Liquid AI just released d1-3B, an open-source multimodal decision model built on its LFM2.5 architecture. Instead of generating conversational text, it evaluates states and questions in a single forward pass with zero output tokens, clocking in at 8 ms on an RTX 4090 and 50 ms on a Jetson Orin Nano. It provides lightning-fast triage and agent guardrails directly on edge hardware.
Liquid AI just rolled out two 350M-parameter retrieval models, LFM2.5-ColBERT and LFM2.5-Embedding, bringing fast multilingual search across 11 languages to edge devices and CPUs. The twin models tackle both single-vector speed and token-level accuracy, outperforming rivals on NanoBEIR and MKQA benchmarks. Why it matters: developers can now run high-accuracy cross-lingual search locally via GGUF without blowing up compute budgets.
Liquid AI just rolled out LFM2-2.6B-Exp, an experimental checkpoint built entirely with pure reinforcement learning. Despite its 2.6B size, it outperforms competing 3B models across instruction, knowledge, and math benchmarks, even topping DeepSeek R1-0528 on IFBench despite being 263 times smaller. The weights are already live on Hugging Face.
Liquid AI just released LFM2-VL, a lightweight open-weight vision-language model family sized at 450M and 1.6B parameters. Built for resource-constrained setups like phones and wearables, it delivers up to 2x faster GPU inference than existing alternatives. The models are available on Hugging Face and free for commercial use for companies under $10M revenue.
MIT spinoff Liquid AI just released its first Liquid Foundation Models (LFMs), a trio of models completely ditching the standard transformer setup. Built on liquid neural networks, the models range from 1.3B to 40.3B parameters and can handle 1 million tokens with minimal memory overhead. It is a major proof that non-transformer architectures can take on today's frontier LLMs.
