Liquid AI develops non-transformer generative AI models, known as Liquid Foundation Models (LFMs), which require less memory while maintaining high performance across various applications. These models address the inefficiencies and high computational costs associated with traditional large language models, enabling scalable AI solutions for diverse industries.
Funding
$250M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
AVFounders
Product
Problem
Traditional large language models (LLMs) demand significant computational resources and memory, hindering their deployment in edge computing environments and increasing operational costs. This limits the accessibility and scalability of AI solutions across various industries.
Solution
Liquid AI develops Liquid Foundation Models (LFMs), a series of non-transformer generative AI models designed to deliver high performance with a significantly smaller memory footprint compared to traditional LLMs. These models enable the development and deployment of efficient AI solutions at various scales, making AI more accessible and cost-effective. By reducing the computational burden, Liquid AI facilitates the integration of advanced AI capabilities into resource-constrained environments.
Target Audience
The primary target audience includes enterprises and developers seeking efficient and scalable AI solutions, particularly those working with edge computing and resource-constrained environments.
Features
- Non-transformer architecture for reduced memory requirements
- High performance across a range of generative AI tasks
- Scalable design suitable for both cloud and edge deployment
- Liquid Engine platform for developing and deploying LFMs