{
  "video_id": "reddit_1u402xu",
  "channel_slug": "LocalLLaMA",
  "channel_handle": "r/LocalLLaMA",
  "title": "What are ultra-tiny llms used for?",
  "url": "https://www.reddit.com/r/LocalLLaMA/comments/1u402xu/what_are_ultratiny_llms_used_for/",
  "external_url": null,
  "upload_date": "20260612",
  "published_at": "2026-06-12T16:24:13+00:00",
  "transcript": "On huggingface i see numerous sub 100m models like SupraLabs/Supra-50M-Instruct and finnianx/michel-tiny , but i really cant imagine a usecase for them. Does anyone here have experience with such tiny llms, or knows of a use case?\n\n\n\n--- Top Comments ---\n\n\n[113 upvotes] I think a lot of people are mostly using them to learn how to make LLMs. If you have limited hardware it is vastly easier to start with a model with a tiny vocabulary and only a handful of layers to train in some vaguely reasonable time on kind-of reasonable hardware.\n\nIt's also a reasonable-ish way to try out entirely different architectures for proof of concept reasons. Sparse attention, text diffusion, and many other architectural changes are likely first tested this way and then scaled up after.\n\nLarge models with large vocabularies and many layers take thousands of hours to train on really big iron (think NVL72 or better) hardware...and most people just don't have the funding to do that.\n\n[51 upvotes] They are fine tuned for classification, text-extraction, narrow-purpose instruction following, etc. They are way faster at inference. Many engineering problems exist at a scale where normal inferencing times are unacceptable. e.g. If you have to process 10 million rows of data, a 15 second response time means having to wait 5 years for it to finish.\n\n[17 upvotes] They're used for very specific tasks like GLM-OCR, which is a 0.9 billion model specifically used for extracting information out of PDFs \n\n[12 upvotes] Phones. \n\nI.e the app Layla.",
  "transcript_chars": 1533,
  "ingested_at": "2026-06-13T01:30:09.160281+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 53,
    "upvote_ratio": 0.9,
    "num_comments": 53,
    "author": "Commercial-Okra-8475",
    "is_self": true
  }
}