Setup LFM2.5-VL-450M PC with NPU with Native FP4 No-Code Guide

Running this model locally is fastest when deployed through Docker.

Use the instructions provided below to complete the setup.

The setup auto-streams the model assets (expect a multi-GB download).

The smart installation system will instantly find the perfect configuration for your specific hardware.

📎 HASH: eee89bd1cfefea9e66991568f28ef093 | Updated: 2026-06-23
  • Processor: next-gen chip for heavy context processing
  • RAM: required: 16 GB absolute minimum for small models
  • Disk Space: 100 GB for multi-modal model vision components
  • Graphics: CUDA Compute Capability 8.0+ required for flash-attention

The LFM2.5-VL-450M is a state‑of‑the‑art multimodal language model that combines advanced vision and language understanding in a single unified architecture. It leverages a large‑scale contrastive pre‑training regimen that aligns image embeddings with textual representations, enabling precise cross‑modal retrieval. With 450 million parameters, the model achieves competitive performance on benchmark datasets while maintaining a relatively small memory footprint. Its design incorporates a hierarchical attention mechanism that dynamically focuses on salient visual regions and contextual words, improving coherence in generated captions. The model supports real‑time inference on consumer‑grade hardware and is optimized for integration into applications requiring robust visual‑language tasks such as image captioning, visual question answering, and content moderation. It was trained on a diverse collection of publicly available image‑text pairs and curated domain‑specific datasets, ensuring broad coverage and reduced bias.

Parameters 450 M
Input Modalities Text, Images
Output Modalities Text (captions, Q&A), Image tags
Training Data Public image‑text pairs + curated datasets
Inference Speed Real‑time on consumer GPUs
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