// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Throughput
Throughput is the number of tasks or requests a system can handle or process within a specific period, like how many predictions an AI model can make per second.
TECHNICAL DEFINITION
Throughput, within AI engineering, represents the volume of data or requests processed by an AI system (e.g., inference service, data pipeline) per unit of time, serving as a key metric for system capacity and efficiency under load.
BACKGROUND
Gemini is a generative artificial intelligence chatbot and virtual assistant developed by Google. It is powered by the family of large language models (LLMs) of the same name, after previously being based on LaMDA and PaLM 2.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Processing rate
- capacity
- transactions per second (TPS)
- requests per second (RPS)
- data rate
USAGE NOTE
High throughput is essential for batch processing and serving many users concurrently.
DEVELOPERS
Organizations developing technology related to Throughput.
Develops open-source tools and platforms, including Text Generation Inference (TGI), to optimize the serving and throughput of large language models for various AI applications.
Creator of Ray, an open-source unified framework for scaling AI and Python applications, which is critical for achieving high throughput in distributed AI training and inference workloads.
Specializes in providing a cloud platform for fast, efficient, and scalable inference for open-source large language models, directly addressing throughput challenges in AI deployment.
Develops and deploys advanced AI models and APIs, focusing on optimizing their inference infrastructure to handle high volumes of requests and achieve significant throughput for diverse AI applications.
Offers a comprehensive MLOps platform, Vertex AI, which includes managed services for deploying and serving AI models with features designed to optimize throughput, scalability, and cost efficiency.
Provides a fully managed machine learning service that helps developers build, train, and deploy ML models. SageMaker includes tools for optimizing model inference and achieving high throughput in production environments.
Offers an enterprise-grade service for the end-to-end machine learning lifecycle, enabling deployment of AI models at scale with robust capabilities for managing and optimizing inference throughput.
Provides an API for running machine learning models, focusing on delivering fast and scalable inference, which directly translates to high throughput for developers integrating AI into their applications.