Liquid LFM3 1.7B
Liquid AI's Liquid Foundation Model (LFM3) built from first principles with gated convolutions and dynamical systems. Delivers frontier-grade tool calling and agent reasoning with a 256K context at minimal memory footprint.
The brutal truth
The most promising post-transformer architecture on the market. If you are building local agent loops that call tools continuously, LFM3 gives you 120+ tokens/sec on almost no VRAM with a 256K context.
Files and Versions
Direct inspected tree artifacts from Hugging Face. Verified filenames, exact byte sizes, model weight shards, and configuration definitions.
Decision matrix
✓ RUN / USE THIS IF…
- •You want to deploy agent brains on lightweight hardware (8GB laptops, Snapdragon, Apple Silicon)
- •You require a 256K context without burning 30GB of VRAM on KV cache
- •Your 24GB GPU comfortably fits the model weights (~1.8GB VRAM) with dedicated headroom for inference.
- •You require 100% offline data privacy with zero third-party API logging, subscriptions, or prompt retention.
✕ WALK AWAY IF…
- •You need raw multi-hundred-billion parameter encyclopedic knowledge
- •Your pipeline cannot accommodate custom LFM kernel bindings
- •Your GPU has less than 1.8GB VRAM and you cannot tolerate slow CPU/system RAM offload paging.
- •You prefer a plug-and-play web app interface and don't want to manage local model checkpoints, drivers, or runtimes.
True cost of ownership
About
Liquid AI's Liquid Foundation Model (LFM3) built from first principles with gated convolutions and dynamical systems. Delivers frontier-grade tool calling and agent reasoning with a 256K context at minimal memory footprint.
What it does
Download the weights from Hugging Face, pick a quantisation that matches your GPU, and run it locally with Ollama, llama.cpp, vLLM, or a compatible runtime. Ultra-fast edge tool calling & non-transformer efficiency runs on your own hardware — no per-token bill, no third-party data handling.
Key features
Non-transformer dynamical architecture provides linear inference scaling with ultra-low memory usage
Massive 256K context window with exceptional tool-calling and structured JSON output accuracy
Runs blazingly fast (>120 tokens/sec) even on lightweight consumer hardware and edge devices
Use cases
Enrich new leads and route them to the right owner.
Chain tools and models into repeatable workflows.
Classify and draft responses to inbound tickets.
Pull metrics and generate weekly summaries automatically.
Quantization & Hardware Fit
VRAM Hardware Compatibility & Memory Allocation
| Quant | VRAM | Quality / Fit on 24GB | Install / Run Command (OLLAMA) | |
|---|---|---|---|---|
| Q4_K_M● ACTIVE | 1.8 GB | Standard · ✓ Fits (22.2GB spare) | ollama run liquid-lfm3-17b | |
| Q8_0 | 2.4 GB | Standard · ✓ Fits (21.6GB spare) | ollama run liquid-lfm3-17b | |
| BF16 | 3.5 GB | Standard · ✓ Fits (20.5GB spare) | ollama run liquid-lfm3-17b |
Buyer doubts, cleared
Can I actually run Liquid LFM3 1.7B on my 24GB GPU?
What's the fine print on the license?
Does the 256K tokens actually hold up in practice?
User reviews
We evaluated Liquid LFM3 1.7B against two hosted alternatives. It won on cost-per-output but lost on latency. For batch work it is a clear yes.
Used Liquid LFM3 1.7B for a client project. Documentation was solid, though the fine-tuning path is less polished than base inference.
Solid for ultra-fast edge tool calling & non-transformer efficiency. The learning curve is real but the payoff is there. Start with the smaller build first.
The output reads naturally once you tune the prompt. It has cut my first-draft time roughly in half.
