r/LocalLLaMA: rules, karma requirements and posting culture

Who posts here

Posts assume you know what a quant, a context window, prefill and decode speed are, and nobody stops to explain them. Typical threads compare quantization formats on a named GPU or report tokens per second after a config change. The rules send beginner questions like how to use a model or what your hardware can run to search, and those posts are removed as low effort.

• Hobbyists with one or more consumer GPUs, tuning inference engines and posting their numbers.
• Engineers who fork engines, train small models from scratch or fine-tune open weights, and write it up.
• People with serious home rigs or workstation cards, comparing setups and power bills.
• Newcomers with a single card or a laptop asking what fits, who get answers only when the question is specific.
• Builders of tools around local models, posting under the I Built A Thing flair.

Karma and account requirements

What it takes to get a post through in r/LocalLLaMA.

What you can post

What happens to a new post

Where the most recent posts ended up. Sample size: 400.

• Stayed up84%
• Removed by Reddit's filters7%
• Removed by moderators8%
• Deleted by the author1%

Reddit counts a removal by the subreddit's own AutoModerator rules as a moderator removal, so that share is not all human. Held by AutoModerator means the post is waiting in the mod queue.

What happened when we posted here

We have posted here ourselves. This is how our own posts and comments fared.

Stayed up

100%

Removed by Reddit's filters

0%

Removed by moderators

0%

Period

January 2026 to August 2026

The rules, in the mods' words

1. 1

   Please search before asking

   Before submitting a post to ask a question, please search this subreddit and related resources. To maintain community quality, questions that fall under Rule 3 (Low Effort Posts) may be removed. This includes questions like: - How to use Llama? - Where to download models? - What models can I run with this hardware? - Help! I'm having *[insert error message here]* Questions that cannot be found by searching are always allowed. If you need help with PC building and budgeting, try r/buildapc

2. 2

   Off-Topic Posts

   Posts must be directly related to Llama or the topic of LLMs.

3. 3

   Low Effort Posts

   Asking questions is allowed, but it's kindly asked that users first spend a reasonable amount of time searching for existing questions on this subreddit or elsewhere that may provide an answer. Since this subreddit receives a high volume of questions daily, this rule is in place to help reduce identical posts. If you can't find an answer to your question and want to make a post here, please be clear and comprehensive when asking to improve your chances of getting an answer from the community.

4. 4

   Limit Self-Promotion

   This is an open community that highly encourages collaborative resource sharing, but self-promotion should be limited. The 1/10th rule is a good guideline: self-promotion should not be more than 10% of your content. Additionally, if you are sharing your project: - Please do not use any sensationalized titles. - Do not use any affiliate links when linking to content. Links must be directly to the source, such as GitHub or Hugging Face. - Do not flair your own project as News. Use Resources.

5. 5

   Follow Reddit's Content Policy

   Posters and commenters are expected to act in good faith. Treat other users the way you want to be treated. Avoid straw-manning and bad-faith interpretations. Avoid presenting misinformation as factual. Please remember to follow Reddit's Content Policy (https://www.redditinc.com/policies/content-policy).

These are the rules as archived in January 2025. Rules change. Check the sidebar before you post.

The culture

r/LocalLLaMA has about 840,000 members and close to 80 posts a day. It started as a room for one model family and is now the default place to talk about running any open-weight model on hardware you own. The typical post is long for Reddit, around 190 words, and it is full of specifics: the GPU, the amount of RAM, the quant, the context size, the tokens per second.

About 63% of posts are text, with links, images and video making up the rest. The staples are benchmark write-ups, config questions, new model releases and projects people built over a weekend. The posts that go furthest are different: news of a big open-weight release, jokes about GPU prices, and arguments that the large labs are pushing regulation to hurt open models. Hardware cost and memory shortages run through the comments.

The live fault line is promotion. One of the most upvoted recent threads asked the moderators to do something about advertising posts for a paid product filling the feed. Posters now open with lines saying they are not a bot or a shill, and people sharing their own work lead with a disclosure and the limits of what they made. Readers check for that.

What lands

• A measured benchmark on named hardware, with the method stated and the caveats included. Negative results are welcome.
• A real engineering project with the work shown: an engine fork tuned for one machine, a small model trained from scratch, an inference engine written in assembly.
• News of a new open-weight release, posted fast with the key numbers in the body.
• Hardware humor. A step by step joke about flying abroad to buy a cheaper graphics card is among the top recent posts.
• Opinion posts that take the side of open models against the large closed labs.

What gets removed or ignored

• Tool launches filed under Discussion. Discussion is the flair moderators remove most in our sample, and a lot of those were product announcements.
• Broad hardware questions of the kind the rules name: what should I run on this machine, help me pick a setup.
• Open prompts with no content of your own, such as asking everyone what they use local models for or which quant they settled on.
• The same project posted twice under two different flairs.
• Recruiting posts: calls for beta testers and paid tasks.
• Posts from accounts with less than 5 karma in the subreddit, which AutoModerator removes on its own.

The unwritten rules

• State your setup in the first lines. Card, VRAM, system RAM, model, quant, engine. A question without them gets no useful answer.
• Claims need numbers. A speed claim without a method gets picked apart, and titles that sound too good get the same treatment.
• If it is your project, say so at the top and say what it cannot do. Readers are tired of hype and reward the post that leads with limits.
• Comments are short, around 24 words, and about one in twenty carries a link. Links to model pages and repos are normal. Links to a product page are not.
• Local means local. Posts about paid cloud models or API pricing sit badly here unless they make the case for running your own.

Self-promotion

The rule is called Limit Self-Promotion, and it limits, it does not ban. Your own work should be at most a tenth of what you post, titles must not be sensational, links go straight to the source such as GitHub or Hugging Face, and your project is flaired Resources, never News. In practice there is a dedicated I Built A Thing flair, it carries about 16% of posts, and many of those do well. What gets removed is the launch dressed up as a discussion, the repeat post, and anything that reads like a campaign. Open code, real measurements and an honest disclosure are the price of entry.

How people write here

Typical post length

190 words

Typical title length

11 words

Titles phrased as a question

18%

Typical comment length

22 words

Comments that include a link

5%

Posts that carry a flair

100%

Top keywords

The words and phrases that show up far more often here than in other communities, from recent posts in r/LocalLLaMA.

• model 155 posts
• models 124 posts
• qwen 71 posts
• llama.cpp 56 posts
• local 113 posts
• ram 50 posts
• tok 40 posts
• flash 43 posts
• vram 38 posts
• gpu 43 posts
• inference 40 posts
• context 71 posts
• weights 34 posts
• prefill 28 posts
• rtx 32 posts
• local models 29 posts
• decode 27 posts
• llm 49 posts
• flash next 24 posts
• strata 27 posts
• tokens 40 posts
• hardware 36 posts
• qwen3.8 23 posts
• gb vram 22 posts
• deepseek 23 posts
• qwen flash 19 posts
• coding 43 posts
• token 32 posts
• cpu 26 posts
• harness 26 posts
• gb ram 22 posts
• gpus 19 posts
• moe 18 posts
• iq3 16 posts
• gguf 16 posts
• qwen3.8-27b 17 posts
• memory 37 posts
• inference engine 16 posts
• engine 32 posts
• generation 31 posts

Common questions