Need smarter insights in your inbox? Join our weekly newsletters to get solely what issues to enterprise AI, knowledge, and safety leaders. Subscribe Now
Liquid AI has launched LFM2-VL, a brand new era of vision-language basis fashions designed for environment friendly deployment throughout a variety of {hardware} — from smartphones and laptops to wearables and embedded programs.
The fashions promise low-latency efficiency, sturdy accuracy, and suppleness for real-world functions.
LFM2-VL builds on the corporate’s current LFM2 structure, extending it into multimodal processing that helps each textual content and picture inputs at variable resolutions.
In keeping with Liquid AI, the fashions ship as much as twice the GPU inference pace of comparable vision-language fashions, whereas sustaining aggressive efficiency on widespread benchmarks.
AI Scaling Hits Its Limits
Energy caps, rising token prices, and inference delays are reshaping enterprise AI. Be part of our unique salon to find how prime groups are:
- Turning power right into a strategic benefit
- Architecting environment friendly inference for actual throughput features
- Unlocking aggressive ROI with sustainable AI programs
Safe your spot to remain forward: https://bit.ly/4mwGngO
“Effectivity is our product,” wrote Liquid AI co-founder and CEO Ramin Hasani in a put up on X asserting the brand new mannequin household:
Two variants for various wants
The discharge consists of two mannequin sizes:
- LFM2-VL-450M — a hyper-efficient mannequin with lower than half a billion parameters (inner settings) aimed toward extremely resource-constrained environments.
- LFM2-VL-1.6B — a extra succesful mannequin that continues to be light-weight sufficient for single-GPU and device-based deployment.
Each variants course of photos at native resolutions as much as 512×512 pixels, avoiding distortion or pointless upscaling.
For bigger photos, the system applies non-overlapping patching and provides a thumbnail for world context, enabling the mannequin to seize each advantageous element and the broader scene.
Background on Liquid AI
Liquid AI was based by former researchers from MIT’s Laptop Science and Synthetic Intelligence Laboratory (CSAIL) with the aim of constructing AI architectures that transfer past the extensively used transformer mannequin.
The corporate’s flagship innovation, the Liquid Basis Fashions (LFMs), are primarily based on rules from dynamical programs, sign processing, and numerical linear algebra, producing general-purpose AI fashions able to dealing with textual content, video, audio, time collection, and different sequential knowledge.
In contrast to conventional architectures, Liquid’s method goals to ship aggressive or superior efficiency utilizing considerably fewer computational sources, permitting for real-time adaptability throughout inference whereas sustaining low reminiscence necessities. This makes LFMs properly fitted to each large-scale enterprise use instances and resource-limited edge deployments.
In July 2025, the firm expanded its platform technique with the launch of the Liquid Edge AI Platform (LEAP), a cross-platform SDK designed to make it simpler for builders to run small language fashions instantly on cell and embedded units.
LEAP presents OS-agnostic help for iOS and Android, integration with each Liquid’s personal fashions and different open-source SLMs, and a built-in library with fashions as small as 300MB—sufficiently small for contemporary telephones with minimal RAM.
Its companion app, Apollo, permits builders to check fashions completely offline, aligning with Liquid AI’s emphasis on privacy-preserving, low-latency AI. Collectively, LEAP and Apollo mirror the corporate’s dedication to decentralizing AI execution, decreasing reliance on cloud infrastructure, and empowering builders to construct optimized, task-specific fashions for real-world environments.
Velocity/high quality trade-offs and technical design
LFM2-VL makes use of a modular structure combining a language mannequin spine, a SigLIP2 NaFlex imaginative and prescient encoder, and a multimodal projector.
The projector features a two-layer MLP connector with pixel unshuffle, decreasing the variety of picture tokens and bettering throughput.
Customers can regulate parameters resembling the utmost variety of picture tokens or patches, permitting them to stability pace and high quality relying on the deployment situation. The coaching course of concerned roughly 100 billion multimodal tokens, sourced from open datasets and in-house artificial knowledge.
Efficiency and benchmarks
The fashions obtain aggressive benchmark outcomes throughout a spread of vision-language evaluations. LFM2-VL-1.6B scores properly in RealWorldQA (65.23), InfoVQA (58.68), and OCRBench (742), and maintains stable leads to multimodal reasoning duties.
In inference testing, LFM2-VL achieved the quickest GPU processing instances in its class when examined on a typical workload of a 1024×1024 picture and brief immediate.
Licensing and availability
LFM2-VL fashions can be found now on Hugging Face, together with instance fine-tuning code in Colab. They’re suitable with Hugging Face transformers and TRL.
The fashions are launched beneath a customized “LFM1.0 license”. Liquid AI has described this license as primarily based on Apache 2.0 rules, however the full textual content has not but been revealed.
The corporate has indicated that business use will likely be permitted beneath sure circumstances, with completely different phrases for corporations above and beneath $10 million in annual income.
With LFM2-VL, Liquid AI goals to make high-performance multimodal AI extra accessible for on-device and resource-limited deployments, with out sacrificing functionality.
