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    "story_id": "mp-2026-10-14-005",
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    "headline": "A Tiny Image Model Trains in 70 Minutes on Four Consumer GPUs",
    "slug": "a-tiny-image-model-trains-in-70-minutes-on-four-consumer-gpus",
    "dek": "MiniDog pretrains a 22-million-parameter dog-image model in 55 minutes and fine-tunes it in another 15.",
    "summary": "MiniDog pretrains a 22-million-parameter dog-image model in 55 minutes and fine-tunes it in another 15.",
    "body_text": "MiniDog is a deliberately narrow demonstration that text-to-image pretraining need not begin at frontier scale. Its authors cache image and text features, combine a small transformer with a semantic latent autoencoder and use 26,000 ImageNet dog images for pretraining plus 2,000 generated images for fine-tuning. They report 55 minutes for pretraining and 15 for fine-tuning on four RTX 3090 GPUs. The work concerns a restricted dog-breed domain and does not imply that a general-purpose generator can be trained this cheaply. Its value is pedagogical: it makes an end-to-end diffusion-transformer experiment reachable on workstation-class hardware.",
    "why_it_matters": "MiniDog pretrains a 22-million-parameter dog-image model in 55 minutes and fine-tunes it in another 15.",
    "limitations": [
      "MiniDog is a deliberately narrow demonstration that text-to-image pretraining need not begin at frontier scale.",
      "The work concerns a restricted dog-breed domain and does not imply that a general-purpose generator can be trained this cheaply."
    ],
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    "first_published_at": "2026-10-14T09:00:00.000-04:00",
    "modified_at": "2026-10-14T09:00:00.000-04:00",
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        "text": "MiniDog pretrains a 22-million-parameter dog-image model in 55 minutes and fine-tunes it in another 15.",
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        "qualification": "MiniDog is a deliberately narrow demonstration that text-to-image pretraining need not begin at frontier scale."
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    "tags": [
      "MiniDog",
      "diffusion models",
      "consumer GPUs"
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    "image_url": null,
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  "sources": [
    {
      "source_id": "source-2026-10-14-005",
      "title": "arXiv preprint 2610.18870, original v1",
      "publisher": "arXiv / submitting research authors",
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      "published_at": "2026-10-13T13:59:53.000-04:00",
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    "name": "The Machine Press",
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    "description": "A daily newspaper for the age of artificial intelligence."
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    "title": "A Tiny Image Model Trains in 70 Minutes on Four Consumer GPUs",
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