How does Vizuara's attention family series compare attention in Llama 3, DeepSeek V3 and Kimi Linear?

Vizuara's lecture maps the three models onto its seven buckets. All three use scaled dot-product scoring, causal (masked) attention and the text domain. Llama 3 adds grouped query attention, rotary position embedding (RoPE) and flash attention. DeepSeek V3 uses multi-head latent attention with a decoupled RoPE and also flash attention. Kimi Linear shares multi-head latent attention with DeepSeek V3 but adds a 3:1 hybrid with Kimi Delta attention and uses no position embedding (NoPE).

• The instructor notes models also differ in parameter count, number of heads and number of stacked blocks, but the buckets capture the attention-level choices.
• DeepSeek V3's decoupled RoPE is held for a later lecture in the series.

▸In their words

“So you can see that the bucket number one and two and seven are exactly the same across these three models.” Instructor · at 17:47 — open in the transcript →

Source: our own transcription of the episode. The full conversation is on The attention family (2026-09-01). Read the whole episode summary →

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