r/LocalLLM rules, stats, and what to post
Know the rules before you post. So easy grandma can do it.
- To post
- No minimum in the rules
- To comment
- No minimum in the rules
- Self-promotion
- Allowed with conditions
- Self promotion should not be more than ten percent of your content.
- Links
- Not mentioned in the rules
- Promo risk
- High
- From the rules, not from removal counts
- Community age
- 3 years
- Since Mar 2023

About r/LocalLLM
A source-backed Rankhog guide to r/LocalLLM: public rules, community context, posting fit, and startup-promotion risk before you publish.Checked from the r/LocalLLM rules on Reddit.
r/LocalLLM rules
6 rules, in the community's own words.
- 1
Off-Topic Posts
Posts must be related to the topic of LLMs.
- 2
Low Effort Posts
Before posting a question, please spend a reasonable amount of time searching for existing questions that may provide an answer.
- 3
Limit Self-Promotion
The 1/10th rule is a good guideline: self-promotion should not be more than 10% of your content.
- 4
Follow Reddit's Content Policy
Please remember to follow Reddit's Content Policy.
- 5
Do not duplicate posts under review
If your post in under review by moderators, do not spam similar posts in an attempt to get around it or you will be banned.
- 6
Post is not local LLM related.
This subreddit is about local LLM usage. Other (closed) models can be discussed, but should not be the primary focus of a post.
What to post in r/LocalLLM
What fits the room, and what gets removed.
Works well
- A walkthrough of your local LLM setup, including the hardware you run it on and what you learned
- A genuine question about running a model locally after you have searched the subreddit and can say what you already tried
- Sharing an open source tool you built, framed as a contribution to local LLM users, kept to a small share of your overall activity
- A comparison post where the local model is the main subject and closed models only appear as context
- A troubleshooting story about a local inference problem you solved, written to help others with the same issue
- A discussion post asking the community how they handle a common local LLM challenge, with no product mention at all
Avoid
- Posting about closed cloud models as the main subject of your post
- Asking a question without searching the subreddit first
- Promoting your own tool in more than about one out of ten posts and comments
- Spamming duplicate posts while your first post sits under review
- Sharing content that breaks Reddit's content policy
- Starting a thread that has nothing to do with large language models
Best hours to post in r/LocalLLM
No pattern yet. r/LocalLLM does not have enough top posts from the last 90 days to name its best hours.
What actually works in r/localllm
These are the highest scoring posts Rankhog observed in r/localllm over the past month, with what each one did differently. Rankhog watches this community continuously, so it sees posts when they are published and again as they climb.

- Upvotes
- 1.3k
- Comments
- 153
- Posted
- Nov 4, 2026
Why it worked
- Rankhog breakout tier from a local post-score estimate
- Told in the first person
- Almost no downvotes (97% upvoted)

- Upvotes
- 857
- Comments
- 223
- Posted
- Nov 5, 2026
Why it worked
- Rankhog breakout tier from a local post-score estimate
- Leads with a concrete number
- Short paragraphs rather than one block of text
- Almost no downvotes (96% upvoted)

- Upvotes
- 833
- Comments
- 190
- Posted
- Nov 6, 2026
Why it worked
- Rankhog breakout tier from a local post-score estimate
- Told in the first person
- Short paragraphs rather than one block of text

- Upvotes
- 829
- Comments
- 225
- Posted
- Nov 8, 2026
Why it worked
- Rankhog breakout tier from a local post-score estimate
- Almost no downvotes (96% upvoted)

- Upvotes
- 444
- Comments
- 162
- Posted
- Nov 6, 2026
Why it worked
- Rankhog breakout tier from a local post-score estimate
- Asks the community a question instead of announcing something
- Broken into a scannable list
- Short paragraphs rather than one block of text
- Almost no downvotes (97% upvoted)

- Upvotes
- 437
- Comments
- 46
- Posted
- Nov 4, 2026
Why it worked
- Rankhog breakout tier from a local post-score estimate
- Almost no downvotes (98% upvoted)

- Upvotes
- 421
- Comments
- 579
- Posted
- Nov 5, 2026
Why it worked
- Rankhog breakout tier from a local post-score estimate
- Asks the community a question instead of announcing something
- Kept short enough to read in one pass

- Upvotes
- 338
- Comments
- 126
- Posted
- Nov 8, 2026
Why it worked
- Rankhog breakout tier from a local post-score estimate
- Short paragraphs rather than one block of text

- Upvotes
- 286
- Comments
- 79
- Posted
- Nov 5, 2026
Why it worked
- Rankhog breakout tier from a local post-score estimate
- Broken into a scannable list
- Short paragraphs rather than one block of text

- Upvotes
- 261
- Comments
- 132
- Posted
- Nov 6, 2026
Why it worked
- Rankhog breakout tier from a local post-score estimate
- Leads with a concrete number
- Short paragraphs rather than one block of text
- Almost no downvotes (97% upvoted)
What r/LocalLLM Is For
This is a subreddit for discussing large language models that run locally, on your own hardware, rather than through a hosted service. People come here to talk about running models themselves, and the rules make clear that the local angle is the heart of the community. Closed models are not banned from conversation, but a post should not make them the main focus. If you build or market anything in this space, you are talking to people who care about keeping their AI on their own machines.
Rules That Matter Before Posting
Four rules decide whether a marketing adjacent post survives here. “Off-Topic Posts” requires every post to relate to LLMs, and “Post is not local LLM related.” goes further by saying this subreddit is about local LLM usage, so closed models can be discussed but should not be the primary focus of a post. “Limit Self-Promotion” points to the 1/10th rule, meaning self promotion should not be more than 10% of your content. “Low Effort Posts” asks you to spend a reasonable amount of time searching for existing questions before you post a new one. On top of those, “Do not duplicate posts under review” bans spamming similar posts to get around moderation, and “Follow Reddit's Content Policy” applies to everything you share.
What To Post
A good post here is genuinely about running LLMs locally, and it shows you did your homework first. Search the subreddit for your question or topic before posting, because the low effort rule is enforced through that expectation. If you have built something, share it as part of a broader contribution, keeping your self promotion to no more than about one in ten of your posts and comments. Write posts that center local usage, like your setup, your experience running models on your own hardware, or a problem you solved, and keep any mention of closed models to a side note rather than the headline.
What To Avoid
The fastest way to lose a post here is to make a closed model the primary focus, because “Post is not local LLM related.” exists for exactly that. Posting without searching first trips “Low Effort Posts”, and pushing your own product too hard trips “Limit Self-Promotion”. If a post sits under review, do not submit copies, because “Do not duplicate posts under review” says spamming similar posts to get around it will get you banned. Anything off the LLM topic fails “Off-Topic Posts”, and everything you post must also satisfy “Follow Reddit's Content Policy”.
A note from Anthony
This is one of the few communities where the rules tell you exactly how much self promotion is acceptable, and I respect that clarity. The local focus rule is the one that catches people, because a post about a closed model can be fine as a side note but cannot be the main event. I also like that the search before posting rule is written as a courtesy to the community rather than a punishment. Come here to be useful first and your tool mentions will land better.
By Anthony Riera, founder and operator of Rankhog. Every guide is built from the community's own public rules and data, then checked by Rankhog's grounding audit before it goes live.
Common questions about r/LocalLLM
What subreddits are similar to r/LocalLLM?
Subreddits like r/LocalLLM are r/ChatGPT, r/singularity, r/MachineLearning, r/OpenAI, r/ArtificialInteligence, r/artificial, r/ClaudeAI and r/LocalLLaMA.
Can I ever mention closed models here?
Yes, but the rule on local focus says closed models should not be the primary focus of a post. Keep your post centered on local LLMs and treat closed models as a side mention at most.
How much self promotion is actually allowed?
The rule text points to the 1/10th rule as a good guideline, meaning self promotion should not be more than 10% of your content. There is no stated hard number of links, so keep promotion rare and your overall activity mostly non promotional.
What happens if I repost while my post is under review?
The rules say do not spam similar posts to get around review, and doing so will get you banned. Wait for the moderators to act on your original post.
Are links allowed in posts?
The rules do not say links are banned, but any link you share still has to respect the self promotion limit and Reddit's content policy. Share links that genuinely help the discussion.
Do I need to search before asking a question?
The low effort rule asks you to spend a reasonable amount of time searching for existing questions that may already answer yours before you post a new one.
Subreddits like r/LocalLLM
Other rooms for the same crowd.