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For the uninitiated, what's a "hybrid linear attention architecture"?
Hey thanks for asking this question. It lead to good replies
any hardware recommendations? how much memory do we need to this?
Amazing how fast AI keeps improving, every new model feels like a big step forward
I switched from chatgpt to Perplexity; and now to Kimi K2, after reading an article here explaining that all the fear around some of the Chinese models spying and so on.. is simply not true. I have to say that in my experience Kimi K2 is way better than perplexity. I hope we can get our act together. Seems that building this Ai's requires a level of collaboration that is in opposition to greed.
Any comparison with existing models on common benchmarks? Text? Coding? MMLU?
Did you even look at the article?

Evaluation Benchmarks Our evaluation encompasses three primary categories of benchmarks, each designed to assess distinct capabilities of the model:

• Language Understanding and Reasoning: Hellaswag [121], ARC-Challenge [14], Winogrande [83], MMLU [36], TriviaQA [47], MMLU-Redux [26], MMLU-Pro [103], GPQA-Diamond [82], BBH [94], and [105].

• Code Generation: LiveCodeBench v6 4 [44], EvalPlus [60].

• Math & Reasoning: AIME 2025, MATH 500, HMMT 2025, PolyMath-en.

• Long-context: MRCR 5 , RULER [38], Frames [52], HELMET-ICL [118], RepoQA [61], Long Code Arena [13] and LongBench v2 [6].

• Chinese Language Understanding and Reasoning: C-Eval [43], and CMMLU [55].

Everyone is worried about AI data centers destroying the planet with their extreme energy needs. Though it seems we have a big learning curve still to make AI inference and training more efficient.

How likely are we to NOT see the AI data center apocalypse through better algorithms?

How does Gemini have a million token context window?