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With the rapid implementation of generative AI, large model training, and high-density intelligent computing clusters, the load patterns and power consumption characteristics of GPUs are undergoing fundamental changes. The steady-state power consumption testing system formed in the era of traditional servers has become unable to meet the verification needs of the new generation of AI computing hardware. Since 2026, a large number of AI training clusters in the industry have encountered difficult-to-locate computing power fluctuations. There is no obvious overheating of GPUs or error codes in the links, but the training throughput has declined periodically. After engineering investigation, it was found that the root cause of the problem often lies in the insufficient dynamic response capability of the power supply link, rather than a single hardware failure. This phenomenon has directly driven a significant improvement in testing standards for GPU operating current, power loss, etc. Both the underlying hardware and algorithms need to adapt to the new generation of testing requirements. An upgrade of the test instrument architecture for AI scenarios is unfolding across the entire industry chain.
From steady state to pulse: GPU workload pattern reconstruction test logic
The load variation of traditional server CPUs is relatively smooth, with TDP fluctuating on a scale of seconds to minutes. The voltage loop of the power supply has ample time to complete its response, and the corresponding power consumption testing system has long been designed around indicators such as steady-state power and average current. However, in the era of AI computing power, the training load of GPUs exhibits typical pulse characteristics. The TDP of NVIDIA H100 is nominally 700W, but in actual training processes, matrix operations and data loading alternate, causing the entire card's power consumption to fluctuate rapidly within the hundreds of watts range. Public materials for OAI architectures clearly provide the transient power envelope: 2×TDP can be as short as 20μs, 1.75×TDP can last for 2ms, and 1.5×TDP can last for 5ms. The pulse cycle of GPU load has been shortened to the order of magnitude of 100μs-1ms, while the typical bandwidth of the power supply voltage loop is only 10kHz-50kHz, corresponding to a response time of 20μs-100μs. When the load pulse frequency approaches the loop bandwidth, the power supply enters a 'quasi-resonant' state, and the output voltage exhibits continuous oscillation rather than a single overshoot, which directly breaks the steady-state measurement assumption of traditional testing instruments.
There is a common attribution misconception in the industry: it is believed that for microsecond-level GPU current transients, the first responder is the rack power supply unit (PSU). In reality, the on-board VRM and decoupling network close to the GPU actually bear the responsibility of the first-level regulation. What is conducted to the PSU output terminal is load steps and continuous ripples on the scale of hundreds of microseconds to milliseconds, which have been shaped by the board-level network. These two power supply units are in series. When the dynamic response margin of the PSU is insufficient, the bus voltage deviation will compress the input margin of the board-level VRM. The superposition of these two levels ultimately triggers the protective frequency reduction of the GPU power supply. This is also a typical mechanism for the 'computing power fluctuation being traced back to the power supply' observed in many clusters - neither level of hardware is damaged, but the margin of each level is gradually eroded by dynamic loads.
High-density AI servers force a comprehensive elevation of testing standards
As the number of GPUs per AI server increases from 8 to higher densities, the power supply unit (PSU) power level has escalated from 2400W in the CRPS 2.0 era to 3200W in CRPS 3.0 and 3200W+165% peak in m-CRPS, capable of instantaneously outputting 5280W. In the ORv3 HPR architecture, a single PSU has reached 5.5kW, and power transient response testing is transitioning from being a marginal item that merely meets standards to a core verification step determining system stability. Simultaneously upgrading is the electrical characteristic of AI accelerators: extremely low operating voltage coupled with unprecedented current demand. This combination fundamentally alters the device's load-bearing operating characteristics and restructures the measurement, stress loading, and verification logic during the testing phase.
At the near end of the load, the number of DrMOS used in a single high-performance GPU is an order of magnitude higher than that in a regular CPU. These intelligent power stages, which integrate gate driving and power devices, impose almost stringent requirements on test accuracy. Engineers need to precisely measure the extremely low on-resistance RDS(on) while passing through a large current of tens of amperes, without causing the device to overheat. At this level, even millivolt-level measurement errors can lead to significant power consumption losses and temperature increases, leaving very little margin for error. If the measurement speed is too slow, thermal effects can easily distort the test results. At the same time, noise, ground potential shifts, and increasingly pronounced parasitic effects under high current all further reduce measurement accuracy. The high-density packaging of devices, multi-channel parallel contact, and common ground loop design all amplify the sources of error in traditional test instruments.
In 2026, the domestic GPU server rental market has surpassed a value of 30 billion yuan. According to the actual test data from a third-party computing power evaluation agency in Q1 2026, 47% of the GPU rental platforms currently on the market exhibit issues with false computing power labeling, with the false labeling rate ranging from 15% to 68%. Notably, 32% of small platforms have a false labeling rate exceeding 40%, resulting in a reduction of user training efficiency by over 35% and an average loss exceeding 2,000 yuan per order. Behind this chaos of false computing power labeling lies not only deliberate violations by some platforms but also the inherent limitation of traditional testing instruments, which cannot accurately capture the true dynamic computing power and instantaneous power consumption of GPUs. Relying solely on the static parameters labeled by the platform is completely insufficient to determine the actual performance of the equipment under real training loads.
Full-chain upgrade: underlying hardware and algorithms synchronously adapt to new requirements
Facing the new challenges brought by AI computing power, dedicated power consumption testing systems are becoming core infrastructure with strategic value. Indicators such as current carrying capacity, energy efficiency, and transient response have been officially incorporated into rigid testing specifications. This also confirms a reality: power consumption performance has become a core factor determining the yield, reliability, and system-level performance of AI servers. The architectural upgrade of traditional testing instruments is moving from single-index optimization to full-chain reconstruction.
At the underlying hardware level, the new generation of test instruments commonly employs sampling front-ends with higher bandwidths, elevating the sampling rate from the traditional MHz level to tens of MHz or even hundreds of MHz, ensuring the complete capture of GPU pulse load waveforms ranging from microseconds to milliseconds. Simultaneously, the design of parasitic parameters in high-current paths is optimized. Methods such as Kelvin connections and low-resistance wiring are employed to offset the voltage drop and ground potential shift in conductors under high current, further reducing millivolt-level measurement errors. Some test systems tailored for intelligent computing scenarios have also incorporated a multi-channel synchronous triggering mechanism, enabling the simultaneous acquisition of voltage and current data at three levels: cabinet PSU, on-board VRM, and GPU die terminal. This fully reproduces the dynamic response process of the power supply chain and identifies margin overlap issues that traditional instruments cannot detect.
At the algorithmic level, the industry is moving away from traditional average power statistical logic and shifting towards analysis models based on time-domain pulse characteristics. The new algorithms can automatically identify GPU load pulses of different periods and amplitudes, precisely calculate the dynamic margin of each power supply unit, and predict in advance the hidden risk of 'each stage is not broken, but the combination triggers frequency reduction'. Some manufacturers have also incorporated AI itself into the testing process, using a pre-trained GPU load feature library to automatically compare the deviation between the measured waveform and the standard envelope, thereby increasing the efficiency of power consumption verification by several times.
From the perspective of long-term industry trends, the central price of computing power has been moving upwards for a long time, with rigid costs such as hardware, electricity, and infrastructure continuing to rise. Whether it is for leading model manufacturers to build their own large-scale computing power clusters or for mid-sized AI application service providers to lease computing power resources, both have raised higher demands for the true stability of computing power. As the commercialization of domestic GPUs enters an accelerated realization period, the mass production scale of local computing power hardware is rapidly expanding. A new power consumption testing system that is compatible with AI pulse loads will become the core infrastructure of the entire computing power industry, providing underlying reliability guarantees for scenarios such as large model training and long-distance tasks for AI Agents.
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