posted in Technology

If open weight models are the future, U.S. AI companies are going to have a hard time

www.fastcompany.com/91577359/why-u-s-ai-companies-cant-match-chinas-open-weight-frontier-models
Fast CompanyIf open weight models are the future, U.S. AI companies are going to have a hard timeOpenAI, Anthropic, and other Western labs have spent billions training the model weights at the heart of their most advanced systems.

Replying to @⁨sanitation@lemmy.today⁩

I ran DeepSeek and Llama and Mistral at home on my consumer grade gaming PC.

With a little tweaking of the system prompts and configuring web search, I was running a local LLM that felt pretty darn close to the commercial LLMs.

With this technology out in the open internet where you can download the models in a few hours I don’t see how the commercial AI companies are going to last. If selling “Artificial Intelligence” subscriptions is all your company does for revenue, you’re screwed.

I downloaded and ran an LLM that I could have a conversation with and feed basic coding problems to for basically zero dollars and ran it on my puny gaming machine…puny compared to enterprise-class hardware. It would be trivial for a company with a very moderate budget to buy some servers and start running their own LLMs that they can use to feed all the PII and HIPPA data they want.

Replying to @⁨DJKJuicy@sh.itjust.works⁩

Depends on your RAM (main + GPU), but assuming 32GB total: Qwen 3.6 35B A3B for coding support, Gemma 4 26B for general stuff. The LM Studio app curates a list of recommended models that will run well in it and makes it easy to run them.

Mind you, what I like most about local models is their limitations, because it turns out closed models have limitations of the same nature, just with quite a bit more runway; and becoming aware of those limitations is valuable.

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