filtering: Evaluating content filtering policies can help balance safety and latency.

By combining these strategies with the insights provided by NVIDIA GenAI-Perf, developers can achieve optimal performance and efficiency in their generative AI applications.

Conclusion

NVIDIA GenAI-Perf provides a comprehensive solution for benchmarking generative AI models, offering insights into critical performance metrics and facilitating optimization. As an open-source tool, it allows for continuous improvement and adaptation to new model types and requirements. By leveraging GenAI-Perf, machine learning engineers can ensure that their applications deliver quick and consistent performance, making it an indispensable tool in the field of generative AI.