Utilization is King: Profitability Comparison of Compute Centers at 30% vs. 60% Utilization
The data center industry faces a structural contradiction in 2026: the growth rate of compute supply far exceeds the matching speed of effective demand. According to TrendForce, the average utilization rate of global compute centers has long hovered in the 30%-40% range, while design targets are typically above 70%. This means a significant portion of capital expenditure—from GPU clusters to power infrastructure—remains idle throughout its lifecycle.
When utilization climbs from 30% to 60%, the profit-loss model undergoes a qualitative change. This article uses a typical compute center as a hypothetical scenario to break down this difference from three dimensions: TCO, revenue, and operations.
30% Utilization: A Typical Portrait of Hidden Losses
Assume a medium-sized compute center with a total investment of approximately 500 million yuan, including 5,000 standard racks, supporting liquid cooling and power systems, with GPU clusters primarily based on 8-card MI308X nodes. Based on an industry average electricity cost of 0.6 yuan/kWh and a depreciation period of 5 years, the annualized TCO is about 120 million yuan.
At 30% utilization, effective output covers only about 35% of fixed costs. Taking AI inference as an example, a single node produces approximately 1200 tok/s per hour. At a market average price of 0.15 yuan per thousand tokens, monthly revenue is about 4 million yuan. After deducting variable costs such as electricity, operations, and networking (about 40% of revenue), the monthly net loss is approximately 2 million yuan.
A more hidden cost comes from "cold starts" and "fragmentation." At 30% utilization, many nodes are idle or under low load, but the power and cooling systems still need to maintain baseline power consumption (about 40% of full load). According to IDC data, this ineffective power consumption accounts for 15%-20% of a data center's total electricity bill.
60% Utilization: The Breakeven Tipping Point
When utilization rises to 60%, the scenario fundamentally changes. Assuming load consolidation and KV cache optimization, a single node's effective output increases to 4800 tok/s, generating monthly revenue of 16 million yuan. Variable costs drop to 30% of revenue, resulting in a monthly net profit of about 5 million yuan.
The key point: the benefits of increased utilization are not linear. When load increases from 30% to 60%, system-level efficiency (including memory bandwidth utilization, network throughput, and power efficiency) often jumps from 50% to over 80%. Taking Mingxin's FX100 KV cache acceleration as an example, in measured, report R2, for a 480B model under TP8 configuration, TTFT p50 decreased from 10.17-35.73s to 7.53-26.35s, a reduction of 26%-32% [source: measured, report R2]. This means the same hardware can handle higher concurrency without increasing response latency.
A compute center with 60% utilization can achieve an annualized ROI of 8%-12%, while at 30% utilization, it is negative 5% to negative 10%. This difference, under a 500 million yuan investment scale, corresponds to an annual profit-loss gap of approximately 80 million yuan.
From Utilization to TCO: An Overlooked Hidden Lever
The impact of utilization on TCO is not only reflected in the revenue side but also in asset depreciation and operational efficiency.
Depreciation Acceleration Effect: The depreciation cycle for GPU clusters is typically 3-5 years. If utilization is only 30%, it means 70% of the compute assets are underutilized during their lifecycle, effectively magnifying the cost per unit of compute by 2.3 times. Taking an 8-card MI308X node as an example (about 300,000 yuan per node), the idle cost per node over a 5-year depreciation period is about 210,000 yuan.
Operational Efficiency Inflection Point: When utilization exceeds 50%, the personnel efficiency ratio of the operations team significantly improves. A 10-person operations team managing about 150 nodes per person at 30% utilization can increase to 250 nodes per person at 60% utilization (due to stable system load and lower failure rates). According to measured, report R9, on the Huawei Atlas 910B platform, FX100 reduced the DeepSeek-70B model loading time from 1399s to 150s (a 9.3x acceleration) [source: measured, report R9], directly reducing operational wait times during node switching and scaling.
Non-linear Power Costs: The data center PUE (Power Usage Effectiveness) is typically 1.6-1.8 at 30% load, but can drop to 1.3-1.4 at 60% load. For a compute center consuming 20 million kWh annually, reducing PUE from 1.7 to 1.4 saves about 3.6 million yuan in electricity costs per year.
Feasible Paths to Improve Utilization
Based on industry practices, the following three paths have been validated through measurements:
KV Cache Tiered Acceleration: For long-context inference scenarios, use NVMe-oF arrays to implement hot/cold tiering of KV cache. In the R2 test, FX100 achieved a throughput increase of 29%-40% (concurrency 8-16) on a 480B model, with TTFT reduced by 26%-32% [source: measured, report R2]. This allows a single node to serve more users simultaneously, directly boosting utilization.
Mixed Scheduling of Training and Inference Loads: Utilize checkpoint saving acceleration technology (measured, report R1: 8-card 32B LoRA, save time reduced from 178s to 94s, a 1.9x improvement [source: measured, report R1]) to use idle windows from training tasks for inference services, enabling resource reuse.
No External Memory Recompute Acceleration: In scenarios without external memory recompute, FX100 achieves 8.6-20x acceleration (recompute baseline TTFT p50 149.5s vs. FX100 11.85s) [source: measured, report R2]. This means cold-start tasks that previously took minutes can be completed in seconds, supporting more frequent model switching and elastic scaling.
Conclusion
The breakeven point of a compute center is essentially a trade-off between utilization and TCO. The difference between 30% and 60% is not a simple 2x relationship but a qualitative shift from loss to profit. Mingxin Technology focuses on storage acceleration and compute optimization. The FX100 series products provide measurable, quantified performance improvements in scenarios such as KV cache, model loading, and checkpoint saving. We welcome joint testing through a gated integration test (approximately 10 weeks) to verify TTFT reduction and throughput gains in specific scenarios. A Python-reproducible calculation model is available after NDA.