Case Studies
案例研究
Case Studies 案例研究
Real results from real repairs. All client information anonymized. 真实维修案例，客户信息均已匿名化处理。
CASES
01
Cloud
Batch GPU Recovery for Cloud Provider
云服务商批量 GPU 修复
Problem
问题
A Major US Cloud Provider (Tier 1) had 48x NVIDIA A100 80GB GPUs experiencing intermittent ECC memory errors and thermal throttling after 18 months of continuous AI training workloads. OEM quoted 6-8 week replacement timeline at full unit cost.
某美国一线云服务商的 48 块 NVIDIA A100 80GB GPU 在连续 18 个月的 AI 训练负载后，出现间歇性 ECC 内存错误和热节流。OEM 报价按全新单价更换，交期 6-8 周。
Diagnosis
诊断
X-ray inspection revealed micro-fractures in BGA solder joints on HBM2e memory stacks, caused by repeated thermal cycling. 12 units also showed degraded thermal interface material.
X 射线检测发现 HBM2e 内存堆栈的 BGA 焊点存在微裂纹，系反复热循环所致。另有 12 块卡的导热界面材料已老化失效。
Solution
方案
BGA reballing of affected memory stacks using lead-free SAC305 solder. Thermal interface replacement with phase-change material. Full thermal profiling and stress testing post-repair.
使用无铅 SAC305 焊料对受损内存堆栈进行 BGA 重植球。用相变材料替换导热界面。修复后进行完整热分布测试和压力测试。
Results
结果
44/48 units (91.7%) restored to full operational spec. 4 units had irreparable die-level damage. Turnaround: 12 business days. Cost savings: ~62% vs OEM replacement.
44/48 块（91.7%）恢复至满载工作状态。4 块因芯片级损伤无法修复。周转时间 12 个工作日，成本节省约 62%。
48
GPUs Processed
处理 GPU 数量
91.7%
Success Rate
修复成功率
12d
Turnaround
周转时间
62%
Cost Savings
成本节省
02
Research
Emergency Server Motherboard Repair
服务器主板紧急抢修
Problem
问题
An AI Research Laboratory (University-affiliated) experienced critical DGX A100 system failure during an active research deadline. 2 server motherboards with power delivery failures — VRM (Voltage Regulator Module) burnout on CPU power rails.
某大学附属 AI 研究实验室在研究截止日前遭遇 DGX A100 系统关键故障。2 块服务器主板供电失效 — CPU 供电轨路上的 VRM（电压调节模块）烧毁。
Diagnosis
诊断
Oscilloscope analysis identified failed MOSFETs in the VRM array. Root cause: inadequate airflow in custom rack configuration caused sustained thermal stress.
示波器分析定位到 VRM 阵列中失效的 MOSFET。根因：定制机架配置的散热气流不足，导致持续热应力。
Solution
方案
Replaced failed MOSFETs and gate drivers. Added thermal pads to VRM inductors. Provided airflow recommendations for rack redesign.
更换失效 MOSFET 和栅极驱动器，为 VRM 电感添加导热垫，并提供机架气流优化建议。
Results
结果
Both motherboards restored within 72 hours (emergency service). System back online before research deadline. Airflow fix prevented recurrence.
两块主板在 72 小时内修复（紧急服务）。系统在研究截止日前恢复上线。气流优化方案杜绝了问题复发。
2
Motherboards
主板数量
72h
Emergency Turnaround
紧急周转
$0
Lost Research Time
研究损失时间
0
Recurrence
问题复发
"Every board we restore is one less in a landfill."
"我们修好的每一块板，都少进一块垃圾填埋场。"
03
Enterprise
Preventive Maintenance Program
预防性维护计划
Problem
问题
A Mid-size Cloud Infrastructure Company was experiencing an 8-12% annual GPU failure rate across a 500-unit fleet. Reactive repairs caused unpredictable downtime and budget overruns.
某中型云基础设施公司旗下 500 块 GPU 集群的年故障率高达 8-12%。被动式维修导致不可预测的停机时间和预算超支。
Solution
方案
Implemented quarterly inspection program with thermal imaging, power rail monitoring, and predictive analytics based on failure patterns. Established FRU (Field Replaceable Unit) inventory buffer.
实施季度巡检计划：热成像、供电轨路监测、基于故障模式的预测性分析。建立 FRU（现场可替换单元）库存缓冲。
Results — 12 Months
结果 — 12 个月后
Failure rate reduced from 11% to 3.2%. Mean time to repair dropped from 15 days to 4 days. Annual hardware budget variance reduced by 40%.
故障率从 11% 降至 3.2%。平均修复时间从 15 天缩短至 4 天。年度硬件预算偏差降低 40%。
Methodology
方法论
FLIR thermal cameras for hot-spot detection. Custom monitoring dashboards tracking power draw anomalies. ML-based failure prediction model trained on historical repair data.
FLIR 热像仪用于热点检测。定制监控面板追踪功耗异常。基于历史维修数据训练的 ML 故障预测模型。
500
Unit Fleet
集群规模
71%
Failure Reduction
故障率降幅
73%
Faster Repairs
修复提速
40%
Budget Improvement
预算优化
04
Startup
Supply Chain Optimization
供应链优化
Problem
问题
An Emerging AI Startup (Series B) was building their first GPU cluster — 128x H100s. Struggling with spare parts availability, no maintenance infrastructure, and facing 16-week lead times for replacement units.
某AI 初创公司（B 轮）正在搭建首个 GPU 集群 — 128 块 H100。面临备件短缺、无维护体系、替换件交期长达 16 周等难题。
Solution
方案
Designed complete spare parts strategy: maintained 5% FRU buffer, established vendor relationships for critical components, set up inventory tracking system. Provided on-call repair support.
设计完整备件策略：维持 5% FRU 缓冲库存，建立关键组件供应商关系，搭建库存追踪系统，提供随叫随到的维修支持。
Results
结果
Zero extended downtime in first year of operation. 3 GPU failures resolved within 5 business days each. Estimated $400K+ saved vs maintaining full spare unit inventory.
运营首年零长时停机。3 次 GPU 故障均在 5 个工作日内解决。与维护全套备机库存相比，估算节省超 40 万美元。
Ongoing
持续服务
Quarterly inventory audits. Vendor-managed inventory for high-failure-rate components (fans, power supplies, memory modules). 24/7 on-call SLA for critical failures.
季度库存审计。高故障率组件（风扇、电源、内存模组）由供应商管理库存。关键故障提供 7x24 小时随叫随到 SLA。
128
GPUs Managed
管理 GPU 数量
0
Extended Downtime
长时停机
5d
Avg Repair Time
平均修复时间
$400K+
Saved
成本节省
TR
Next Steps
下一步
Have a Similar Challenge?
遇到类似问题？
Every GPU fleet is different. Tell us about your hardware situation and we'll design a solution that fits.
每个 GPU 集群都不一样。告诉我们您的硬件现状，我们为您量身定制解决方案。
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