发生了什么: 22,580: GPT-2 to Kimi K3, explained

Kimi K3 contains 22,580 times more parameters than GPT-2, but architectural innovations like linear attention and DeltaNet fundamentally changed how models process sequences.

为何重要: 22,580: GPT-2 to Kimi K3, explained

Engineers building or optimizing large language models need to understand how efficiency techniques evolved from 2019 to 2026 to make informed architecture choices.

注意: 22,580: GPT-2 to Kimi K3, explained

Linear attention trades softmax expressiveness for fixed-size state, reducing memory bandwidth but potentially losing fidelity. DeltaNet addresses information interference in fixed caches but adds complexity.

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22,580: GPT-2 to Kimi K3, explained

Baseten · 发布于 2026年7月30日 · 阅读约3分钟

30秒读懂

发生了什么: Kimi K3 contains 22,580 times more parameters than GPT-2, but architectural innovations like linear attention and DeltaNet fundamentally changed how models process sequences.
为何重要: Engineers building or optimizing large language models need to understand how efficiency techniques evolved from 2019 to 2026 to make informed architecture choices.
注意: Linear attention trades softmax expressiveness for fixed-size state, reducing memory bandwidth but potentially losing fidelity. DeltaNet addresses information interference in fixed caches but adds complexity.

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