NVIDIA B200 Cost Breakdown: What Blackwell Really Costs to Manufacture

The NVIDIA B200 represents the most expensive merchant AI accelerator ever produced. At an estimated manufacturing cost of ~$6,400, it nearly doubles the H100's ~$3,320 COGS — yet NVIDIA sells it for approximately $40,000, maintaining an estimated 84% gross margin. Understanding where that $6,400 goes, and why each component costs what it does, is essential for anyone forecasting AI infrastructure budgets, evaluating competitive alternatives, or modeling the economics of next-generation data centers.

This analysis walks through every layer of the B200's manufacturing cost using data from our Cost Bridge tool, which models 13 AI accelerators side by side.

The B200 at a Glance

The B200's dual-die design is the fundamental architectural shift from the H100. Instead of one monolithic GPU die, Blackwell uses two GPU dies connected via a 10 TB/s chip-to-chip NVLink-C2C interconnect on a single CoWoS-L package. This approach improves effective yield (smaller dies yield better) while dramatically increasing total compute density — at the cost of significantly more complex packaging.

Manufacturing Cost Breakdown

The B200's estimated $6,400 COGS breaks down across four primary cost buckets. Explore the full breakdown interactively in the Cost Bridge Chart.

Logic Die: ~$850 (13% of COGS)

The B200 uses two GPU dies, each approximately 800mm², fabricated on TSMC's 4NP process (an optimized variant of the N4 family). At an estimated wafer cost of ~$16,000-$17,000 for 4NP and a mature yield around 70-75% for an 800mm² die, each good die costs roughly $350-$425. Two dies bring the total logic cost to approximately $850.

This is counter-intuitive: despite having nearly double the total silicon area, the B200's logic die cost is only ~$550 more than the H100's ~$300. The reason is yield. An 814mm² monolithic die has significantly lower yield than two 800mm² dies (because defect probability scales exponentially with area). The dual-die approach is fundamentally a yield optimization strategy that trades packaging complexity for silicon efficiency.

HBM3e Memory: ~$2,900 (45% of COGS)

Memory is the single largest cost component — and the fastest-growing one. The B200 uses 8 stacks of HBM3e at 24GB each (192GB total), compared to the H100's 5 stacks of HBM3 at 16GB each (80GB total). At estimated HBM3e pricing of ~$350-$370 per stack, the total memory cost reaches approximately $2,900. Track live HBM pricing and supply data.

The cost increase from H100 to B200 is driven by three factors:

1. More stacks (8 vs 5): +60% in stack count
2. Higher capacity per stack (24GB vs 16GB): taller stacks with more DRAM layers
3. HBM3e premium: ~15-20% more expensive per stack than HBM3 due to higher bandwidth interface and tighter manufacturing tolerances

This confirms a structural shift in AI chip economics: memory has permanently overtaken logic as the dominant cost driver. On the H100, HBM represented ~41% of COGS. On the B200, it's ~45%. This trend will intensify as HBM4 arrives with even higher costs per stack.

Advanced Packaging: ~$1,100 (17% of COGS)

The B200 uses TSMC's CoWoS-L (Chip-on-Wafer-on-Substrate, Large) packaging — a more complex and expensive variant than the CoWoS-S used on the H100. CoWoS-L uses an organic interposer with local silicon interconnect (LSI) bridges rather than a single monolithic silicon interposer. This is necessary because the B200's package — two GPU dies plus 8 HBM stacks — exceeds the reticle size limit of a single silicon interposer. Model these packaging cost trade-offs yourself.

At ~$1,100, packaging represents a 47% increase over the H100's ~$750 CoWoS-S cost. The additional expense comes from:

• Larger interposer area and more LSI bridge components
• Higher microbump count for dual-die interconnect
• More complex underfill and thermal management for the 1,000W TDP
• Lower yields on the larger, more complex package assembly

Test, Assembly & Other: ~$1,550 (24% of COGS)

The remaining costs include wafer probe testing, known-good-die (KGD) validation for each of the two logic dies and 8 HBM stacks, final package test, burn-in, and module assembly. The dual-die architecture increases test complexity since both dies and the chip-to-chip interconnect must be validated independently and as a system.

B200 vs H100: Generational Cost Comparison

The generational cost evolution reveals where NVIDIA chose to invest — and where the market forced their hand:

What NVIDIA chose: The dual-die architecture was a strategic design decision to maximize compute density within packaging constraints. It allows NVIDIA to push past the reticle limit without waiting for a process node shrink. The 2.3x performance gain (989 → 2,250 TFLOPS FP16) justifies the complexity.

What the market forced: The memory cost escalation (+115%, from $1,350 to $2,900) is largely external to NVIDIA. HBM pricing is controlled by the memory oligopoly (SK Hynix ~50%, Samsung ~30%, Micron ~20%), and the HBM supply crisis has given these vendors substantial pricing power. NVIDIA must absorb these costs or pass them to customers.

The margin story: Despite nearly doubling COGS, NVIDIA's estimated gross margin only drops from ~88% to ~84%. This is possible because the B200's sell price (~$40,000) increased by 43% while COGS increased by 93%. NVIDIA can sustain this because the B200's performance-per-dollar actually improves: at ~$17.78/TFLOP (FP16) vs H100's ~$28.31/TFLOP, the B200 is a better deal for the buyer despite the higher absolute price. For context on how NVIDIA maintains these margins across the competitive landscape, see our market share analysis.

What This Means for AI Infrastructure Costs

The B200's cost structure has direct implications for cloud GPU pricing and AI training economics:

Cloud GPU-hour pricing: At a 3-year depreciation schedule and typical 60% utilization, the B200's ~$40,000 ASP translates to approximately $2.50-$3.00/GPU-hour in infrastructure cost (before power, networking, and operations). Cloud providers targeting ~30% gross margin would need to charge $4.00-$5.00/GPU-hour — roughly in line with early Blackwell pricing observed from major cloud providers.

Training cost trajectory: A 70B parameter model trained on 256 B200 GPUs for 2 weeks costs approximately $1.2M-$1.5M in compute alone. This represents a ~25-30% cost reduction per TFLOP-hour compared to equivalent H100 training, making the B200 more cost-efficient despite its higher unit price.

The memory cost wildcard: With HBM representing 45% of B200 COGS, any movement in HBM pricing has an outsized impact on the entire accelerator's economics. A 20% increase in HBM3e spot prices would add ~$580 to the B200's manufacturing cost — equivalent to a 9% COGS increase from memory alone. This makes NVIDIA's B200 margins more sensitive to memory market dynamics than any previous generation.

Model B200 economics yourselfOpen the calculator pre-loaded with B200 parameters — TSMC N5, dual-die (~1600mm²), CoWoS, HBM3e (8 stacks), 84% margin

References & Sources

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   Epoch AI. "Estimating the Costs of AI Chip Production". 2025.
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   Raymond James. "Semiconductor Industry Cost Structure Analysis". Q4 2025.
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   TrendForce. "Advanced Packaging and HBM Pricing Quarterly Report". Jan 2026.
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   NVIDIA. "Blackwell Architecture Technical Brief". 2025.