Liquid AI is an efficiency-first foundation model company. We build highly capable, compute-optimized models that bring intelligence to any device and medium of choice.
Liquid AI Launches LEAP and Liquid Apollo: The Easiest Way to Build with On-Device AI | Liquid AI
Today marks a pivotal milestone in the evolution of edge AI. Liquid AI is thrilled to announce LEAP v0, our first developer-ready platform for on-device AI deployment—and Liquid Apollo, a lightweight iOS-native application built to showcase and stress-test small foundation models directly on your phone.
Introducing LFM2: The Fastest On-Device Foundation Models on the Market | Liquid AI
Today, we release LFM2, a new class of Liquid Foundation Models (LFMs) that sets a new standard in quality, speed, and memory efficiency for on-device deployment. Built on a hybrid architecture, LFM2 delivers 200% faster decode and prefill performance than Qwen3 and Gemma 3 on CPU. It also significantly outperforms models in each size class on instruction-following and function calling—the core capabilities that make LLMs reliable for building AI agents.
Liquid <> .txt Collaboration | Liquid AI
Faster and more accurate function calling on the edge with .txt’s structured outputs and Liquid Foundation Models
Automotive — Solutions — Liquid AI
On-device AI for automakers — real-time, personalized in-car assistants that run on the vehicle's existing CPUs and NPUs.
MacPaw Partners with Liquid AI to Bring On-Device AI to Millions of Mac Users — Blog
MacPaw partners with Liquid AI to bring private, fast, on-device AI to millions of Mac users, starting with the Eney assistant for macOS.
Liquid AI on Twitter / X
Today, we release LFM2.5-350M. Agentic loops at 350M parameters.A 350M model trained for reliable data extraction and tool use, where models at this scale typically struggle.<500MB when quantized, built for environments where compute, memory, and latency are constrained.🧵 pic.twitter.com/zZPKzcCwH9— Liquid AI (@liquidai) March 31, 2026
The Future of AI Should Serve People, Not Platforms - The Liquid Frontier
Google for Developers Blog - News about Web, Mobile, AI and Cloud
LiteRT is the universal framework for on-device AI. The production stack delivers 1.4x faster cross-platform GPU performance, streamlined NPU acceleration, and superior GenAI support for open models like Gemma.
google-ai-edge/LiteRT
LiteRT, successor to TensorFlow Lite. is Google's On-device framework for high-performance ML & GenAI deployment on edge platforms, via efficient conversion, runtime, and optimization
Automated Architecture Synthesis via Targeted Evolution | Liquid AI
Today, we report advances in automated neural network architecture design and customization. We developed algorithms for the synthesis of tailored architectures (STAR), based on evolutionary algorithms applied to a numerical representation for model architectures derived from a new design theory. STAR automates the process of architecture discovery and optimization, turning it into an end-to-end process. With these methods, we have been able to tailor architectures to custom tasks, metrics, and hardware. We used STAR to synthesize hundreds of different designs that outperform strong Transformer and hybrid architectures in quality, with smaller caches and number of parameters.
Models on-device | Ai2
Ai2, a non-profit research institute founded by Paul Allen, is committed to breakthrough AI to solve the world’s biggest problems.
Together AI | The AI Native Cloud
Build what's next on the AI Native Cloud. Full-stack AI platform for inference, fine-tuning, and GPU clusters — powered by cutting-edge research.
Mirai Labs: Frontier On-Device AI Lab
Models, runtime & infrastructure to make on-device AI interactive, ambient & continuous.
Meet Foundry: An AI Startup that Builds, Evaluates, and Improves AI Agents
containers/ramalama
RamaLama is an open-source developer tool that simplifies the local serving of AI models from any source and facilitates their use for inference in production, all through the familiar language of containers.