GPU Depreciation Strategies: Optimizing Asset Lifecycles

December 2025 Update: Microsoft CEO: "I didn't want to go get stuck with 4-5 years of depreciation on one generation." Hyperscalers extended server useful life from 3-4 to 6 years—collectively saving ~$18B annual depreciation. NVIDIA releases new architectures every 18-24 months vs 6-year depreciation schedules. CoreWeave betting on inference workloads extending GPU value retention.

Microsoft's Satya Nadella revealed a crucial insight about GPU infrastructure planning: "I didn't want to go get stuck with four or five years of depreciation on one generation."¹ The comment captures the central tension in AI infrastructure finance—accounting for hardware that costs $50,000+ per GPU in a field where NVIDIA releases new architectures every 18-24 months. Getting depreciation strategy right means the difference between competitive infrastructure costs and financial drag that undermines AI economics.

Hyperscalers extended server useful life assumptions from 3-4 years to 6 years, collectively saving an estimated $18 billion in annual depreciation expense.² Whether this accounting treatment reflects economic reality or accounting optimization remains hotly debated. Organizations building AI infrastructure must develop depreciation strategies that balance tax efficiency, financial reporting, and operational reality.

The depreciation landscape

Major cloud providers have converged on extended depreciation schedules despite rapid hardware advancement:

Hyperscaler approaches

Amazon: Extended server depreciation from 3 years to 4 years in 2020, then to 6 years by 2023. AWS justified the change based on operational data showing servers remained productive beyond original assumptions.

Microsoft: Extended from 4 years to 6 years for server and network equipment in cloud infrastructure. SEC filings hedge with 2-6 year equipment lifespan ranges.

Google: Moved to 6-year useful life assumptions alongside other hyperscalers.

Meta: Extended to 5 years rather than 6, reflecting slightly more conservative assumptions.³

CoreWeave: Uses 6-year depreciation cycles, betting heavily on GPU value retention through inference workloads after training relevance declines.⁴

Financial impact

The collective shift from 3-year to 6-year depreciation dramatically reduced reported expenses:

Annual Depreciation = Asset Value / Useful Life

$300B collective CapEx (2024 estimate):
- 3-year depreciation: $100B annual expense
- 6-year depreciation: $50B annual expense
- Savings: $50B annually

Actual 2024 estimates suggest hyperscalers reduced collective depreciation from $39 billion to $21 billion through extended useful life—a 46% reduction in reported depreciation expense.⁵

The value cascade model

Extended depreciation assumptions rest on the "value cascade" theory of GPU utility:⁶

Years 1-2 (Training): GPUs support frontier model training requiring maximum performance. This represents highest-value use demanding latest-generation hardware.

Years 3-4 (Production Inference): As newer GPUs take over training, previous generation moves to high-value real-time inference. Performance remains adequate for serving models trained on the same or earlier hardware.

Years 5-6 (Batch/Analytics): Final lifecycle phase supports batch processing, analytics workloads, and cost-sensitive inference where latency matters less than economics.

CoreWeave CEO Michael Intrator validated this pattern: A100 chips announced in 2020 remain fully booked for inference workloads, and H100s from expired contracts rebooked at 95% of original pricing.⁷

Value cascade requirements

The cascade model depends on several conditions:

1. Inference demand growth: Production AI deployments must grow faster than training capacity, creating sustained demand for inference hardware.

2. Model-hardware compatibility: Models trained on newer GPUs must run efficiently on older hardware for inference.

3. Total cost economics: Operating costs (power, cooling, space) must remain acceptable relative to compute value delivered.

4. Maintenance viability: Hardware must remain reliable and maintainable through extended service life.

Depreciation strategy options

Conservative (3-year)

Approach: Match depreciation to typical training hardware relevance period.

Advantages: - Aligns with observable technology cycles - Maximizes tax shields in early years - Reduces risk of unexpected write-downs - Conservative financial reporting

Disadvantages: - Higher reported depreciation expense - May not reflect actual secondary market value - Creates earnings volatility on resale gains

Best for: Organizations with uncertain AI workload longevity, risk-averse financial reporting, or limited secondary market access.

Extended (6-year)

Approach: Match hyperscaler practice assuming value cascade economics.

Advantages: - Lower annual depreciation expense - Improved reported profitability - Aligns with hyperscaler precedent - Matches Azure/AWS hardware retirement patterns (7-9 years actual service)⁸

Disadvantages: - Risk of impairment if hardware obsoletes faster than expected - May overstate asset values - Creates potential write-down risk on technology shifts

Best for: Organizations with diverse workloads (training + inference), established secondary market relationships, and confidence in long-term AI demand.

Hybrid (accelerated front-loaded)

Approach: Higher depreciation in years 1-2 (50-60% of value), slower depreciation in years 3-6.

Advantages: - Captures primary use value decline - Maintains reasonable book value for secondary market - Balances tax efficiency with conservative reporting - Flexibility for actual disposal timing

Disadvantages: - More complex accounting - May require custom depreciation schedules - Less established precedent

Best for: Organizations wanting balance between aggressive and conservative approaches.

Tax optimization strategies

Section 179 deduction

Qualifying equipment can be fully expensed in the year of purchase rather than depreciated:

2025 limits: Up to $1.22 million deduction with $3.05 million phase-out threshold.

Applicability: GPU servers generally qualify as Section 179 property. Verify classification with tax advisors.

Strategic use: Expense GPU purchases in high-income years to maximize tax benefit.

Bonus depreciation

Current rules: 60% bonus depreciation for 2025 (phasing down from 100% in 2022).

Application: Apply bonus depreciation to remaining cost after Section 179, then standard depreciation to balance.

Example:

$300,000 GPU server
- Section 179: $300,000 (full deduction if within limits)
- Alternative: $180,000 bonus (60%) + standard depreciation on $120,000

International considerations

Transfer pricing: GPU deployments across jurisdictions require careful transfer pricing documentation.

Treaty benefits: Some jurisdictions offer accelerated depreciation or investment credits for technology assets.

Permanent establishment risk: GPU deployments may create taxable presence in foreign jurisdictions.

Refresh cycle optimization

Factors driving refresh decisions

Performance requirements: Training workloads demand latest generation; inference tolerates older hardware longer.

Power efficiency: Newer GPUs typically deliver better performance per watt. At scale, power savings may justify refresh even when performance suffices.

Reliability: Failure rates increase with age. Balance repair costs against replacement economics.

Support availability: Vendor support timelines and spare parts availability constrain maximum service life.

Secondary market timing: Optimal resale occurs before new architecture launches depress previous-generation pricing.

Refresh cycle models

Fixed cycle (3-4 years): Replace hardware on fixed schedule regardless of condition. - Predictable capital planning - Consistent performance levels - May dispose of functional hardware prematurely

Condition-based: Replace when performance or reliability degrades below thresholds. - Maximizes hardware value extraction - Requires robust monitoring - Creates refresh timing uncertainty

Workload migration: Cascade hardware through workload tiers as new equipment arrives. - Training → Production inference → Batch processing - Maximizes asset utilization - Requires diverse workload portfolio

Refresh timing recommendations

Training clusters: Refresh on 2-3 year cycles to maintain competitive capability. Performance-sensitive applications justify frequent updates.

Production inference: Refresh on 4-5 year cycles or when efficiency gains exceed refresh costs. Inference workloads tolerate older hardware well.

Development/test: Cascade production hardware to development environments. Extend development hardware lifecycle to 5-6+ years.

Financial planning integration

CapEx budgeting

Multi-year planning: Model GPU refresh cycles 3-5 years forward, accounting for: - Technology roadmaps (NVIDIA Blackwell → Vera Rubin → future) - Workload growth projections - Depreciation schedule transitions - Secondary market assumptions

Reserve planning: Establish technology refresh reserves sized to smooth annual CapEx volatility.

Working capital impact

Inventory considerations: GPU shortages have created strategic inventory decisions. Balance holding costs against supply risk.

Disposal timing: Coordinate secondary market sales with fiscal year boundaries for tax optimization.

Financial reporting alignment

Impairment testing: Regular assessment of GPU carrying values against recoverable amounts. Technology shifts may trigger impairment charges.

Disclosure requirements: Significant depreciation policy decisions may require disclosure in financial statement notes.

Operational lifecycle management

Asset tracking requirements

Maintain comprehensive records supporting depreciation and disposal:

Physical tracking: - Serial numbers and location - Installation and commissioning dates - Configuration and specifications - Maintenance history

Financial tracking: - Original cost and vendor - Depreciation method and schedule - Accumulated depreciation - Estimated residual value

Utilization tracking: - Operating hours and utilization rates - Workload types supported - Performance benchmarks over time - Power consumption trends

End-of-life planning

Disposal options:

1. Secondary market sale: Maximize value recovery through ITAD partners or direct sales
2. Trade-in: Vendor programs may offer credits against new purchases
3. Internal redeployment: Cascade to lower-tier workloads or other business units
4. Donation: Tax benefits for qualifying charitable donations
5. Recycling: Environmentally responsible disposal when no economic value remains

Data destruction: Ensure proper data sanitization before any disposal method. Document destruction for compliance.

Organizations managing GPU asset lifecycles can leverage Introl's global deployment expertise for hardware procurement, refresh planning, and disposition across 257 locations worldwide.

Strategic recommendations

For CFOs

1. Benchmark against peers: Understand hyperscaler depreciation practices and rationale before setting internal policy.

2. Model scenarios: Quantify earnings and tax impact of different depreciation schedules before committing.

3. Document assumptions: Create clear record of useful life assumptions and supporting analysis for audit defense.

4. Monitor technology: Stay informed on AI hardware advancement to anticipate potential impairment triggers.

For infrastructure leaders

1. Track utilization data: Build evidence base supporting extended useful life assumptions through operational metrics.

2. Develop cascade playbooks: Create processes for migrating hardware between workload tiers as it ages.

3. Establish disposal channels: Build relationships with secondary market partners before hardware reaches end of primary use.

4. Plan refresh timing: Coordinate refresh cycles with technology releases and fiscal calendars.

For tax advisors

1. Optimize deduction timing: Coordinate Section 179 and bonus depreciation across portfolio for maximum benefit.

2. International planning: Structure cross-border GPU deployments for tax efficiency while managing permanent establishment risk.

3. Transfer pricing documentation: Ensure GPU cost allocations withstand scrutiny in multi-jurisdictional operations.

The depreciation imperative

GPU depreciation strategy ranks among the most consequential financial decisions for AI-intensive organizations. The hyperscaler shift to 6-year assumptions signals confidence in the value cascade model, but individual organizations must evaluate whether their workload mix and secondary market access support similar assumptions.

Microsoft's approach—deliberately spacing purchases to avoid generational lock-in—suggests sophisticated operators view depreciation as active management rather than passive accounting. The 18-24 month NVIDIA release cadence creates natural decision points where refresh timing intersects depreciation strategy.

For most enterprises, hybrid approaches balancing tax optimization against conservative reporting provide the most flexibility. Aggressive 6-year assumptions make sense only when organizations have demonstrated value cascade capability and established secondary market relationships. Conservative 3-year schedules make sense when AI workload longevity remains uncertain or training dominates inference in workload mix.

The trillion-dollar question of GPU depreciation ultimately resolves through operational data rather than accounting theory. Organizations that track utilization, monitor reliability, and actively manage hardware transitions will develop evidence-based depreciation policies. Those relying solely on external benchmarks or aggressive assumptions may face uncomfortable impairment conversations when technology shifts outpace useful life assumptions.

References

1. CNBC. "The question everyone in AI is asking: How long before a GPU depreciates?" November 14, 2025. https://www.cnbc.com/2025/11/14/ai-gpu-depreciation-coreweave-nvidia-michael-burry.html

2. SiliconANGLE. "Resetting GPU depreciation: Why AI factories bend, but don't break, useful life assumptions." November 22, 2025. https://siliconangle.com/2025/11/22/resetting-gpu-depreciation-ai-factories-bend-dont-break-useful-life-assumptions/

3. MBI Deep Dives. "Big Tech's Deteriorating Earnings Quality." 2025. https://www.mbi-deepdives.com/big-tech-earnings-quality/

4. theCUBE Research. "298 | Breaking Analysis | Resetting GPU Depreciation — Why AI Factories Bend, But Don't Break, Useful Life Assumptions." 2025. https://thecuberesearch.com/298-breaking-analysis-resetting-gpu-depreciation-why-ai-factories-bend-but-dont-break-useful-life-assumptions/

5. SiliconANGLE. "Resetting GPU depreciation."

6. ———. "Resetting GPU depreciation."

7. CNBC. "The question everyone in AI is asking."

8. Applied Conjectures. "How Long Do GPUs Last Anyway? A Look Into Hyperscalers' Depreciation Policies, GPUaaS Unit Economics." 2025. https://appliedconjectures.substack.com/p/how-long-do-gpus-last-anyway-a-look

Key takeaways

For CFOs: - Hyperscalers extended server depreciation from 3-4 years to 6 years, saving ~$18B annually - 2024 estimates: collective depreciation reduced from $39B to $21B (46% reduction) - Model scenarios before committing—earnings and tax impact varies significantly

For infrastructure leaders: - Value cascade model: Years 1-2 (training) → Years 3-4 (inference) → Years 5-6 (batch) - CoreWeave: H100s from expired contracts rebooked at 95% of original pricing - Azure/AWS actual hardware retirement: 7-9 years service life

For tax advisors: - Section 179 (2025): Up to $1.22M deduction with $3.05M phase-out threshold - Bonus depreciation: 60% for 2025 (phasing down from 100% in 2022) - Transfer pricing and permanent establishment risk for cross-border GPU deployments

For refresh planning: - Training clusters: 2-3 year refresh cycles for competitive capability - Production inference: 4-5 year cycles or when efficiency gains exceed refresh costs - Optimal secondary market resale timing: before new architecture launches depress pricing