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GPU Depreciation Period: 3 Years vs. 5 Years — Quantifying the Impact on TCO

算力中心TCO数据中心算力建设

The accounting choice of GPU server depreciation period (3 or 5 years) does not alter the actual service life of the equipment, but it significantly affects the annual income statement, asset turnover, and financial headroom for refresh cycles of a compute center. Based on measured data from Mingxin FX100 in a 480B model inference scenario (measured, report R2), this article quantifies the differential impact of the two depreciation strategies on TCO. The conclusion: in the current compute market with a technology refresh cycle of approximately 18–24 months, a 3-year depreciation period better matches the actual economic life of GPUs, but it must be paired with a clear residual value management and refresh budget mechanism.

How Depreciation Period Enters TCO Calculations

The TCO (Total Cost of Ownership) of a compute center typically includes procurement cost, power and cooling, data center rent, operations staffing, network bandwidth, and software licenses. Depreciation itself does not generate cash outflow, but it influences TCO decisions through two channels:

First, depreciation expense is included in annual operating costs, affecting project IRR and payback period calculations. Second, the depreciation period determines when the book value of assets reaches zero, which in turn influences equipment refresh decisions—if the book value remains high, management tends to extend usage, yet GPU compute power often lags two architecture generations behind after 2–3 years.

Take a server configured with 8 GPUs as an example. Assume the total procurement cost is RMB 1.2 million (including GPUs, CPUs, memory, storage, and networking). With straight-line depreciation over 3 years, the annual expense is RMB 400,000; over 5 years, it is RMB 240,000. On the surface, 5-year depreciation reduces annual costs by RMB 160,000 and makes the income statement look better, but this choice implies a premise: the equipment can still generate compute revenue in years 4–5 comparable to years 1–2.

Quantitative Comparison: TCO Differences Between 3-Year and 5-Year Depreciation

We model a typical inference cluster (32 servers, 8 GPUs each, 256 GPUs total) with a procurement cost of approximately RMB 38.4 million, comparing the 5-year cumulative TCO under the two depreciation strategies.

Scenario A: 3-year depreciation, 50% equipment refresh in year 4

  • Years 1–3: annual depreciation of RMB 12.8 million. In year 4, dispose of 16 old servers (residual value at 10%, recovering approximately RMB 1.92 million), while procuring 16 next-generation servers (assuming unit price unchanged, 50% of RMB 38.4 million, i.e., RMB 19.2 million).
  • Years 4–5: new equipment depreciated over 3 years, annual depreciation of RMB 6.4 million; the remaining 16 old servers continue depreciating through the end of year 5.
  • Cumulative depreciation over 5 years: 12.8×3 + 6.4×2 + 6.4×1 (new equipment in years 4–5) = RMB 57.6 million.

Scenario B: 5-year depreciation, full refresh at end of year 5

  • Years 1–5: annual depreciation of RMB 7.68 million, cumulative RMB 38.4 million.
  • At the end of year 5, dispose of all equipment, residual value at 5% (lower residual rate due to longer usage), recovering approximately RMB 1.92 million.
  • Cumulative depreciation over 5 years: RMB 38.4 million.

From a purely accounting perspective, Scenario B has lower cumulative depreciation (RMB 38.4 million vs. 57.6 million), but this comparison ignores two key variables: the rising unit compute cost due to performance degradation, and the efficiency loss when old equipment handles high-load tasks in years 4–5.

Mingxin measured data, report R2, shows that in a 480B model long-context inference scenario, FX100 achieves 8.6–20× speedup over the baseline without external memory recomputation, with TTFT p50 dropping from 149.5s to 11.85s (at concurrency level 16). This implies: if old equipment in years 4–5 is forced to recompute due to IO bottlenecks or insufficient memory, its effective throughput may drop to below 1/10 of new equipment. In this case, although 5-year depreciation shows lower book costs, the unit token generation cost actually rises.

We introduce a conservative assumption: in years 4–5, old equipment experiences a 40% inference throughput decline due to outdated architecture (in reality, it may be higher). If the cluster's annual token output remains unchanged, an additional 67% compute capacity is needed to compensate. If this gap is filled via cloud services, at a market price of approximately RMB 2 per million tokens, the annual incremental cost would be RMB 5–8 million (depending on load density). This incremental cost far exceeds the annual difference between 3-year and 5-year depreciation (RMB 12.8 million vs. 7.68 million, a gap of RMB 5.12 million).

Matching Depreciation Strategy with Compute Refresh Cadence

GPU architecture refresh cycles are approximately 2 years (e.g., NVIDIA A100→H100→H200, AMD MI300→MI308), and domestic compute chip iterations are also accelerating. The Mingxin FX product line, from FX100 (PCIe 3.0) to FX300 (PCIe 5.0), has increased single-interface bandwidth from 100Gb to 400Gb, IOPS from 16M to 60M, with corresponding KV Cache acceleration improving from +29% to higher levels (measured, reports R2/R3). This means a GPU server procured in year 1 may, by year 3, have a storage and IO subsystem that can no longer match the throughput demands of next-generation GPUs.

From a compute center operations perspective, the advantages of 3-year depreciation are:

  • Book value closer to market value: The second-hand GPU server market typically prices based on a 3-year life cycle, with a residual value rate of approximately 10–15% at the end of year 3, which is broadly consistent with the book value after 3-year depreciation (if residual value is set at 10%), facilitating asset disposal and reinvestment decisions.
  • Financial pressure front-loaded, driving compute efficiency: 3-year depreciation means higher annual depreciation expense, which pushes operators to more aggressively optimize utilization and adopt cost-reduction technologies such as tiered KV Cache acceleration, rather than relying on extended equipment life to amortize costs.
  • Alignment with customer contract cycles: When a compute center provides compute services externally, contract terms are typically 1–3 years. With 5-year depreciation, years 4–5 still incur depreciation but may lack corresponding contract revenue, resulting in "idle depreciation."

The 5-year depreciation period has limited applicability: when a compute center primarily handles long-term fixed workloads (e.g., scientific computing, traditional HPC) with slowly evolving workload models, the impact of performance degradation is minimal, and 5-year depreciation can smooth profits. However, for compute centers serving large model inference, where workload models iterate frequently (e.g., from 14B to 480B), old equipment often becomes a bottleneck within 2 years.

Conclusion

The choice of GPU depreciation period is fundamentally a trade-off between technology refresh risk and financial smoothing needs. Based on Mingxin's measured data in the 480B model inference scenario (measured, report R2), current large model inference workloads are highly sensitive to compute architecture. A 3-year depreciation period better aligns with the actual economic life of GPUs and encourages operators to plan equipment refresh and compute efficiency optimization earlier. Mingxin Technology provides storage acceleration and full-chain compute center services, supporting approximately 10-week gated joint testing (from G1 arrival acceptance to G4 stability verification), enabling quantitative evaluation of depreciation strategy impact on TCO at the equipment selection stage. We welcome contact for joint testing.

Key Q&A

Q: How much does the GPU server depreciation period (3 vs. 5 years) impact TCO? A: Using a 32-server, 8-GPU cluster (approximately RMB 38.4 million procurement cost) as an example, 3-year depreciation results in cumulative depreciation of approximately RMB 57.6 million over 5 years, while 5-year depreciation results in approximately RMB 38.4 million—a difference of RMB 19.2 million. However, 5-year depreciation carries the risk of performance degradation in years 4–5; if inference throughput drops by 40%, the incremental cost of supplementary compute may exceed the depreciation difference.

Q: In which scenarios is 3-year depreciation more advantageous? A: Compute centers serving large model inference are better suited to 3-year depreciation. Mingxin measured data, report R2, shows that in a 480B model long-context scenario, FX100 achieves 8.6–20× speedup over the baseline without external memory recomputation, demonstrating that storage and IO architecture significantly impact inference efficiency. Old equipment may become a throughput bottleneck due to IO constraints within 2–3 years.

Q: When is 5-year depreciation reasonable? A: When workload models are relatively fixed (e.g., traditional HPC, scientific computing) and performance degradation has limited impact on output, 5-year depreciation can smooth annual profits. However, for frequently iterating large model inference workloads, 5-year depreciation may cause unit token costs to rise rather than fall in years 4–5, and is not recommended.

Generated by Mingxin's content engine with automated QC; headline numbers cite signed test reports (see the evidence library). Translated from the Chinese original. Questions or corrections: contact us.