{
  "video_id": "reddit_1u85t9c",
  "channel_slug": "LocalLLaMA",
  "channel_handle": "r/LocalLLaMA",
  "title": "Local models went from mostly useless to actually useful really fast. What changed?",
  "url": "https://www.reddit.com/r/LocalLLaMA/comments/1u85t9c/local_models_went_from_mostly_useless_to_actually/",
  "external_url": null,
  "upload_date": "20260617",
  "published_at": "2026-06-17T09:55:13+00:00",
  "transcript": "https://preview.redd.it/knc4ht7bft7h1.png?width=1048&format=png&auto=webp&s=49abdb8b0f358e799ecb06aa49134d9b0fd49336\n\nMitchell Hashimoto had a good point earlier: local models went from basically useless to actually useful in what feels like one year.\n\nI think thats pretty accurate.\n\nA year ago I mostly treated local models like toys for privacy, simple chat, or small RAG tasks. Now people are actually using Gemma, Qwen, GLM, Kimi, etc. for coding, private docs, local workflows and even replacing some API calls.\n\nI dont think they fully replace the best closed models for long repo work yet. The gap is still obvious when the task needs planning, context, and fixing its own mistakes.\n\nBut the jump in usable quality feels real.\n\nFor people running local models every day, what changed the most for you?\n\nBetter base models, better quants, better tools like llama.cpp/Ollama, more VRAM or something else?\n\n\n\n--- Top Comments ---\n\n\n[66 upvotes] I think qwen 3.6 27b is the mark of this transition. From model being able to making tool calls here and there to it being able to reliably do it over long context. \n\n[66 upvotes] Vram. It's all about vram; there's no use for a \"smart\" model if your setup will take hours to complete tasks. You maybe can run the slow model once or twice for the fun, but to be a daily drive, your Ai should respond quickly, so you need weights and kv fully in gpu.\n\n[49 upvotes] Honestly they were never \\*dogshit\\* like people who weren’t in the trenches say. They were orders of magnitudes less powerful than the closed offerings; gpt 3.5 turbo was not close to being distilled into a local offering, but they were never at the “this is a useless Markov chain” level people like that Twitter poster like to say. I built out tooling that I used to get people to pay me money using llama 3.1 8b (offsec/pentest related) in mid 24.\n\nDid it need a lot of work to hammer it into shape? Sure. But so does basically everything in offensive security so it wasn’t anything unfamiliar to grep | jq and mutate data before it hits a json blob. \n\n[39 upvotes] It's wild how a decent 7B model today can outperform what I was using locally a year ago",
  "transcript_chars": 2171,
  "ingested_at": "2026-06-17T13:30:11.710267+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 113,
    "upvote_ratio": 0.87,
    "num_comments": 64,
    "author": "BTA_Labs",
    "is_self": true
  }
}