Baseten engineer says Kimi K3’s leap came from a chain of targeted fixes, not scale alone
khademinori · x · 2026-07-28
A Baseten engineer spent 48 hours dissecting Kimi K3’s architecture and argued the model’s gains are not just about scale.
- The thread says K3 can “fit” 22,580 GPT-2-sized models from 2019, highlighting how far model scale has advanced in seven years.
- But the core claim is that the progress came from a sequence of targeted fixes, not brute-force parameter growth.
- It traces the path from GPT-2 to linear attention, DeltaNet, gated DeltaNet, and Kimi’s own KDA attention.
- Each iteration is framed as solving a specific weakness of the previous generation: memory limits, information interference, inability to selectively forget, and residual stream dilution.
- The post also argues open source accelerates this kind of architectural scrutiny, because once code is public, outside engineers can quickly map out the technical lineage.
Related event: Kimi K3 shifts attention from scale to architecture(25 posts)→
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