Genesis: From Pattern Recognition to Analytical Framework
This framework did not emerge from a single insight. It crystallized over 18 months of deep-dive research — from Research #01 (Baidu's ERNIE, Dec 2024) through Research #15 (DeepSeek V4 architecture decomposition, Apr 2026) — tracking how China's leading AI labs responded to a singular constraint: the inability to access NVIDIA's frontier B200/B300 GPUs.
The timeline tells the story. In late 2024, DeepSeek demonstrated that MoE + MLA could match dense model quality at a fraction of training cost. Through 2025, we watched successive architectural innovations — sparse attention, hash-based retrieval, manifold-constrained connections, INT4-native quantization — each pushing the same direction: doing more with less silicon. By May 2026, even as the US relaxed GPU export controls, H200 remains the ceiling for China-bound sales. DeepSeek V4-Pro's architecture, born from this constraint, had already rendered the bandwidth premium between H100 and B200 largely irrelevant for its workloads.
The breakthrough came during the complete decomposition of V4-Pro's architecture: when we mapped every projection matrix, every compression ratio, every FLOPs-per-byte calculation onto the Roofline model, a universal pattern became visible. The same analytical lens — how efficiently does a model convert memory traffic into computation? — could evaluate any architecture on any hardware. What had been scattered observations across 15 research notes converged into a single, quantifiable framework.
Core thesis: The frontier of LLM competition is not parameter count — it is memory traffic density. The architectures that push their Ridge Point leftward (requiring less bandwidth to become compute-bound) will dominate on cost, throughput, and hardware flexibility. This framework quantifies exactly how far left each architecture has moved.