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How to Run LFM3 Locally: The Non-Transformer Model That Fits in 1.1 GB
LFM3 is 1.7B parameters, fits in 1.1 GB of RAM, and it’s not a Transformer. Here’s how to run it in three commands.
Last week Liquid AI dropped something that quietly broke the internet in the ML community: LFM3, a family of small language models built on a completely different architecture than every model you’ve heard of.
Not GPT. Not Llama. Not Mistral. None of them are Transformers either — oh wait, they are. LFM3 is not.
And it fits on your laptop.
Let me show you how to run it right now, then explain why this matters more than the usual “new model Tuesday” noise.
Why LFM3 is different
Since 2017, every serious language model has been a Transformer. Attention mechanisms, quadratic complexity on long sequences, the whole package. We’ve gotten very good at scaling Transformers, but their fundamental limitations haven’t gone away: they’re memory-hungry, slow on long contexts, and architecturally frozen.
Liquid AI is an MIT spinout, and their bet is that State Space Models (SSMs) — specifically their hybrid “Liquid Foundation Model” architecture — can match Transformers at a fraction of the compute and memory cost.
