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Build efficient general-purpose AI at every scale.

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Today we introduce Liquid Labs, our advanced research unit, with the goal of understanding and building efficient and adaptive intelligence systems. Liquid Labs consolidates our existing research efforts at Liquid across architecture of foundation models, multimodality, training, data, and inference. The lab also will be home to new frontier research work across the broad range of foundation model build-up stack. Read the full announcement: We are hiring: Also find us at NeurIPS 2025 exhibition hall! 🚀

Today we introduce Liquid Labs, our advanced research unit, with the goal of understanding and building efficient and adaptive intelligence systems. Liquid Labs consolidates our existing research efforts at Liquid across architecture of foundation models, multimodality, training, data, and inference. The lab also will be home to new frontier research work across the broad range of foundation model build-up stack. Read the full announcement: We are hiring: Also find us at NeurIPS 2025 exhibition hall! 🚀

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Training LFMs at scale means solving parallelism across every layer of the architecture. And not all layers are the same. Our CTO Mathias Lechner (Mathias Lechner) sits down with Liquid's founding engineer Paul Pak (Paul Pak) to talk training infrastructure: Data, tensor, pipeline, expert, and context parallelism, and how they make context parallelism work across hybrid architectures with both attention and convolution operators.

Training LFMs at scale means solving parallelism across every layer of the architecture. And not all layers are the same. Our CTO Mathias Lechner (Mathias Lechner) sits down with Liquid's founding engineer Paul Pak (Paul Pak) to talk training infrastructure: Data, tensor, pipeline, expert, and context parallelism, and how they make context parallelism work across hybrid architectures with both attention and convolution operators.

17,125 просмотров • 8 дней назад

Meet Liquid ShieldFlow. An on-device privacy layer powered by a device-native Liquid Foundation Model, Liquid ShieldFlow redacts sensitive data before it ever leaves your machine. No GPU needed and light on memory. It runs on almost any PC, locally, in real time. ShieldFlow was featured yesterday at Microsoft Build for Foundry Local. It also ran live on AMD laptops at 𝐂𝐎𝐌𝐏𝐔𝐓𝐄𝐗 𝐓𝐀𝐈𝐏𝐄𝐈. Request your early access here to ShieldFlow here:

Meet Liquid ShieldFlow. An on-device privacy layer powered by a device-native Liquid Foundation Model, Liquid ShieldFlow redacts sensitive data before it ever leaves your machine. No GPU needed and light on memory. It runs on almost any PC, locally, in real time. ShieldFlow was featured yesterday at Microsoft Build for Foundry Local. It also ran live on AMD laptops at 𝐂𝐎𝐌𝐏𝐔𝐓𝐄𝐗 𝐓𝐀𝐈𝐏𝐄𝐈. Request your early access here to ShieldFlow here:

18,811 просмотров • 11 дней назад

The bottleneck in LLM inference isn't compute. It's how fast you can move the weights. Our CTO Mathias Lechner, Mathias Lechner, joins Piotr Mazurek, Piotr Mazurek (in SF 🌉), from our inference team, to discuss what actually limits token throughput and how we're optimizing for it.

The bottleneck in LLM inference isn't compute. It's how fast you can move the weights. Our CTO Mathias Lechner, Mathias Lechner, joins Piotr Mazurek, Piotr Mazurek (in SF 🌉), from our inference team, to discuss what actually limits token throughput and how we're optimizing for it.

21,393 просмотров • 19 дней назад

Building a model is just the start. Post-training makes it useful. Our CTO Mathias Lechner (Mathias Lechner) sits down for a conversation with Maxime Labonne (Maxime Labonne), our head of post-training, on the pipeline that takes a base model from autocomplete to something that can reason and follow instructions.

Building a model is just the start. Post-training makes it useful. Our CTO Mathias Lechner (Mathias Lechner) sits down for a conversation with Maxime Labonne (Maxime Labonne), our head of post-training, on the pipeline that takes a base model from autocomplete to something that can reason and follow instructions.

25,877 просмотров • 25 дней назад

a vision language model too fast for human eyes! kudos Xenova 🐐

a vision language model too fast for human eyes! kudos Xenova 🐐

50,097 просмотров • 3 месяцев назад

Well said. AI shouldn't just live in a data center, but it should run on the device you're already using: Whether it's a phone, a car, an appliance, or another everyday device in the real world. That’s what we’re working on at Liquid AI. We appreciate the spotlight on our COO Jeffrey Li (jeffrey li), who joined the AI-Curious Podcast (AI-Curious). Thank you, Jeff Wilser!

Well said. AI shouldn't just live in a data center, but it should run on the device you're already using: Whether it's a phone, a car, an appliance, or another everyday device in the real world. That’s what we’re working on at Liquid AI. We appreciate the spotlight on our COO Jeffrey Li (jeffrey li), who joined the AI-Curious Podcast (AI-Curious). Thank you, Jeff Wilser!

12,995 просмотров • 28 дней назад

Tonight at #CES, Liquid’s CEO Ramin Hasani Ramin announced to the world the LFM 2.5 model library. Watch Ramin’s full announcement alongside Lisa Su, CEO of AMD, and learn how we are making intelligence accessible everywhere. Read the full release: LEAP: HF:

Tonight at #CES, Liquid’s CEO Ramin Hasani Ramin announced to the world the LFM 2.5 model library. Watch Ramin’s full announcement alongside Lisa Su, CEO of AMD, and learn how we are making intelligence accessible everywhere. Read the full release: LEAP: HF:

19,775 просмотров • 5 месяцев назад

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