Model ID
LiquidAI/LFM2.5-VL-3B
Capabilities
LFM2.5 VL 3B
LFM2.5 VL 3B is a compact multimodal language model designed for efficient on-device vision-language tasks, combining the LFM2.5-2.6B language model with a SigLIP2 NaFlex vision encoder. It uses a 30-layer hybrid architecture with 2,048 hidden size, 32 attention heads, and 8 key-value heads, alternating short convolution and full-attention layers for efficient sequence processing. Supporting a 32K-token context window, it uses native-resolution image processing with 512×512 patches and thumbnail processing. The model is optimized for image grounding, object detection, full-page OCR, layout understanding, multilingual applications, and efficient on-device multimodal inference.
About this model
LFM2.5 VL 3B is a compact multimodal language model designed for efficient on-device vision-language tasks, combining the LFM2.5-2.6B language model with a SigLIP2 NaFlex vision encoder. It uses a 30-layer hybrid architecture with 2,048 hidden size, 32 attention heads, and 8 key-value heads, alternating short convolution and full-attention layers for efficient sequence processing. Supporting a 32K-token context window, it uses native-resolution image processing with 512×512 patches and thumbnail processing. The model is optimized for image grounding, object detection, full-page OCR, layout understanding, multilingual applications, and efficient on-device multimodal inference.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (LiquidAI/LFM2.5-VL-3B). Released under LFM 1.0. Further reading: Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
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