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AI Silicon Break-Even Monitor

Estimate when custom AI silicon repays design and software costs—and what that could mean for GPU spend and supplier economics.

Public-equity scenario engine

When does custom silicon threaten GPU economics?

Translate NRE, software, ramp risk, workload fit and lifecycle TCO into break-even volume—then separate hyperscaler savings from possible supplier content.

Program risk

Chip NRE$MSoftware porting$MRamp cost$M

DelaymonthsDelay cost$M/mo

Failure probability%Time to productionyears

Workload and fleet

Inference workload%

Inference throughput× GPUTraining throughput× GPU

Utilization%Useful lifeyearsAnnual custom unitsDeployment horizonyearsDiscount rate%

Supplier content low%Supplier content high%

Per-accelerator TCO

GPUCustom

Hardware$Hardware$Networking$Networking$HBM content$HBM content$PowerkWPowerkW

Facility PUE

Electricity price$/kWh

Scenario clears the hurdle

Fixed program cost includes direct delay cost plus failure-probability-weighted sunk program and delay cost.

1.19 GPU-equivalent / custom unit

Break-even volume

60,476

Risk-adjusted fixed cost ÷ lifecycle savings per unit

Payback after launch

0.6 years

100,000 units/year over 4 years

Program NPV

$6,076.4M

10% discount rate; 4 annual cohorts after launch

Program ROIC

561.4%

All deployment cohorts' undiscounted lifecycle net savings ÷ fixed cost

Lifecycle saving / unit

$26,908

$46,031 GPU-equivalent vs $27,984 custom

Displaced GPU spend

$21,226.5M

All cohorts' avoided GPU-equivalent acquisition spend; not vendor revenue guidance

Supplier content range

$4,860M–$7,560M

All cohorts; user-set share of custom hardware, networking and HBM spend

Hyperscaler net saving

$9,135.8M

$1,627.3M fixed risk-adjusted program cost

Scenario, not forecast. Throughput equivalence, HBM content, yield/ramp risk and supplier share are editable assumptions. “Displaced spend” is the modeled hardware equivalent; it does not predict NVIDIA, Broadcom, Marvell, foundry, memory, packaging or hyperscaler reported revenue.

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Generic cluster TCO does not answer the public-equity question: at what workload volume can custom silicon overcome NRE, software migration, ramp delay, and failure risk? This model estimates break-even units, payback, hyperscaler savings, displaced GPU spend, and custom-silicon opportunity across explicit scenario ranges.

How this ai silicon break-even monitor works

The model compares risk-adjusted up-front custom-silicon design, software, and ramp costs with lifecycle savings per accelerator. It then estimates the volume and time required to break even and translates the scenario into hyperscaler savings and public-company exposure ranges.

Formula

Break-even units = (NRE + software + ramp + expected delay/failure cost) ÷ lifecycle savings per deployed accelerator

NRE, yields, packaging, transfer pricing, software cost, and performance are rarely fully public. The model is most useful for locating the break-even boundary, not declaring one architecture universally cheaper.

Primary specifications

Before you use the result

Assumptions

• Custom and merchant accelerators deliver comparable useful workload output after utilization adjustments.
• Failure probability and production delay are explicit expected-cost inputs, not hidden inside a discount rate.
• Displaced GPU spend and supplier opportunity are exposure scenarios, not company revenue forecasts.

Quick start

1. Separate the workload into training and inference and set realistic utilization.
2. Enter merchant-GPU and custom-silicon hardware, power, networking, HBM, and useful-life assumptions.
3. Stress NRE, software migration, ramp delay, failure probability, and deployment volume.

Frequently asked questions

What determines custom-silicon break-even volume?

Up-front design, software, ramp, and delay costs are divided by risk-adjusted lifecycle savings per deployed accelerator. Lower utilization, short useful life, or ramp failure pushes break-even higher.

Why separate training and inference workloads?

Workload stability, software requirements, utilization, and performance needs differ. A fixed high-volume inference workload may justify specialization sooner than changing frontier-training workloads.

Does displaced GPU spend equal lost GPU-company revenue?

No. It is a scenario exposure before supply constraints, pricing response, workload growth, networking sales, cloud demand, and other offsets.

Can public data prove the exact ASIC cost?

Usually not. NRE, yields, packaging, software expense, and transfer pricing are rarely disclosed in full. The model is designed to show the sensitivity rather than hide uncertainty behind one point estimate.

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