Liquid AI's d1-3B brings multimodal models to the edge

Liquid AI has introduced d1-3B, part of a new line of open multimodal "decision" models built on the company's LFM2 architecture and designed to run at the edge. At roughly 3 billion parameters, the model is small enough to deploy on constrained hardware while handling vision-language tasks, according to the company's announcement on Hugging Face.

The pitch here is less about chasing frontier benchmarks and more about placement. By keeping the parameter count modest and targeting on-device inference, d1-3B is positioned for scenarios where sending data to the cloud is impractical — think robotics, cameras, and other latency- or privacy-sensitive applications that need to interpret images and text locally.

Why it matters

The open-weights landscape has been dominated by ever-larger models, but a growing share of real-world demand sits at the opposite end: compact systems that run where the data is generated. A multimodal model in the 1B–7B range that can actually fit on edge hardware fills a practical gap.

• Multimodal: handles both vision and language inputs
• Compact: around 3B parameters, sized for edge deployment
• Open: weights published on Hugging Face under the company's license

Liquid AI frames d1 as a family rather than a one-off, suggesting more variants may follow. For developers building on-device applications, the release offers a lightweight option worth evaluating against existing small multimodal models. Full details are available in Liquid AI's blog post.