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Article:Samuel Batista10 Nov 2026
DeepSeek V4-Pro Architecture: KV Cache Compression and Hyper-Connections Visualised
Samuel Batista breaks down the inner mechanics of DeepSeek V4-Pro across 61 layers and a 1-million-token context window. The visual breakdown explains compressed attention, four-lane hyper-connections, and sparse MoE routing. It shows how the model cuts KV cache footprint while scaling parameters.
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DeepSeek V4-Pro Architecture: KV Cache Compression and Hyper-Connections Visualised

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