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Joined 3 years ago
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Cake day: November 26th, 2023

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  • Running model that is good at everything require huge amount of energy and huge data center. Those models are mixture of experts. Latest Kimi K3 have 896 experts. Imagine you have company with 896 employees. Each question involves 16 employees to figure out what to do in what area of your business. Like a brainstorm to solve problem. Now if you know exactly what you want and in which area you actually need only 1-5 people. Like an agile team instead of all those people that you have. So you can hire just couple Kimi K3 experts. 16 experts are 100B parameters so roughly 1 expert in frontier open source model is 6B parameters. 5 experts is 30B parameters. You can run 27B Qwen 3.6 quantized into int4 on your computer like other people are doing right now.

    I posted link below to example where they fine tuned model ( take it like a employee training ) for specific task.









  • First thing is that the market is pretty young. Imagine you are now using IBM mainframe PC to run chatgpt and not even windows1.0 on your IBM pc.

    There are small AI models. Small agentic capable AI models that can do tool calling. GenAI and what you described. One thing is some solutions are mathematically impossible to gain certain quality. Other thing is they’re just not owned by billionaires and not used by corporations so press don’t cover them and nobody cares except few enthusiasts and people that need such capabilities and know limitations. Press only covers what they’re paid for or what can gain some traction.

    The hate on AI as you described doesn’t help AI as a science domain. It’s not really related to science but related to ineffective spending of money by billionaires.