LFM Models: Liquid Foundation Models Explained (2026 Guide)

What Is an LFM Model?

LFM (Liquid Foundation Model) is the name Liquid AI gives its series of generative AI foundation models. The family launched on September 30, 2024 with three sizes — LFM-1B, LFM-3B, and LFM-40B — and the founding claim was specific: state-of-the-art quality at every scale, with a smaller memory footprint than comparable transformer models.

That claim held up. LFM-1B set new state-of-the-art scores in the 1B category on benchmarks like MMLU and ARC-C, and LFM-3B delivered performance comparable to 7B–13B transformer models while using a fraction of the memory. The reason is architecture, not magic — LFMs are built on liquid neural network principles that compress state differently from attention, which is the key to their efficiency.

Why the Architecture Matters: LFM vs Transformer

	Transformer (Qwen, Llama, Gemma)	LFM (Liquid)
Core mechanism	Self-attention over the full token history	Hybrid of liquid (state-space) layers + selected attention
Memory vs context length	Grows with context (KV cache)	Roughly constant — state is compressed
Best hardware	GPU-heavy; needs big VRAM for long context	GPUs, CPUs, and NPUs — efficient even on-device
Small-model efficiency	1B–3B models are usable but limited	1B–3B models compete with 4x-larger transformers
Ecosystem	Massive — fine-tunes, tools, integrations everywhere	Growing — GGUF, Ollama, vLLM support now standard

💡 In plain terms: a transformer's memory grows as you feed it more context. An LFM compresses what it knows into a fixed-size state, so long context stays cheap. That's why a sub-12B LFM can run on hardware where a transformer of the same size struggles — and why Liquid AI has aimed the family squarely at on-device AI.

The LFM Family: 2024 → 2026

Generation	Released	Models	Focus
LFM 1.x	Sep 2024	LFM-1B, LFM-3B, LFM-40B	First series — SOTA per size, smaller memory
LFM2	Jul 2025 (report Nov 2025)	Dense 350M–2.6B; 8.3B MoE (1.5B active)	On-device, edge, hybrid architecture
LFM2.5	Jan 2026	350M, 1.2B, 2.6B, 8B-A1B MoE + Thinking + encoders	Tiny on-device family — agents, long context, CPU

Liquid AI's design process is itself distinctive: the LFM2 generation used hardware-in-the-loop architecture search — co-designing the architecture with the hardware it will run on — which is why the models are unusually friendly to CPU and NPU inference, not just GPUs.

The 2026 LFM2.5 Lineup, Up Close

LFM2.5-2.6B — Deploy Agents Everywhere

The flagship of the 2.5 generation: "deploy agents everywhere," with fast long-context inference even on CPU. The natural pick for agent loops on laptops and edge boxes, backed by GGUF builds and Ollama support.

LFM2.5-1.2B-Thinking — Reasoning Under 1GB

Released January 2026: a 1.2B reasoning model that fits in under 1GB on-device. Chain-of-thought at a size that runs on phones — the poster child for Liquid's efficiency thesis.

LFM2.5-350M — No Size Left Behind

The tiny one: agentic loops at 350M parameters. Trained on 28T tokens with a data ratio far above typical small models, it does surprisingly capable work for its size — routing, extraction, and simple agent tasks on Raspberry Pi-class hardware.

LFM2.5-8B-A1B — The MoE Addition

Liquid's efficient MoE: 8B total parameters with ~1B active per token (the "A1B" suffix, same convention as GPT-OSS and GLM Flash). Big-model capability with tiny-model compute per token.

🧮 LFM2.5 Encoders & Embeddings

Beyond chat models, the 2.5 generation includes bidirectional encoders (350M and 230M), an embedding model (350M), and a ColBERT-style model (350M) — meaning LFM covers the whole RAG stack, including the embedding side this blog covers in the embedding-model ranking.

🔍 LFM2.5-VL Extract Models

Vision-language extract models (450M and 1.6B) for document and screenshot information extraction — on-device OCR-style extraction without a cloud call.

📋 LFM2 Specialists

The 2.5 generation builds on LFM2 specialist models — transcript, PII-extract (JP), ENJP machine translation, RAG, and tool-use variants at 350M–2.6B — showing the family is designed for concrete edge jobs, not just chat.

🤝 LFM + AMD on-device

January 2026: Liquid and AMD demonstrated a fine-tuned LFM doing meeting-transcript summarization entirely on a Ryzen laptop — the "private AI on your hardware" story this blog covers in the honest private-LLM guide, shipped in hardware.

How to Run LFM Models

Ollama: LFM2.5 models are available in the library — ollama run lfm2.5:2.6b (or lfm2.5:1.2b for a smaller footprint).
llama.cpp / GGUF: GGUF builds are published for the 2.5 family — download and run with the same workflow as any GGUF model.
vLLM: the 2.5 family has vLLM deployment guides — the production path for serving LFMs at scale.
Hugging Face: all weights live under the LiquidAI namespace, free to download and fine-tune.

🚀 Where LFMs fit your stack

LFMs are the efficiency play: 2.6B for agents on a laptop, 1.2B-Thinking for phone-grade reasoning, 350M for routing and extraction — with LFM embedding/ColBERT models to complete a local RAG pipeline. For maximum model choice and ecosystem, the general local-model ranking and RAM-based download guide cover the transformer side. And for the quantization math that makes any of them fit, see Q4_K_M vs Q8_0.

Frequently Asked Questions (FAQ)

What is an LFM model?

LFM stands for Liquid Foundation Model — a family of generative AI models from Liquid AI designed from first principles rather than the standard transformer architecture. LFMs use a hybrid architecture built on liquid neural network ideas, which delivers state-of-the-art quality at each size while keeping a much smaller memory footprint than comparable transformer models.

Who makes LFM models?

Liquid AI, a US company founded by MIT researchers, makes the LFM family. The first series (LFM-1B, LFM-3B, LFM-40B) launched in September 2024, and the family has since grown with the LFM2 and LFM2.5 generations focused on on-device and edge deployment.

What is the difference between LFM and transformer models?

Transformers process every token against the full attention history, which grows memory use with context length. LFMs use a hybrid architecture with liquid neural network components — the state is compressed into a fixed-size representation, so memory stays roughly constant and inference is faster on CPU, edge devices, and NPUs. That is why a 3B LFM can match transformer models 2–4x its size while running in less memory.

What LFM models can I run in 2026?

The LFM2.5 family is the on-device line: 350M (agentic loops at tiny scale), 1.2B (the Thinking variant fits under 1GB), 2.6B (fast long-context even on CPU), and the LFM2.5-8B-A1B MoE (8B total, 1B active). GGUF builds are available for Ollama and llama.cpp, so they run on laptops, phones, and Raspberry Pi-class devices.

Are LFM models free to use?

Yes. Every LFM is free to download, run, and fine-tune. Liquid AI publishes the weights openly on Hugging Face, and there is a permissive license for commercial use — check each model card for exact terms.

Are LFM models better than Qwen or Llama at the same size?

On several benchmarks, yes — LFM-3B was shown performing comparably to 7B–13B transformer models, and LFM-1B set new state-of-the-art scores in the 1B category at launch. The practical comparison is different though: LFMs win on memory efficiency and CPU/edge speed, while Qwen and Llama have far larger ecosystems and fine-tune libraries. Choose by deployment target, not just benchmark score.