LFM2-8B-A1B: An Efficient On-device Mixture-of-Experts | Liquid AI
We are releasing LFM2-8B-A1B, our first on-device Mixture-of-Experts (MoE) with 8.3B total parameters and 1.5B active parameters per token. By activating only a sparse subset of…
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LFM2-Audio: An End-to-End Audio Foundation Model | Liquid AI
LFM2-Audio defines a new class of audio foundation models: lightweight, multimodal, and real-time. By unifying audio understanding and generation in one compact system, it enables conversational…
Introducing Liquid Nanos — frontier‑grade performance on everyday devices | Liquid AI
We’re launching Liquid Nanos — a family of 350M–2.6B parameter foundation models that deliver frontier‑model quality on specialized, agentic tasks while running directly on phones, laptops,…
Introducing LFM2-2.6B: Redefining Efficiency in Language Models | Liquid AI
We're excited to announce LFM2-2.6B, the newest and currently largest model in our Liquid Foundation Model 2 series. Building on our 350M, 700M, and 1.2B models,…
From Cloud to Keyboard: LEAP Expands to Laptops with Cutting-Edge AMD Ryzen™ Power | Liquid AI
Liquid AI, the MIT-born leader in efficient foundation models, today announced native support for AMD’s latest Ryzen™ and Ryzen AI™ processors within the Liquid Edge AI…
LFM2-VL: Efficient Vision-Language Models | Liquid AI
Today, we release LFM2-VL, our first series of vision-language foundation models. These multimodal models are designed for low-latency and device-aware deployment. LFM2-VL extends the LFM2 family…