In high-density computing scenarios such as large AI model training and simulation rendering, the stability of GPUs—as the "heart of computing power"—directly determines the success of business operations.A single A100 GPU failure could disrupt training tasks worth tens of millions, while an operational oversight in an H800 cluster could cause project delays lasting weeks. With years of deep expertise in high-performance computing services, Yuanjie Computing has built a technology-driven GPU repair and maintenance system through its practical experience supporting AI applications across a wide range of industries, establishing a robust defense line to ensure continuous computing power.
I. Fault Diagnosis: Precision Technology for Pinpointing the Root Cause
GPU failures are far more complex than those of ordinary hardware—ranging from microcircuit damage in memory chips to signal attenuation in NVLink interconnects, from logic conflicts in driver firmware to poor contact in liquid-cooled environments. Even the slightest anomaly can trigger a chain reaction. Yuanjie Computing’s three-tier diagnostic system enables second-level response and precise fault localization.
At the hardware level, we employ a dual-verification approach combining “physical inspection and signal analysis.” Using X-ray inspection equipment to penetrate PCB layers, we can identify defects invisible to the naked eye—such as detached memory chips and bulging capacitors—with a precision of 5 μm, equivalent to one-tenth the diameter of a human hair.For XID error codes specific to high-end GPUs like the A100 and H100, engineers parse low-level NVAPI logs to rapidly distinguish between hardware failures (e.g., XID 64 memory row remapping failure), communication failures (e.g., XID 79 NVLink link error), and software anomalies (e.g., XID 43 driver timeout), achieving a diagnostic accuracy rate exceeding 98%.
Software-level diagnostics rely on Yuanjie Computing’s proprietary operations and monitoring platform, which collects over 120 metrics in real time, including GPU core temperature, graphics memory bandwidth, and power supply voltage.When an abnormal increase in ECC error counts or a sudden temperature rise exceeding 90°C is detected, the system automatically triggers a stress test. It verifies the reproducibility of the failure using a combined load from FurMark and 3DMark, and pinpoints the root cause by integrating low-level data from nvidia-smi.This "hardware transparency + software traceability" diagnostic model enables the identification of 80% of common faults within 2 hours.
II. Core Repair: Precision Processes Tailored for High-End Computing Devices
To address different severity levels of faults, Yuanjie Computing has developed a tiered repair technology system, specifically tailored to the full range of GPU devices in its service portfolio, from the RTX 4090 to the H200.
For basic faults such as cooling system failures, we employ a “customized cooling reconstruction” solution: replacing thermal paste with high-thermal-conductivity nano-silicone grease, upgrading to high-density fin heat sinks, and performing dynamic balancing calibration on fans to ensure GPU temperatures remain below 85°C under full load.For power supply failures in multi-GPU clusters such as the A800, engineers use a programmable power supply to simulate a 2000W*4 redundant power environment. Through waveform analysis, they locate capacitors with cold solder joints and replace them precisely using a heat gun, thereby avoiding secondary damage common in traditional repairs.
Chip-level fault repair is at the core of our technology.To address high-end issues such as GPU core solder joint defects and damaged memory chips, we have established a Class 100 cleanroom equipped with high-precision BGA rework stations and laser ball placement equipment. The repair process strictly adheres to the original manufacturer’s temperature profile: preheating at 150°C to remove solder oxidation layers, a peak temperature of 245°C to complete core repositioning, and a cooling phase utilizing gradient cooling technology to ensure that solder strength and chip performance remain unaffected.In a repair case involving an H100 cluster at a major internet company, GPUs repaired using this process underwent a 12-hour, 14.7kW full-load test, with ResNet-50 training efficiency restored to 99.8% and a performance deviation from new cards of less than 0.5%.
Software system repairs focus on compatibility and stability: For CUDA version and framework conflicts, we rapidly match compatible versions through a containerized environment; for BIOS firmware failures, we use the NVFlash tool to perform low-level reflashes and simultaneously update GPU microcode, ensuring seamless compatibility with Yuanjie Computing’s Kubernetes cluster management system.
III. Full-Cycle Maintenance: Building a Sustainable Ecosystem for Computing Power
The long-term stable operation of GPUs relies on a full-cycle maintenance system of “prevention – repair – optimization.” Yuanjie Computing deeply integrates maintenance services into its computing power solutions, forming end-to-end technical support from device deployment to decommissioning.
In the preventive maintenance phase, we employ an “algorithm-based early warning + regular inspection” model. A predictive model trained on two and a half years of failure data from Delta clusters can identify potential risks 72 hours in advance—when GSP RPC communication latency exceeds the threshold, the system automatically generates a maintenance ticket. Engineers use Row Remapping technology to preemptively isolate faulty memory rows, preventing error propagation.Routine inspections cover 18 standard procedures, including hardware cleaning, interface tightening, and pressure testing of liquid cooling lines. Specifically for the high-speed interconnect links between 4x400G network cards and GPUs, NCCL bandwidth calibration is performed quarterly to ensure cluster communication efficiency.
The post-repair quality control system is equally rigorous: all repaired equipment must pass a 72-hour burn-in test simulating full-load AI training scenarios, during which 10 key metrics are monitored; for GPUs in liquid-cooled architectures, additional 24-hour leak detection and hot-swap tests are conducted to ensure compliance with safety standards for high-density deployments.Through this system, Yuanjie Computing’s repaired equipment achieves an average mean time between failures (MTBF) of over 26,000 node-hours, far exceeding the industry average.
For GPU equipment under lease, we offer a dual-layer protection of “Hardware Warranty + Software Support”: replacement with original manufacturer parts ensures consistent performance, while 24/7 online engineers provide rapid response to driver adaptation, cluster debugging, and other needs. Combined with the elastic scheduling of our distributed computing network, this enables seamless switching of faulty nodes, limiting business downtime to minutes.
Technology safeguards computing power; expertise ensures value
In today’s era of exploding AI computing demand, GPU repair and maintenance have long moved beyond the passive “fix-it-when-it-breaks” model, becoming a core component of computing cost optimization and business continuity assurance.Yuanjie Computing leverages chip-level repair technology as its core and a full-lifecycle maintenance system as its foundation, integrating over 20 years of high-end hardware service experience into every inspection and repair. We not only provide stable support for our own computing clusters but also empower industry partners through technology transfer.
From the precise repair of a single faulty card to the operational optimization of clusters comprising thousands of cards, Yuanjie Computing remains anchored in technology—because we deeply understand that the stable operation of every GPU is the driving force behind the acceleration of AI innovation.