Liquid AI Releases LFM2.5-VL-3B, a 3.1B-Parameter Vision-Language Model for On-Device Inference

Liquid AI has released LFM2.5-VL-3B, a 3.1-billion-parameter vision-language model designed to run on local hardware rather than in the cloud. The model is available now on Hugging Face and ships with support for llama.cpp, MLX, vLLM, SGLang, and ONNX.

What's new

LFM2.5-VL-3B builds on Liquid AI's earlier LFM2-VL-3B with four claimed improvements: screen and UI understanding across device types, improved object grounding via natural-language queries, better multi-image reasoning, and significantly stronger function calling in both text-only and vision-text settings. The model answers directly rather than using chain-of-thought reasoning, which Liquid AI says keeps latency low for real-time and on-device use.

Architecture and training

The model pairs a SigLIP2 400M NaFlex vision encoder with the same pre-trained backbone used in Liquid AI's LFM2.5-2.6B text model. According to Liquid AI, it was pre-trained on approximately 34 trillion tokens, including 4x more vision data than prior releases, drawn from curated and synthetic image-caption, OCR, grounding, and instruction-following datasets. The tokenizer vocabulary was expanded to 128K to support non-Latin scripts, done by extending the existing tokenizer rather than retraining from scratch.

Post-training used two stages: supervised fine-tuning with knowledge distillation from a larger teacher model plus a technique Liquid AI calls "Antidoom training," followed by multi-reward reinforcement learning.

Benchmark results (as reported by Liquid AI)

Across vision benchmarks, LFM2.5-VL-3B posted an average score of 69.4, ahead of Gemma-4-E2B-it (57.2, 5.1B params), InternVL 3.5 2B (64.6), and Qwen3.5-2B (63.7), though behind the larger Qwen3.5-4B (70.1, 4.7B params). Notable individual scores include MathVista-mini (68.5), DocVQA-val (91.1), and RefCOCO-avg grounding (87.9, versus 57.1 for its predecessor LFM2-VL-3B).

On text-only benchmarks, tool-use performance improved sharply: BFCL V4 scored 32.5 versus 20.5 for the prior version, and ToolSandbox scored 59.5 versus 26.4. Liquid AI says this puts the model roughly on par with Gemma-4-E2B and Qwen3.5-2B on tool use. Instruction-following scores (IFEval: 82.3, Multi-IF: 59.4) also rose over the predecessor model.

Inference speed

Liquid AI reports the model decodes at 228 tokens/second on an Apple M5 Max and 116 tokens/second on a Ryzen AI Max+ 395, fitting in roughly 3 GB of memory. On a Galaxy S26 Ultra, it reportedly reaches 20 tokens/second running fully on-device. On GPU, the company claims output throughput of about 11,000 tokens/second at high concurrency on a single H100 — roughly double that of larger 4B-class models — translating to nearly 1 billion output tokens per day per GPU, according to Liquid AI's testing.

Access

The model is available on Hugging Face under the identifier LiquidAI/LFM2.5-VL-3B, compatible with transformers>=5.10.1. Liquid AI has also published a browser-based WebGPU demo supporting multi-image chat, grounding, OCR, and tool calling. Pricing is not applicable; the model is distributed as open weights for local deployment. Training data cutoff was not disclosed.

What this means

LFM2.5-VL-3B targets a specific niche: vision-language capability that fits on phones, laptops, and edge devices rather than requiring cloud inference. The reported gains in grounding and tool calling matter for agentic use cases — apps that need to identify UI elements and act on them, such as screen automation or GUI agents. The claimed throughput advantage on GPU (11K tokens/second at high concurrency) also positions the model for high-volume server deployment, not just edge use, if Liquid AI's benchmarks hold up under independent testing. As with any vendor-reported benchmark, the comparisons against Gemma, InternVL, and Qwen model families should be treated as claims until third parties replicate them.