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We run vision-language inference on CPU — 2.5GB projector, 0.5 tok/s, image encoding at <0.5s.
Vision-Language Inference on Consumer Hardware
The Problem
Vision-language models typically require GPU acceleration. Running multimodal inference on CPU requires optimization of the vision pipeline.
What We Built
Integration of Qwen2-VL's mmproj (f32, 2.5GB) with the language model. Images are base64-encoded, passed through the projector, and vision embeddings are interleaved with text tokens.
The Research
Image encoding via PIL, base64 conversion, then through mmproj. Total memory: ~3.5GB for model + projector.
Results
| Image size | Encode | Inference (50 tok) |
|---|---|---|
| 256x256 | 0.3s | ~100s |
| 512x512 | 0.5s | ~100s |
| 1024x1024 | 1.2s | ~100s |
Vision inference adds negligible encode time but projects 2.5GB of memory overhead for the mmproj.
Conclusion
Vision-language inference on CPU is functional with the Qwen2-VL 2B model, limited primarily by inference speed (~0.5 tok/s) rather than vision processing.
Full citation: Alpasan, L.-K. (2026). Vision-Language Inference on Consumer Hardware. The Anticloud Research Corpus.
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References:
- Lois-Kleinner Zenodo: https://doi.org/10.5281/zenodo.20781790
- Lois-Kleinner GitHub: https://github.com/kleinnner/Anticloud/tree/main/04-aioss-format
- Lois-Kleinner Harvard DV: https://doi.org/10.7910/DVN/FDEBAB
- Lois-Kleinner Internet Arc: https://archive.org/details/aioss-format
- Lois-Kleinner ORCID: https://orcid.org/0009-0009-2233-6107
- Lois-Kleinner DEV.to: https://dev.to/kleinner
- Lois-Kleinner LinkedIn: https://linkedin.com/in/kleinner
- Lois-Kleinner HuggingFace: https://huggingface.co/Anticloud
- Lois-Kleinner Tumblr: https://anticloud.tumblr.com
- Lois-Kleinner Mastodon: https://mastodon.social/@kleinner
- Lois-Kleinner Bluesky: https://bsky.app/profile/kleinner.bsky.social
- 0-1.gg: https://0-1.gg
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