Secondary GPU Markets: Buying and Selling Used AI Hardware

Updated December 11, 2025

December 2025 Update: CoreWeave H100s from 2022 contract expirations rebooking at 95% of original pricing. Hyperscalers extended depreciation to 6 years, saving ~$18B annually on $300B+ CapEx. Value cascade: Years 1-2 for frontier training, 3-4 for inference, 5-6 for batch workloads. Jensen Huang joking "when Blackwell ships, you couldn't give Hoppers away"—but inference demand keeps previous-gen hardware valuable.

CoreWeave's H100 GPUs from 2022 contract expirations immediately rebooked at 95% of original pricing.¹ The data point reveals something counterintuitive: AI accelerators retain substantial value even as NVIDIA releases newer generations. A mature secondary market has emerged for enterprise GPU hardware, creating opportunities for organizations to acquire compute capacity at significant discounts while enabling sellers to recover capital from infrastructure upgrades.

The trillion-dollar question hovering over AI infrastructure investments—how fast do GPUs actually depreciate—shapes procurement strategy, financial planning, and infrastructure lifecycle decisions. Understanding secondary market dynamics helps enterprises optimize GPU acquisitions, time hardware refresh cycles, and maximize returns on AI infrastructure investments.

The depreciation debate

Hyperscalers and GPU cloud providers disagree fundamentally about how long AI accelerators retain economic value:

Extended depreciation (6 years): Amazon, Microsoft, and Google all extended server useful life assumptions from 3-4 years to 6 years by 2023.² CoreWeave uses six-year depreciation cycles. The accounting treatment reduces annual depreciation expense significantly—estimates suggest the change saved hyperscalers $18 billion in 2024 alone across their collective $300+ billion CapEx.³

Aggressive depreciation (2-3 years): Critics argue AI hardware advances too quickly for six-year assumptions. NVIDIA releases new architectures every 2 years (A100 in 2020, H100 in 2022, B200 in 2024), with each generation delivering 2-3x performance improvements. Jensen Huang himself joked that "when Blackwell starts shipping in volume, you couldn't give Hoppers away."⁴

The value cascade model: A more nuanced view recognizes that GPUs serve different workloads as they age:⁵ - Years 1-2: Frontier model training requiring maximum performance - Years 3-4: High-value real-time inference where previous-gen hardware suffices - Years 5-6: Batch inference and analytics workloads

The cascade framework explains CoreWeave's experience: A100 GPUs remain fully booked because inference workloads don't require cutting-edge silicon. Organizations training frontier models need B200s; organizations serving production inference often don't.

Current market pricing

Secondary GPU prices reflect the interplay between new hardware supply, inference demand, and generational transitions:

H100 pricing (2025)

New/retail: - Base price: $25,000-$28,000 - Server-integrated configurations: $35,000-$40,000+ - Supply-constrained periods: 40-60% premiums⁶

Secondary market: - Lightly used (1-2 years): 70-85% of new pricing - Moderate use (2-3 years): 50-70% of new pricing - Grey market/eBay: 20-40% discounts but warranty/support risks

Cloud rental context: - Hyperscaler on-demand: $3-4/hour (down 44% from 2024) - Budget providers: $1.80-2.50/hour - 300+ new providers entered market in 2025⁷

A100 pricing dynamics

A100 prices fell more sharply than H100 in mid-2025 corrections, reflecting the GPU's position in a broader, more liquid secondary market.⁸ The A100 serves diverse workloads including inference, mid-tier training, and edge deployments—exactly the applications where used hardware finds second life.

Current A100 secondary pricing: - 40GB variants: $8,000-12,000 (from $15,000+ new) - 80GB variants: $12,000-18,000 (from $25,000+ new) - Server bundles (8x A100): Significant volume discounts

B200 transition impact

NVIDIA's B200 general availability (expected Q1 2026) will pressure H100 secondary values. Historical patterns suggest 10-20% price reductions for previous-generation hardware as enterprises upgrade.⁹ Organizations planning H100 purchases should factor transition timing into acquisition decisions.

Acquisition strategies

Buying used GPUs

Certified refurbished programs: Major vendors (Dell, HPE, Supermicro) offer certified refurbished GPU servers with warranties. Pricing typically runs 30-40% below new with 1-2 year warranty coverage. The approach balances cost savings against support assurance.

Hyperscaler surplus: When cloud providers refresh infrastructure, significant GPU inventory enters the market. Timing purchases around known upgrade cycles (AWS typically refreshes 3-4 years post-launch) can yield favorable pricing. Enterprise remarketing channels handle most volume.

Broker networks: Specialized IT asset disposition (ITAD) companies aggregate GPU inventory from multiple sellers. Reputable brokers verify hardware provenance, test functionality, and provide limited warranties. Expect 20-30% savings versus certified refurbished.

Direct enterprise purchases: Organizations upgrading infrastructure sometimes sell directly to known buyers, particularly within industry networks. Direct transactions avoid broker margins but require due diligence on hardware condition and provenance.

Grey market risks: eBay, Alibaba, and similar platforms offer the deepest discounts but carry significant risks: - Limited or no warranty coverage - Potential firmware restrictions or region locks - Counterfeit or misrepresented hardware - No support for driver/firmware updates

Grey market purchases suit organizations with hardware expertise and risk tolerance for non-critical workloads.

Due diligence checklist

Before purchasing secondary GPUs:

1. Verify provenance: Request original purchase documentation, especially for high-value H100s. Legitimate sellers provide invoices showing authorized distribution channels.

2. Check warranty status: Some manufacturer warranties transfer to subsequent owners; others don't. Verify warranty coverage before assuming transferability.

3. Test functionality: Stress-test GPUs under representative workloads before finalizing purchase. Memory errors, thermal throttling, and performance degradation may not appear in basic validation.

4. Confirm firmware/driver compatibility: Ensure GPUs can receive current firmware updates. Some grey market units have restricted firmware access.

5. Assess physical condition: Inspect for thermal paste degradation, fan wear, and physical damage. Data center GPUs typically fare better than consumer units due to controlled environments.

Selling strategies

Timing hardware refresh

Optimal sell timing balances several factors:

Performance requirements: Sell when hardware no longer meets primary workload needs, not when it becomes completely obsolete. GPUs supporting yesterday's training requirements still serve tomorrow's inference workloads.

Market conditions: New architecture launches depress previous-gen values temporarily. Selling 6-12 months before expected successor launch maximizes recovery. Selling immediately after launch minimizes recovery.

Contract obligations: Cloud commitments or lease terms may constrain timing. Factor these constraints into refresh planning.

Microsoft CEO Satya Nadella described spacing AI chip purchases to avoid "getting stuck with four or five years of depreciation on one generation."¹⁰ The approach trades volume discounts against obsolescence risk.

Recovery expectations

Enterprise GPU resale typically recovers 60-80% of original purchase price depending on:¹¹ - Age and condition - Generation currency (how many successors released) - Market supply/demand balance - Completeness of package (GPU-only vs server)

Bundling strategy: Selling complete GPU servers as turnkey offerings often yields better returns than parting out individual components. Buyers value simplified deployment; sellers reduce remarketing complexity.

Sales channels

ITAD partners: IT asset disposition companies handle logistics, data destruction certification, and buyer sourcing. They typically take 15-25% commission but reduce seller burden significantly.

Direct enterprise sales: Selling to known buyers (perhaps through industry associations or professional networks) avoids broker fees but requires seller effort in finding buyers and negotiating terms.

Auction platforms: Technology-focused auctions aggregate buyer demand but create price uncertainty. Suitable for inventory liquidation when speed matters more than price optimization.

Trade-in programs: Some vendors offer trade-in credits against new hardware purchases. The approach simplifies transactions but typically recovers less than open-market sales.

Financial planning considerations

Depreciation schedule optimization

Organizations should align accounting depreciation with actual hardware lifecycle:

Conservative approach (3 years): Matches typical primary use period. Higher annual depreciation expense but no surprise write-downs when hardware becomes obsolete.

Extended approach (6 years): Matches hyperscaler precedent. Lower annual expense but requires confidence in secondary market value retention.

Hybrid approach: Accelerated depreciation in years 1-2 (50-60% of value), slower depreciation in years 3-6. Reflects value cascade economics where primary use captures most value.

Total cost of ownership adjustments

Factor residual value into TCO calculations:

Effective Annual Cost = (Purchase Price - Expected Resale) / Years of Use
                      + Annual Operating Costs (power, cooling, support)

For example, an H100 server: - Purchase: $300,000 - Expected resale (4 years): $90,000 (30% residual) - Operating cost: $40,000/year - Effective annual cost: ($300,000 - $90,000) / 4 + $40,000 = $92,500

Versus assuming zero residual: - Effective annual cost: $300,000 / 4 + $40,000 = $115,000

The 20% TCO difference significantly impacts build-vs-rent decisions.

Tax implications

Consult tax advisors on: - Depreciation method selection (straight-line vs accelerated) - Section 179 deductions for qualifying equipment - Sale timing for capital gains optimization - Like-kind exchange possibilities

Market structure and participants

Seller categories

Hyperscalers: AWS, Azure, and GCP periodically retire GPU capacity, creating substantial secondary supply. Typically routed through certified remarketing partners rather than open market.

GPU cloud providers: CoreWeave, Lambda Labs, and similar companies refresh infrastructure on faster cycles than hyperscalers. Contract expirations create predictable supply.

Enterprises: Organizations completing AI projects or pivoting strategies sell surplus capacity. Quality varies widely based on use patterns and maintenance practices.

Failed startups: AI company failures release GPU inventory, sometimes at distressed prices. Due diligence critical given uncertain provenance and maintenance history.

Buyer categories

Cost-conscious enterprises: Organizations with inference workloads or research applications where previous-gen hardware suffices.

Emerging markets: Organizations in price-sensitive regions acquiring infrastructure at accessible price points.

AI startups: Early-stage companies bootstrapping compute capacity before securing funding for new hardware.

Research institutions: Academic and non-profit organizations with limited budgets seeking capable hardware.

2025 market dynamics

Several factors shape current secondary market conditions:

Tariff impacts: U.S. trade policies enacted in 2025 increased GPU component costs 20-40%, affecting both new and grey market pricing.¹² Organizations importing secondary hardware face similar tariff exposure.

Supply normalization: H100 supply constraints eased significantly through 2025 as NVIDIA production scaled. Improved new supply reduces secondary market premium for immediate availability.

Inference demand growth: Production AI deployments require massive inference capacity. Previous-gen hardware perfectly serves these workloads, sustaining secondary demand even as training requirements push toward newer architectures.

B200 anticipation: Expected B200 general availability in Q1 2026 creates uncertainty about H100 value retention. Some buyers delay purchases awaiting price corrections; some sellers accelerate disposition.

Strategic recommendations

For buyers

1. Match hardware to workload: Don't overpay for training-class hardware when inference-class suffices. A100s serve many inference workloads capably at substantial discounts to H100s.

2. Time purchases strategically: Buy 3-6 months after new architecture launches when price corrections stabilize. Avoid buying immediately before known launches.

3. Build relationships: Develop connections with ITAD partners and enterprise sellers. Relationship buyers often receive first notice of attractive inventory.

4. Budget for validation: Allocate resources for hardware testing before full deployment. Secondary hardware requires more validation than new purchases.

For sellers

1. Plan refresh cycles proactively: Model hardware depreciation scenarios and monitor secondary market pricing. Don't wait until hardware is obsolete to explore remarketing.

2. Maintain documentation: Preserve purchase records, maintenance logs, and configuration documentation. Complete records command premium pricing.

3. Consider timing: Sell before successor launches, not after. The 6-12 month window before expected new architecture launch typically maximizes recovery.

4. Evaluate trade-in programs: Compare vendor trade-in offers against open market pricing. Convenience may justify modest premium surrender.

Organizations navigating secondary GPU markets can leverage Introl's hardware expertise for procurement strategy and lifecycle planning across global deployments.

The maturing market

Secondary GPU markets have evolved from informal broker networks into structured marketplaces with predictable pricing, established participants, and professional practices. The maturation reflects AI infrastructure's transition from experimental to essential—organizations now plan GPU lifecycles with the same rigor applied to traditional data center hardware.

CoreWeave's experience—A100s fully booked, H100s rebooked at 95% of original pricing—suggests GPU depreciation fears may be overstated. Hardware that once trained frontier models finds productive second life serving inference workloads that don't require cutting-edge performance. The value cascade extends useful life well beyond aggressive depreciation assumptions.

For enterprises, secondary markets create optionality: buy used hardware at discounts, sell aging infrastructure to recover capital, or hold equipment knowing inference demand sustains value. Understanding market dynamics transforms GPU procurement from binary build-vs-rent decisions into sophisticated portfolio management where hardware assets flow to their highest-value applications across the industry ecosystem.

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. 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/

4. Tom's Hardware. "GPU depreciation could be the next big crisis coming for AI hyperscalers." November 2025. https://www.tomshardware.com/tech-industry/gpu-depreciation-could-be-the-next-big-crisis-coming-for-ai-hyperscalers-after-spending-billions-on-buildouts-next-gen-upgrades-may-amplify-cashflow-quirks

5. SiliconANGLE. "Resetting GPU depreciation."

6. Jarvislabs.ai. "NVIDIA H100 Price Guide 2025: Detailed Costs, Comparisons & Expert Insights." 2025. https://docs.jarvislabs.ai/blog/h100-price

7. Accio. "h100 gpu trends 2025: Cloud Prices Drop." 2025. https://www.accio.com/business/h100-gpu-trends

8. Silicon Data. "A100 vs H100: When GPU Prices Break Out of Sync." October 2025. https://www.silicondata.com/blog/gpu-market-dynamics-a100-h100

9. CyFuture Cloud. "How Does the Price of the NVIDIA H100 Change Over Time?" 2025. https://cyfuture.cloud/kb/gpu/how-does-the-price-of-the-nvidia-h100-change-over-time

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

11. Data Center Knowledge. "GPU Repurposing Strategies: From Sunk Cost to Cash Flow." 2025. https://www.datacenterknowledge.com/servers/5-ways-to-repurpose-data-center-gpu-hardware

12. Research and Markets. "Data Center GPU Market - Global Forecast 2025-2030." 2025. https://www.researchandmarkets.com/report/data-center-gpu

Key takeaways

For finance teams: - H100 secondary pricing: lightly used (1-2 yrs) 70-85% of new; moderate use (2-3 yrs) 50-70%; enterprise resale recovers 60-80% of original - Extended depreciation (6 yrs) saved hyperscalers $18B in 2024; aggressive (2-3 yrs) matches NVIDIA's 2-year architecture cycle - TCO with 30% residual value: $92,500/year effective cost vs $115,000 assuming zero residual (20% difference)

For procurement teams: - Certified refurbished 30-40% below new with 1-2 year warranty; brokers 20-30% savings; grey market 20-40% off but warranty/counterfeit risks - A100 40GB: $8-12K secondary (vs $15K+ new); A100 80GB: $12-18K (vs $25K+ new) - Due diligence: verify provenance, check transferable warranty, stress-test under load, confirm firmware update access

For strategic planning: - Value cascade model: Years 1-2 frontier training, Years 3-4 inference, Years 5-6 batch/analytics—explains A100s remaining fully booked - Optimal sell timing: 6-12 months before successor launch; selling immediately after launch minimizes recovery - CoreWeave H100s rebook at 95% original pricing; B200 GA expected Q1 2026 will pressure H100 values

For operations teams: - 300+ new GPU cloud providers entered market in 2025; H100 on-demand pricing dropped 44% from 2024 - Hyperscaler surplus hits market when AWS/Azure/GCP refresh (3-4 years post-launch); routed through certified remarketing partners - 2025 tariffs increased GPU component costs 20-40%; affects both new and grey market pricing

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