At $900, the actual GPU silicon in Nvidia’s B200 – 208 billion transistors across two 800mm dies fabricated on TSMC’s cutting-edge 4NP process – represents only 14% of total production cost. The chip design that makes Nvidia famous costs less than the packaging required to assemble it.
The B200 bill of materials reveals a counterintuitive coststructure. Logic die fabrication: $900 (14%). High Bandwidth Memory: $2,900 (45%). Advanced packaging: $1,100 (17%). Yield losses: $1,000 (16%). Other components: $500 (8%).
TSMC charges approximately $17,000 per 12-inch wafer at the 4NP process node. Die yield is modeled between 40% and 70%, centered at 60% – reflecting the challenges of producing high-area chiplets on leading-edge nodes. Even with these costs, the silicon itself is surprisingly affordable relative to total production cost.
Packaging plus yield loss ($2,100) exceeds logic die fabrication ($900) by more than 2x. The challenge isn’t making chips – it’s assembling them reliably.
Framework Analysis
This inverts how most observers think about semiconductor value. Nvidia’s genius is in architecture and software – CUDA, the parallel computing platform developing for 17 years. But the coststructure shows that physical assembly, not transistor design, dominates production economics.
TSMC’s Chip-on-Wafer-on-Substrate (CoWoS) packaging has been the real production bottleneck. CoWoS-L technology integrates a redistribution layer with a partial silicon interposer, enabling the stacking of more HBM around GPU dies. Each B200 module contains two GPU dies plus multiple HBM stacks, all interconnected on a single substrate.
The implication: Nvidia’s competitive moat isn’t primarily in manufacturing cost. It’s in architecture, software ecosystem, and the ability to secure scarce supply chain capacity. The Five Defensible Moats in AI framework shows why software lock-in matters more than silicon efficiency.
Strategic Implications
The 14% silicon cost explains why chip-matching competitors struggle. AMD’s MI300X delivers comparable silicon performance to Nvidia’s H100. But matching silicon doesn’t replicate the CUDA ecosystem, the enterprise relationships, or the supply chain positioning.
It also explains Nvidia’s system strategy. By selling DGX servers and rack-scale solutions rather than bare chips, Nvidia captures value from integration that would otherwise go to system builders. The company effectively internalizes the packaging and assembly premium.
For TSMC, the data validates its strategic pivot toward advanced packaging. CoWoS capacity – not leading-edge logic – has become the binding constraint on AI chip production. TSMC has secured over 70% of CoWoS-L capacity for Nvidia in 2025.
The Deeper Pattern
Value in semiconductors is migrating from transistor density to system integration. The era where smaller nodes automatically meant better economics is ending. Assembly, interconnection, and ecosystem now dominate competitive advantage.
Key Takeaway
Nvidia’s silicon brilliance is real, but it represents only 14% of production cost. Packaging, memory, and yield losses dominate – shifting where value and vulnerability concentrate in the AI chip supply chain.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
