// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Latency
Latency is the delay between when you send a request and when you get a response back. It measures how long something takes to happen.
TECHNICAL DEFINITION
Latency, in MLOps and prompt design, quantifies the time delay from an input request (e.g., prompt submission) to the system's output response (e.g., generated text), critically impacting user experience and real-time application performance.
BACKGROUND
Generative artificial intelligence (GenAI) is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, software code or other forms of data. These models learn the underlying patterns and structures of their training data, and use them to generate new data in response to input, which often takes the form of natural language prompts.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Delay
- response time
- lag
- processing delay
- round-trip time
USAGE NOTE
Minimizing latency is crucial for interactive AI applications like chatbots and real-time recommendations.
DEVELOPERS
Organizations developing technology related to Latency.
Develops GPUs and software platforms like TensorRT and Triton Inference Server, which are critical for accelerating AI model inference and significantly reducing latency in AI applications and prompt processing.
Provides an ecosystem for machine learning, including inference APIs, optimization libraries (Optimum), and model deployment solutions designed to improve model efficiency and reduce latency for various AI tasks.
Offers cloud services like Amazon SageMaker for building, training, and deploying ML models, with specialized inference instances (e.g., Inf1, Trn1) and deployment options focused on low-latency AI inference.
Provides Vertex AI, a comprehensive ML platform that includes tools for deploying and serving AI models, with a strong emphasis on optimizing performance, scalability, and reducing inference latency for prompt-based and other AI applications.
Offers services and tools within Azure Machine Learning for high-performance model deployment and scalable inference, enabling developers to manage and optimize AI model latency in production environments.
Focuses on providing fast, efficient, and cost-effective inference for open-source large language models and foundation models, directly addressing the challenge of high latency in AI applications.
Through its Lakehouse Platform and acquisition of MosaicML, Databricks offers tools for optimizing the entire MLOps lifecycle, including efficient training and inference, to reduce latency for AI models at scale.
Provides a platform for prompt engineering and LLM deployment, including features for monitoring and evaluating prompt performance, which often involves optimizing response times and reducing latency of AI interactions.
As a developer of leading large language models like GPT series, OpenAI continuously works on optimizing the inference latency of its models and APIs to ensure faster response times and improved user experience.