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Next up, let’s load the model onto our GPUs. It’s time to understand what we’re working with and make hardware decisions. Kimi-K2-Thinking is a state-of-the-art open weight model. It’s a 1 trillion parameter mixture-of-experts model with multi-headed latent attention, and the (non-shared) expert weights are quantized to 4 bits. This means it comes out to 594 GB with 570 GB of that for the quantized experts and 24 GB for everything else.
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Over the last 18 months or so, the industry has begun adding a lot of structure around this core concept with two goals in mind: increasing response quality and/or reducing token usage.
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Designers also try to save frames by sharing brand-new bricks with other teams, giving them a heads-up that they might come in handy for other sets, too. Some of that happens automatically: “When someone puts in an order for a particular color change, we can see it showing up in the library of digital bricks,” says Scott.
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