GenAI-Perf by NVIDIA for Optimizing Generative AI Model Performance ✨

Enhancing AI Model Performance Measurement with NVIDIA’s GenAI-Perf

NVIDIA has introduced GenAI-Perf, a new tool focused on improving the measurement and optimization of generative AI models. This tool, integrated into the latest NVIDIA Triton release, aims to assist machine learning engineers in finding the ideal balance between latency and throughput, especially for large language models (LLMs).

Key Metrics for Improved LLM Performance

Performance metrics for LLMs go beyond traditional latency and throughput measures. Critical metrics include:

Time to first token: The duration between sending a request and receiving the initial response.
Output token throughput: The quantity of output tokens generated per second.
Inter-token latency: The time between intermediate responses divided by the number of generated tokens.

These metrics are vital for applications where rapid and consistent performance is essential, with time to the first token often taking precedence.

Introduction to GenAI-Perf

GenAI-Perf is specifically designed to measure these key metrics accurately, assisting users in identifying optimal configurations for peak performance and cost efficiency. This tool supports standard datasets like OpenOrca and CNN_dailymail and enables standardized performance evaluations with different inference engines via an OpenAI-compatible API.

GenAI-Perf aims to become the default benchmarking tool for all NVIDIA generative AI solutions, including NVIDIA NIM, NVIDIA Triton Inference Server, and NVIDIA TensorRT-LLM, facilitating easy comparisons among various serving options supporting the OpenAI-compatible API.

Supported Endpoints and Usage

Currently, GenAI-Perf supports three OpenAI endpoint APIs: Chat, Chat Completions, and Embeddings. As new model types emerge, additional endpoints will be added. GenAI-Perf is also open source, welcoming contributions from the community.

To begin using 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 model type, such as GPT2 for chat and chat-completion endpoints, and intfloat/e5-mistral-7b-instruct for embeddings.

Profiling and Performance Results

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

Request latency (ms): Average of 1679.30, with a minimum of 567.31 and a maximum of 2929.26.
Output sequence length: Average of 453.43, ranging from 162 to 784.
Output token throughput (per sec): 269.99.

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

Concluding Thoughts

GenAI-Perf presents a comprehensive solution for benchmarking generative AI models, providing insights into essential performance metrics and enabling optimization. Being an open-source tool, it allows for continuous enhancements and adaptation to new model types and operational needs.

Hot Take: Elevating AI Performance Measurement with GenAI-Perf

Dear crypto reader, NVIDIA’s GenAI-Perf stands out as a valuable tool in the realm of AI model performance measurement. By focusing on critical metrics and offering benchmarking capabilities, this tool empowers you to enhance the efficiency and effectiveness of your generative AI models. Embrace GenAI-Perf to unlock new levels of optimization in your AI endeavors!