LiquidAI LFM2.5-VL-3B: a 3.1B local VLM that beats Gemma-4 E4B — screen understanding 2.5 → 82.2
artificial
LiquidAI LFM2.5-VL-3B: a 3.1B local VLM that beats Gemma-4 E4B — screen understanding 2.5 → 82.2
LiquidAI LFM2.5-VL-3B: a 3.1B local VLM that beats Gemma-4 E4B — screen understanding 2.5 → 82.2 TL;DR: LiquidAI released LFM2.5-VL-3B, a 3.1B vision-language model that runs fully local (llama.cpp, MLX, vLLM, even a WebGPU demo). • Beats Gemma-4 E4B (...
artificial

LiquidAI LFM2.5-VL-3B: a 3.1B local VLM that beats Gemma-4 E4B — screen understanding 2.5 → 82.2

LiquidAI LFM2.5-VL-3B: a 3.1B local VLM that beats Gemma-4 E4B — screen understanding 2.5 → 82.2

TL;DR: LiquidAI released LFM2.5-VL-3B, a 3.1B vision-language model that runs fully local (llama.cpp, MLX, vLLM, even a WebGPU demo).

• Beats Gemma-4 E4B (8B) 69.4 vs 59.7; edges Qwen3.5-4B • Screen understanding: 2.5 → 82.2 on ScreenSpot-v2 Web (huge jump) • 228 tok/s on M5 Max, 20 tok/s on a Galaxy S26 Ultra • Function calling / object grounding included

What I found interesting is the business angle: for simple document/screen tasks, local AI flips 'AI feature' from a monthly API bill into a one-time engineering task with zero data leaving the building. Big cloud models still win for complex reasoning — this just made the small-model bucket genuinely usable.

I wrote a short analysis here: https://www.zyntopia.com/news/lfm2-5-vl-3b-edge-vision

submitted by /u/Designer_Athlete7286
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