Kini Awoṣe Ipilẹ Liquid kan? Liquid AI LFM Itọsọna

Liquid AI is an American AI company spun out of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. TechCrunch names robotics researcher Daniela Rus as a co-founder, and the company grew out of liquid neural network research by Rus and her colleagues. The company releases and sells Liquid Foundation Models, or LFMs: general-purpose generative models built to be small and fast enough to run on edge devices, meaning hardware at the point of use such as smartphones, laptops, cars and robots, rather than on large server clusters. A foundation model is a model trained on broad data that can then be adapted to many tasks.

The company's name comes from its research roots. In a 2020 paper accepted to AAAI-21, Hasani, Lechner, Amini, Rus and Radu Grosu introduced liquid time-constant networks. These are recurrent neural networks, models that process a sequence one step at a time while carrying a hidden state, whose neurons follow simple differential equations. The twist is that each neuron's time constant, which controls how quickly it reacts to new input, is itself changed by a learned gate that depends on the input. The authors showed the networks stay stable and bounded and reported better time-series prediction than classical and modern recurrent networks. TechCrunch described the idea as inspired by the brain of a roundworm and noted that such networks can be much smaller than conventional models.

The commercial LFMs are not literal copies of those 2020 networks. They borrow the broader idea of input-dependent operators. Liquid AI's July 2025 LFM2 announcement frames its design space around what it calls linear input-varying (LIV) operators: linear operations whose weights are generated on the fly from the input they act on. Under that view, convolutions, recurrences and attention are all special cases, so an automated search can mix them. The company built a neural architecture search engine called STAR to explore that space, and for LFM2 it scored candidates by measured peak memory and prefill and decode speed on Qualcomm Snapdragon phone CPUs instead of relying on theoretical proxies such as cache size.

The search produced a hybrid backbone. Each LFM2 model in the first release has 16 blocks: 10 double-gated short convolution blocks and 6 grouped-query attention blocks, with a SwiGLU feed-forward layer and RMSNorm normalization in each block. A short convolution blends each token only with a few neighbors, so its cost grows linearly with text length and it needs no growing memory cache. Grouped-query attention, a cheaper variant of standard attention in which several query heads share one set of keys and values, handles the long-range lookups that convolutions cannot. Keeping attention to a minority of blocks is what shrinks memory use and speeds up generation on CPUs. The blog says the choice of short convolutions follows from the target hardware, embedded system-on-chip CPUs, and from how well existing kernel libraries run them.

The milestones are recent. Liquid AI released its first LFM series on 30 September 2024: LFM-1B (about 1.3 billion parameters), LFM-3B (about 3.1 billion) and LFM-40B, a mixture-of-experts model with 40.3 billion total parameters and roughly 12 billion active per token, all with a 32,000-token context window. That generation is now listed as deprecated. In December 2024 the company raised a $250 million Series A led by AMD, which TechCrunch reported at a valuation above $2 billion citing Bloomberg, with plans to tune models for AMD GPUs, CPUs and accelerators. LFM2 followed on 10 July 2025 with open-weight 350M, 700M and 1.2B checkpoints, and the LFM2 technical report posted to arXiv on 28 November 2025 extended the family to a 2.6B dense model and an 8.3B mixture-of-experts model with 1.5B active parameters. Newer LFM2.5 checkpoints, such as LFM2.5-1.2B-Instruct, keep the same 16-block layout but are pretrained on 28 trillion tokens and add multi-stage reinforcement learning.

Compared with the obvious alternatives, small transformer models such as Qwen3, Llama 3.2 and Gemma 3, the trade is speed and memory against breadth. Liquid AI's own table shows LFM2-1.2B scoring 55.23 on MMLU, a general-knowledge test, against 59.11 for the larger Qwen3-1.7B and 46.6 for Llama-3.2-1B-Instruct, while beating all three on IFEval, an instruction-following test, with 74.89. The company reports LFM2-700M decoding faster than Qwen3-0.6B in both ExecuTorch and llama.cpp despite being 16% larger. Compared with pure state-space models such as Mamba, which replace attention entirely with a recurrence, LFM2 keeps a few attention blocks for precise recall over long inputs.

The limitations matter. Nearly every benchmark and speed figure comes from Liquid AI itself, and its blog says it modified some evaluation procedures relative to the standard EleutherAI evaluation harness, so results are not directly comparable with other leaderboards. Qwen3 was tested with its reasoning mode turned off. Models of one to three billion parameters still hold far less world knowledge than frontier models and can be confidently wrong, so they suit narrow tasks such as extraction, classification and tool calling better than open-ended factual questions. Speed claims also depend on quantization: the published phone and laptop tests used 4-bit weights, which trade some accuracy for size.

In practice the requirements are modest. The models ship as open weights on Hugging Face with deployment packages for ExecuTorch, llama.cpp and vLLM, so a developer can run a 1.2B model on an ordinary laptop or phone without a GPU. The license is based on Apache 2.0 but adds a revenue threshold: academic and research use is free, commercial use is free for companies under $10 million in revenue, and larger companies must contact Liquid AI for a commercial license. Organizations that want custom models or enterprise support deal with the company directly.