LFM3 de Liquid AI ofrece seis modelos multimodales de clase 2B y peso abierto que se ejecutan en una sola GPU, lo que permite a los desarrolladores desplegar IA avanzada en teléfonos, portátiles y servidores privados.
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Liquid AI has released LFM3, its new generation of Liquid Foundation Models. Liquid AI LFM3 is a family of 6 open-weight, 2B-class models built on one shared backbone. The family covers text, vision, audio, omni and decision tasks. Every model can be fine-tuned on a single GPU. Developers can then deploy on phones, laptops, private servers or the cloud. TL;DR Size: LFM3-1.7B has 1.73B parameters and a 256K context window. LFM3-VL-2B has 1.92B and 128K. LFM3-Omni-2B has 2.2B. Runs on: about 1 GB of memory at 4-bit, so phones and laptops. GGUF, MLX, ONNX, FP8 and NVFP4 checkpoints ship. Performance: LFM3-1.7B averages 49.37 across 29 benchmarks, above Qwen3.5-9B at 47.24 (Liquid’s own eval). Best: IFEval 96.66, and AutomationBench 41.29 vs 15.10 for Qwen3.5-9B. Worst: Humanity’s Last Exam (no tools) 7.17, and RULER 128K 65.56 vs 87.11 for Qwen3.5-9B. What is Liquid AI LFM3? LFM3 is a multimodal model family from Liquid AI that branches 6 models from 1 base. All of them start from LFM3-1.7B-Base. The release includes: LFM3-1.7B: a text model for local agents, post-trained for harnesses like Hermes Agent, Pi and OpenClaw. LFM3-VL-2B: the first LFM vision model with video input, sampled at 2 fps. LFM3-Audio-2B: speech in and out across 18 languages, with tool calling. LFM3-Omni-2B: vision, audio and language in one model, answering in text or speech. d1-2B and d1-Omni-2B: decision models that answer many questions in 1 forward pass. How does the tri-hybrid architecture work? The backbone mixes 3 operators across 24 layers. There are 13 gated short convolutions, 5 sliding-window attention layers and 6 global attention layers. The SWA window is 256 tokens. Convolution kernels grew from 3 to 7. Liquid AI team found this mix with its hardware-in-the-loop architecture search. LFM2 was tuned on 1 phone and 1 laptop. LFM3 was tuned across 14 device and backend combinations. These include a Raspberry Pi 5 and the Qualcomm Hexagon NPU. Other changes include hea
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LEANN reduce drásticamente el almacenamiento RAG al recomputar los embeddings al instante, lo que permite búsquedas semánticas a gran escala en laptops estándar.
LFM3 permite a los desarrolladores crear modelos multimodales especializados, ampliando las capacidades de IA más allá del procesamiento de un solo tipo de datos.