#local-ai

The post answers rumors from the previous few days that Qwen4 would be the last open-weight Qwen series and that later models would be available only through an API. It shares an image of a statement it attributes to Qwen head Liu Dayiheng, saying future Qwen 4.0 and Qwen 5.0 models will continue to be open source, that AI's value should not belong to only a few people, and that the series has passed 3 billion downloads, 460 open-source models and 300,000 derivative models. We could not find a separate report of the Qwen 4/5 wording; 36Kr reported very similar remarks, and the same figures, from Liu's first appearance as Qwen's leader at Alibaba's conference in Hangzhou on September 22.

It was the top thread of our sweep relative to community size: about 4,800 upvotes and 500 comments in a 329,000-member community. The most-upvoted replies celebrate the pledge; others praise how capable Qwen's models are for their size. Skeptics point out that a company's 'forever' promise lasts only as long as its current management and business conditions, and a few add that it holds only as long as China's government allows it.

The post shares an image and a transcription of remarks it attributes to OpenAI CEO Sam Altman: that local inference (running AI models on your own computer) is "a terrible allocation of scarce resources," because it leaves part of the world's limited supply of memory chips sitting in personal computers that are in use only a small fraction of the time, while cloud data centers can keep that hardware busy. The poster adds a joking summary framing it as a seller explaining why you shouldn't own the product. We could not find the original interview or post the quote comes from, so treat the wording as the poster's.

The most-upvoted replies read the remarks as self-interested. Several argue that hardware people own is also used for gaming and rendering, and one compares the argument to dismissing personal computers in the 1970s. A minority concede that, in purely technical terms, shared cloud hardware is probably more efficient, while saying they still don't want a few companies controlling it. About 3,800 upvotes and 1,700 comments in a 329,000-member community.

A developer shared DigUp, a free Mac app that runs Google DeepMind's EmbeddingGemma 2 locally through llama.cpp so you can search text, images, audio and video by description, such as "where they talk about sleep" in a podcast, and jump to the moment. The poster says the model download is 865 MB and indexing peaks under 2 GB of memory. The top reply praised it as how self-promotion should be done; others asked for a command-line version.

A post in r/LocalLLaMA says PewDiePie used OpenAI's API to build training data for a local model, was banned for using its outputs to train another model, won an appeal, did it again and was banned a second time, then switched to open-source tools to build a fully local agent. Those details are the poster's account. The top reply, with about 1,600 upvotes: "Especially because he paid them. They paid no one." Other replies point to OpenAI's own data scraping and to how expensive the hardware for local models still is.

It scored highest of any thread in our sweep relative to its subreddit's size (about 4,300 upvotes in a 329,000-member community).

A poster says they bought an abandoned crypto-mining rig on Avito (a Russian classifieds site) for about $35, negotiated down from $60, and found nine working 6 GB GPUs, 54 GB of VRAM in all, with only an SSD missing. The top reply teases them for haggling: "Bro has no conscience negotiating 54GB VRAM down from $60 to $35 in 2026."