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r/LocalLLM rules, stats, and what to post

Know the rules before you post. So easy grandma can do it.

248.8K members
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
A grandma in an orange sweater at an old beige computer, reading r/LocalLLM on Reddit. A Rankhog sticker and a Reddit sticker are on the computer.

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. 1

    Off-Topic Posts

    Posts must be related to the topic of LLMs.

  2. 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. 3

    Limit Self-Promotion

    The 1/10th rule is a good guideline: self-promotion should not be more than 10% of your content.

  4. 4

    Follow Reddit's Content Policy

    Please remember to follow Reddit's Content Policy.

  5. 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. 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.

  1. Screenshot of a post in r/localllm titled "Our “quite terrible” allocation of DRAM" with 1.3k upvotes
    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)

    Read the full thread on Reddit

  2. Screenshot of a post in r/localllm titled "QWEN 3.8 FN - 8bit FP quant - 96.32 tokens/s - Context of 95k - $1500 hardware" with 857 upvotes
    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)

    Read the full thread on Reddit

  3. Screenshot of a post in r/localllm titled "I removed guardrails from Qwen and it is performing EPICALLY" with 833 upvotes
    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

    Read the full thread on Reddit

  4. Screenshot of a post in r/localllm titled "Qwopeful 🤞🏼" with 829 upvotes
    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)

    Read the full thread on Reddit

  5. Screenshot of a post in r/localllm titled "DDR4 512GB vs. switching to DDR5 for MoE offload on a multi-GPU rig?" with 444 upvotes
    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)

    Read the full thread on Reddit

  6. Screenshot of a post in r/localllm titled "Nvidia RTX Spark encrypted firmware slows inference speed to 35% on Linux" with 437 upvotes
    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)

    Read the full thread on Reddit

  7. Screenshot of a post in r/localllm titled "Is bro right? Local is a pipe dream?" with 421 upvotes
    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

    Read the full thread on Reddit

  8. Screenshot of a post in r/localllm titled "Probably old news, but any website you have open can reach your local LLM server while you browse" with 338 upvotes
    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

    Read the full thread on Reddit

  9. Screenshot of a post in r/localllm titled "2x RTX PRO 6000 running DeepSeek-V4-Flash (284B) fully offline: 1,170 tok/s peak" with 286 upvotes
    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

    Read the full thread on Reddit

  10. Screenshot of a post in r/localllm titled "Qwen3.8 27B FP8 on 4× Radeon AI PRO R9700: 7K tok/s prefill, 814 tok/s C8 decode" with 261 upvotes
    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)

    Read the full thread on Reddit

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 , 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.

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