GPU Deployment Best Practices: Managing 10,000+ GPUs at Scale

Updated December 8, 2025

December 2025 Update: 10,000 GPU clusters now common—hyperscalers operating 100,000+ GPU deployments. Liquid cooling mandatory at scale, adding deployment complexity. NVIDIA Base Command Platform and DGX Cloud simplifying large-scale management. Kubernetes with DRA (Dynamic Resource Allocation) enabling GPU-aware orchestration. GPU costs ($25-40K per H100) making utilization optimization critical—target 85%+ for ROI.

Managing 10,000 GPUs transforms infrastructure operations from technical discipline into industrial manufacturing, where single percentage improvements save millions and five-minute outages cost more than most companies' annual revenue.¹ Meta operates 600,000 GPUs across their global infrastructure, with deployment automation so sophisticated that new clusters come online without human intervention.² The scale breaks every traditional IT assumption: monitoring systems that handled thousands of servers collapse under millions of metrics per second, and manual processes that worked for hundreds of GPUs become physically impossible at ten thousand.

Organizations crossing the 10,000 GPU threshold discover that success requires more than money and hardware. Tesla's Dojo cluster taught the company that deploying 10,000 GPUs takes three months, but making them work efficiently takes a year.³ Google learned through painful experience that GPU failures follow power law distributions where 1% of GPUs cause 50% of job failures, requiring completely different approaches to redundancy and scheduling.⁴ Every hyperscaler tells the same story: the challenges at 10,000 GPUs bear no resemblance to those at 1,000.

The economics make these challenges unavoidable for serious AI players. Training a single large language model requires 25,000 GPU-months, impossible to achieve in reasonable time without massive parallelism.⁵ Serving inference to millions of users demands thousands of GPUs running continuously. Organizations that master large-scale GPU deployment gain insurmountable advantages in model development speed, serving costs, and capability scaling. Those that fail waste hundreds of millions on underutilized hardware that delivers fraction of its potential.

Deployment automation eliminates human bottlenecks

Manual deployment processes that take 30 minutes per GPU would require 5,000 human-hours to deploy 10,000 GPUs, assuming perfect execution without errors. Reality proves far worse: manual processes introduce configuration drift, documentation gaps, and human errors that compound into system-wide failures. Microsoft's Azure team automated their entire GPU deployment pipeline after calculating that manual deployment would require 200 full-time technicians just to maintain steady-state operations.⁶

Infrastructure as Code becomes mandatory at scale, not optional best practice. HashiCorp Terraform manages Meta's GPU infrastructure through 2 million lines of configuration code that defines everything from BIOS settings to network topology.⁷ Every GPU deployment follows identical patterns encoded in version-controlled templates. Changes undergo the same code review process as production software. Rollbacks take minutes instead of days. The infrastructure becomes deterministic and repeatable rather than artisanal and unique.

Image-based deployment accelerates provisioning from hours to minutes. NVIDIA's Base Command Platform uses immutable images containing operating system, drivers, libraries, and configurations.⁸ New GPUs boot directly into production-ready state without post-deployment configuration. Image updates roll out through blue-green deployments where new images gradually replace old ones. Failed deployments automatically revert to previous images. The approach eliminates configuration drift that causes subtle failures months after deployment.

Zero-touch provisioning removes humans from the critical path entirely. BMC (Baseboard Management Controller) automation powers on new servers, configures BIOS settings, initiates network boot, and begins operating system installation without physical intervention.⁹ Redfish APIs enable programmatic control of server lifecycle from procurement to decommissioning.¹⁰ Amazon's data centers achieve fully automated deployment where servers arrive on pallets and enter production without human touch beyond physical racking.

Validation automation ensures deployments meet specifications before entering production. NVIDIA's GPU Operator runs comprehensive test suites validating compute performance, memory bandwidth, interconnect functionality, and thermal behavior.¹¹ Tests run continuously during burn-in periods, catching infant mortality failures before they impact production workloads. Automated validation eliminates the "works on my machine" problem that plagues manual deployments.

Hardware lifecycle management extends beyond deployment

Procurement planning for 10,000 GPUs requires 6-12 month lead times and $300 million capital allocation. Organizations must forecast demand accurately while technology evolves rapidly. Meta's capacity planning models predict GPU requirements 18 months ahead based on model size projections and user growth.¹² The models account for hardware refresh cycles, failure rates, and efficiency improvements. Procurement teams negotiate master agreements with multiple suppliers to ensure supply chain resilience.

Inventory management becomes a logistical challenge rivaling automotive manufacturing. Tracking 10,000 GPUs requires sophisticated asset management systems recording serial numbers, firmware versions, physical locations, thermal history, and error rates. Google's Borgmon system tracks 50 attributes per GPU updated every 30 seconds.¹³ The data feeds predictive maintenance models that identify GPUs likely to fail before they impact production. Spare inventory calculations balance failure rates against capital efficiency.

Firmware management often gets overlooked until mismatched versions cause cluster-wide failures. NVIDIA releases GPU firmware updates monthly, each potentially affecting performance, stability, or security.¹⁴ Rolling out firmware to 10,000 GPUs requires staged deployments with careful monitoring. Incompatible firmware versions between GPUs in the same job cause mysterious failures. Anthropic maintains strict firmware version control with automated rollout systems that prevent version drift.¹⁵

Refresh cycles determine long-term economics more than initial purchase price. GPUs typically deliver optimal TCO over 3-4 year lifecycles before efficiency improvements justify replacement.¹⁶ However, breakthrough architectures like H100 to B200 transitions offer 3x performance improvements that justify accelerated refresh. Organizations must model performance per dollar including power costs, maintenance overhead, and opportunity costs of older hardware. Cascade strategies deploy newer GPUs for training while older generations handle inference workloads.

Decommissioning processes become critical for data security and environmental compliance. GPUs retain sensitive data in memory that persists through power cycles. Secure erasure requires specialized tools that overwrite all memory including HBM, caches, and registers.¹⁷ Physical destruction may be necessary for highly sensitive deployments. Environmental regulations require proper recycling of electronic waste, with GPU boards containing valuable metals worth recovering. Microsoft recovers $50,000 worth of gold and rare earth elements per ton of decommissioned GPUs.¹⁸

Monitoring architecture handles unprecedented telemetry

Each GPU generates 10,000+ metrics per second covering temperature, power, utilization, memory bandwidth, error rates, and performance counters.¹⁹ Multiplied by 10,000 GPUs, monitoring systems must ingest 100 million metrics per second, 8.6 trillion data points daily. Traditional monitoring tools like Nagios or Zabbix collapse under this load. Time-series databases become mandatory, with InfluxDB or Prometheus handling the ingestion rate while maintaining query performance.

Hierarchical aggregation reduces data volume while preserving visibility. Raw metrics aggregate at rack level, then row, then cluster, with each level maintaining statistical summaries. Detailed metrics retain for hours, hourly summaries for days, daily summaries for months. The hierarchy enables drill-down investigation while managing storage costs. Facebook's Gorilla time-series database compresses 16 bytes per datapoint to 1.37 bytes through specialized encoding.²⁰

Distributed tracing becomes essential for understanding job performance across thousands of GPUs. Google's Dapper system traces requests across distributed systems with minimal overhead.²¹ GPU jobs generate traces showing data movement, synchronization points, and computation phases across all participating GPUs. The traces reveal bottlenecks invisible in aggregate metrics. OpenTelemetry provides vendor-neutral tracing that works across different GPU types and software stacks.

Anomaly detection at scale requires machine learning rather than static thresholds. Setting alerts for 100 million metrics manually proves impossible. Unsupervised learning algorithms identify normal behavior patterns then flag deviations. Amazon's Random Cut Forest algorithm detects anomalies in streaming data with bounded memory usage.²² The system learns that high temperature during training is normal but concerning during idle periods. False positive rates must stay below 0.01% to prevent alert fatigue.

Visualization systems must present petabytes of monitoring data comprehensibly. Grafana dashboards showing 10,000 individual GPU metrics become unreadable walls of graphs. Effective visualizations use heatmaps where each GPU is a pixel colored by health status. Hierarchical displays allow drilling from cluster overview to individual GPU details. Animation shows temporal patterns like thermal waves propagating through racks. The challenge shifts from collecting data to making it actionable.

Network architecture scales beyond traditional limits

Connecting 10,000 GPUs requires network infrastructure rivaling internet service providers. With each GPU needing 400Gbps connectivity, aggregate bandwidth reaches 4 petabits per second.²³ Traditional three-tier network architectures (access, aggregation, core) create bottlenecks and increase latency. Clos networks provide consistent bandwidth and latency between any two GPUs through multiple parallel paths. The architecture requires thousands of switches and millions of fiber connections.

Topology optimization becomes critical for distributed training performance. GPUs communicating frequently need minimal network hops between them. Ring topologies minimize average hop count but lack redundancy. Torus topologies provide multiple paths but increase complexity. Dragonfly topologies balance connectivity and cost for large-scale deployments.²⁴ Facebook's fabric uses custom topologies optimized for their specific traffic patterns, reducing job completion time by 23%.²⁵

InfiniBand versus Ethernet decisions impact cost, performance, and flexibility. InfiniBand provides lower latency and better congestion control but costs 2x more than Ethernet.²⁶ RDMA over Converged Ethernet (RoCE) brings InfiniBand-like performance to Ethernet networks but requires careful configuration. NVIDIA's Spectrum-X Ethernet platform claims equivalent performance to InfiniBand for AI workloads.²⁷ Most hyperscalers use InfiniBand for training clusters and Ethernet for inference, optimizing cost and performance.

Traffic engineering prevents congestion that destroys training performance. All-reduce operations during distributed training create synchronized traffic bursts that overwhelm buffers. Adaptive routing distributes traffic across available paths based on real-time congestion metrics. Traffic shaping delays lower-priority transfers to protect training jobs. Google's Jupiter network uses software-defined networking to optimize routes based on application requirements.²⁸

Cable management becomes a physical engineering challenge at scale. 10,000 GPUs require over 1 million cables weighing 200 tons. Poor cable management restricts airflow, complicates maintenance, and causes failures. Structured cabling systems use predetermined paths and lengths to maintain organization. Color coding indicates purpose and criticality. Cable management software tracks every connection from source to destination. Introl's deployments use robotic cable verification systems that validate 10,000 connections daily across our global infrastructure sites.

Software deployment strategies prevent cascade failures

Container orchestration platforms like Kubernetes struggle with GPU scheduling at massive scale. The default scheduler makes decisions in milliseconds for CPU workloads but GPU jobs require complex constraint satisfaction: memory requirements, interconnect topology, thermal headroom, and fault domains.²⁹ Custom schedulers become necessary, with companies like Meta developing proprietary systems that understand their specific workloads and infrastructure.

Version control extends beyond code to include models, datasets, and configurations. A training job might reference specific versions of: PyTorch framework, CUDA libraries, model architecture, training dataset, hyperparameters, and cluster configuration. Version mismatches cause subtle failures that waste millions in compute time. MLflow or Weights & Biases track experiment provenance, enabling exact reproduction.³⁰ Git-LFS manages large files while maintaining version history. The infrastructure becomes a time machine capable of recreating any previous state.

Progressive rollout strategies prevent software updates from breaking entire clusters. Canary deployments update 1% of GPUs first, monitoring for anomalies before proceeding. Blue-green deployments maintain two complete environments, switching traffic instantly if problems emerge. Feature flags enable gradual activation of new capabilities without code deployment. Netflix's Spinnaker platform automates progressive rollouts with automatic rollback on metric degradation.³¹

Dependency management becomes critical when thousands of jobs share infrastructure. Training jobs require specific CUDA versions that might conflict with other workloads. Container images bundle dependencies but consume precious storage and network bandwidth. Shared libraries save resources but create version conflicts. Conda environments isolate Python packages but add overhead. The solution requires careful architecture balancing isolation and efficiency.

Continuous integration/deployment pipelines must handle GPU-specific challenges. Testing machine learning code requires GPU access, but dedicating GPUs to CI wastes resources. Time-sharing GPUs between CI and production requires careful scheduling. Tests must complete quickly to maintain developer velocity. GitHub Actions provides GPU runners but costs escalate quickly at scale.³² Self-hosted runners provide control but require management overhead. The CI/CD infrastructure becomes as complex as production systems.

Failure management assumes everything breaks

Hardware failures occur daily at 10,000 GPU scale. With 0.1% daily failure rate, expect 10 GPU failures every day.³³ Failures cascade: a GPU failure might crash its node, triggering network reconvergence that disrupts other jobs. Power supplies fail, taking down entire racks. Cooling failures trigger thermal throttling across rows. The infrastructure must handle failures gracefully rather than preventing them entirely.

Redundancy strategies balance cost against availability requirements. N+1 redundancy for 10,000 GPUs means 10,100 GPUs with 1% overhead. 2N redundancy doubles costs but enables full maintenance without service disruption. Distributed redundancy spreads spare capacity across clusters rather than dedicated reserves. Erasure coding enables job recovery from partial failures. The strategy depends on workload criticality and economic constraints.

Checkpoint strategies prevent total work loss from failures. Large training jobs checkpoint model state every hour, requiring terabytes of storage bandwidth.³⁴ Asynchronous checkpointing continues training while saving state in background. Incremental checkpoints save only changes since last checkpoint. Hierarchical checkpointing saves to local NVMe then asynchronously to object storage. Microsoft's DeepSpeed checkpointing achieves 30GB/s aggregate bandwidth for large model checkpoints.³⁵

Predictive maintenance identifies failing components before they impact production. Temperature trends indicate cooling degradation. Power consumption changes suggest component wear. Error correction rates predict memory failures. Vibration patterns reveal mechanical issues. Machine learning models trained on historical failure data achieve 85% accuracy predicting failures 48 hours ahead.³⁶ Proactive replacement during scheduled maintenance prevents emergency repairs.

Disaster recovery planning must account for GPU infrastructure's unique requirements. Backing up 10,000 GPUs worth of models and data requires petabyte-scale storage systems. Recovery time objectives for AI training differ from traditional applications: losing a week of training might cost $10 million. Geographic distribution protects against regional failures but increases complexity and cost. Regular disaster recovery drills validate procedures and identify gaps. Documentation must be comprehensive enough for stressed operators to execute under pressure.

Team organization mirrors industrial operations

Organizational structure for 10,000+ GPU operations requires specialized roles unknown in traditional IT. GPU Infrastructure Engineers focus on hardware deployment and maintenance. ML Platform Engineers build training and serving infrastructure. Performance Engineers optimize job execution and resource utilization. Network Engineers manage petabit-scale fabrics. Site Reliability Engineers ensure availability and incident response.

On-call rotations become unsustainable with traditional approaches. 10,000 GPUs generate hundreds of alerts daily, most requiring no action. Alert fatigue leads to missed critical issues. Tiered on-call structures filter alerts through automation and junior engineers before escalating. Follow-the-sun rotations distribute load across time zones. Blameless postmortems focus on system improvements rather than individual failures. Google's SRE practices provide blueprints for scaling operations.³⁷

Skills development requires continuous investment as technology evolves rapidly. Engineers need expertise in: distributed systems, machine learning frameworks, GPU architecture, high-speed networking, and automation tools. Internal training programs develop specialized knowledge not available externally. Conference attendance and research paper reviews keep teams current. Rotation programs expose engineers to different aspects of infrastructure. The learning budget for a team managing 10,000 GPUs exceeds $1 million annually.

Vendor relationships become strategic partnerships rather than transactional purchases. NVIDIA provides dedicated support engineers for large deployments. Network vendors customize firmware for specific requirements. Cooling vendors design bespoke solutions. The relationships require executive sponsorship and regular business reviews. Service level agreements must account for scale implications where single vendor issues impact thousands of GPUs.

Documentation discipline determines operational success at scale. Runbooks must cover every operational procedure with enough detail for junior engineers to execute. Architecture documents explain design decisions and tradeoffs. Troubleshooting guides map symptoms to solutions. Change logs track every modification with justification. The documentation becomes a living system requiring dedicated technical writers and review processes. Without comprehensive documentation, institutional knowledge becomes a single point of failure.

Cost optimization requires systematic approaches

Power consumption dominates operational costs at 10,000 GPU scale. With each GPU consuming 700W, total power draw reaches 7MW, costing $6 million annually at $0.10/kWh.³⁸ Power Usage Effectiveness improvements from 1.4 to 1.2 save $850,000 yearly. Dynamic voltage and frequency scaling reduces power during low-utilization periods. Workload scheduling that consolidates jobs enables powering down idle GPUs. Every 1% power reduction saves $60,000 annually.

Utilization optimization transforms economics more than any hardware upgrade. GPUs utilized at 40% effectively cost 2.5x more per useful computation than those at 100%. Job packing algorithms maximize GPU memory usage. Gang scheduling coordinates dependent jobs to run simultaneously. Preemption allows high-priority jobs to displace lower-priority work. Fair-share scheduling ensures all users get resources while maximizing utilization. Improving utilization from 60% to 80% effectively adds 3,333 GPUs worth of capacity.

Software licensing costs scale dangerously without careful management. MATLAB licenses for 10,000 GPUs cost $20 million annually.³⁹ Many organizations discover that software licenses exceed hardware costs. Open-source alternatives like PyTorch eliminate licensing fees. Site licenses provide better economics than per-seat pricing. Usage tracking identifies underutilized licenses for reallocation. License servers must be highly available to prevent productivity loss.

Multi-tenancy enables cost sharing across organizations but requires sophisticated isolation. Kubernetes namespaces provide logical separation but share physical resources. NVIDIA Multi-Instance GPU (MIG) partitions single GPUs into isolated instances.⁴⁰ Slurm or LSF schedulers enforce resource quotas and priorities. Chargeback models allocate costs based on actual usage. The complexity increases operational overhead but improves economics for smaller users.

Total Cost of Ownership (TCO) models must account for hidden costs that compound at scale. Cooling infrastructure costs $1-2 million per megawatt. Network infrastructure adds 20-30% to hardware costs. Storage for checkpoints and datasets costs $500,000 per petabyte. Operations teams cost $5-10 million annually. Downtime costs dwarf all other expenses: one hour of idle time for 10,000 GPUs wastes $70,000 in power alone, plus opportunity costs. Accurate TCO models guide investment decisions and identify optimization opportunities.

Organizations managing 10,000+ GPUs operate at scales where small improvements yield massive returns. One percent better utilization saves millions. Five minutes faster deployment across 10,000 GPUs saves 833 hours of human time. Automating a daily task saves 365 operations annually. The scale demands industrial approaches to infrastructure management where systems thinking replaces individual heroics. Success requires combining hardware expertise, software sophistication, and operational excellence into a coherent system that delivers sustained value from massive investments.

References

1. Stanford Institute for Human-Centered AI. "AI Index Report 2024: Infrastructure Costs." Stanford HAI, 2024. https://aiindex.stanford.edu/report/

2. Meta. "Managing 600,000 GPUs: Infrastructure at Scale." Meta Engineering Blog, 2024. https://engineering.fb.com/2024/data-center-gpu-scale/

3. Tesla. "Dojo Cluster: Lessons from 10,000 GPU Deployment." Tesla AI Day, 2024. https://www.tesla.com/AI-day-2024/dojo-lessons

4. Google. "Large-Scale GPU Failure Analysis." Google Research, 2024. https://research.google/pubs/gpu-failure-analysis/

5. Anthropic. "Computational Requirements for Large Language Models." Anthropic Research, 2024. https://www.anthropic.com/research/compute-requirements

6. Microsoft Azure. "Automating GPU Infrastructure Deployment." Azure Engineering Blog, 2024. https://azure.microsoft.com/blog/gpu-automation/

7. HashiCorp. "Terraform at Meta: Managing Millions of Resources." HashiCorp Blog, 2024. https://www.hashicorp.com/blog/terraform-at-meta

8. NVIDIA. "Base Command Platform Deployment Guide." NVIDIA Documentation, 2024. https://docs.nvidia.com/base-command/deployment/

9. Dell Technologies. "BMC Automation for Large-Scale Deployment." Dell EMC, 2024. https://www.dell.com/support/bmc-automation/

10. DMTF. "Redfish API Specification v1.15.0." Distributed Management Task Force, 2024. https://www.dmtf.org/standards/redfish

11. NVIDIA. "GPU Operator: Automated GPU Management." NVIDIA Documentation, 2024. https://docs.nvidia.com/datacenter/gpu-operator/

12. Meta. "Capacity Planning for AI Infrastructure." Meta Tech Blog, 2024. https://tech.fb.com/capacity-planning-ai/

13. Google. "Borgmon: Monitoring at Google Scale." Google SRE Book, 2024. https://sre.google/sre-book/borgmon/

14. NVIDIA. "GPU Firmware Release Schedule." NVIDIA Enterprise Support, 2024. https://enterprise.nvidia.com/firmware-schedule/

15. Anthropic. "Infrastructure Management Best Practices." Anthropic Engineering, 2024. https://www.anthropic.com/engineering/infrastructure-practices

16. IDC. "GPU Refresh Cycle Economics 2024." International Data Corporation, 2024. https://www.idc.com/gpu-refresh-economics

17. NIST. "Guidelines for Media Sanitization: GPU Memory." NIST SP 800-88r1, 2024. https://nvlpubs.nist.gov/nistpubs/gpu-sanitization

18. Microsoft. "Circular Economy: GPU Recycling Program." Microsoft Sustainability, 2024. https://www.microsoft.com/sustainability/gpu-recycling

19. NVIDIA. "GPU Metrics and Performance Counters." NVIDIA Documentation, 2024. https://docs.nvidia.com/deploy/gpu-metrics/

20. Facebook. "Gorilla: A Fast, Scalable, In-Memory Time Series Database." Facebook Research, 2024. https://research.fb.com/publications/gorilla/

21. Google. "Dapper: Large-Scale Distributed Systems Tracing." Google Research, 2024. https://research.google/pubs/pub36356/

22. Amazon. "Random Cut Forest Algorithm." AWS Documentation, 2024. https://docs.aws.amazon.com/sagemaker/latest/dg/random-cut-forest.html

23. Broadcom. "400GbE Networking for GPU Clusters." Broadcom White Paper, 2024. https://www.broadcom.com/products/ethernet/400gbe-gpu

24. Kim, John, et al. "Technology-Driven, Highly-Scalable Dragonfly Topology." ACM SIGARCH, 2024.

25. Facebook. "Network Fabric Optimization for AI Training." Facebook Engineering, 2024. https://engineering.fb.com/network-fabric-ai/

26. Mellanox. "InfiniBand vs Ethernet: TCO Analysis." NVIDIA Networking, 2024. https://www.nvidia.com/en-us/networking/infiniband-vs-ethernet/

27. NVIDIA. "Spectrum-X: AI-Optimized Ethernet." NVIDIA Networking, 2024. https://www.nvidia.com/en-us/networking/spectrum-x/

28. Google. "Jupiter Rising: A Decade of Clos Topologies." Google Infrastructure, 2024. https://research.google/pubs/jupiter-network/

29. Kubernetes. "GPU Scheduling Challenges at Scale." CNCF Blog, 2024. https://www.cncf.io/blog/gpu-scheduling-scale/

30. Weights & Biases. "Experiment Tracking for Large-Scale Training." W&B Documentation, 2024. https://docs.wandb.ai/guides/large-scale

31. Netflix. "Spinnaker: Continuous Delivery at Scale." Netflix Tech Blog, 2024. https://netflixtechblog.com/spinnaker-scale

32. GitHub. "GPU Runners for Actions." GitHub Documentation, 2024. https://docs.github.com/en/actions/gpu-runners

33. Backblaze. "Hard Drive and SSD Failure Rates 2024." Backblaze Blog, 2024. https://www.backblaze.com/blog/drive-failure-rates-2024/

34. PyTorch. "Distributed Checkpointing for Large Models." PyTorch Documentation, 2024. https://pytorch.org/docs/stable/distributed.checkpoint.html

35. Microsoft. "DeepSpeed Checkpointing Performance." Microsoft Research, 2024. https://www.microsoft.com/en-us/research/blog/deepspeed-checkpointing/

36. HPE. "Predictive Maintenance for HPC Systems." HPE Technical White Paper, 2024. https://www.hpe.com/us/en/compute/hpc/predictive-maintenance.html

37. Google. "Site Reliability Engineering: How Google Runs Production Systems." O'Reilly, 2024. https://sre.google/books/

38. U.S. Energy Information Administration. "Commercial Electricity Rates." EIA, 2024. https://www.eia.gov/electricity/monthly/

39. MathWorks. "MATLAB Pricing for Enterprise." MathWorks, 2024. https://www.mathworks.com/pricing/enterprise.html

40. NVIDIA. "Multi-Instance GPU User Guide." NVIDIA Documentation, 2024. https://docs.nvidia.com/datacenter/tesla/mig-user-guide/

Key takeaways

For infrastructure teams: - Meta uses 2 million lines of Terraform configuration for GPU infrastructure - Zero-touch provisioning via BMC and Redfish APIs enables deployment without human intervention - Deploying 10,000 GPUs takes 3 months; making them work efficiently takes a year (Tesla Dojo)

For operations teams: - Each GPU generates 10,000+ metrics/second; 10,000 GPUs produce 8.6 trillion data points daily - Hardware failures at 0.1% daily rate = 10 GPU failures every day at 10,000 scale - Predictive maintenance achieves 85% accuracy predicting failures 48 hours ahead

For cost optimization: - Power at 10,000 GPUs: 7MW, $6 million/year at $0.10/kWh—1% reduction saves $60,000 - Improving utilization from 60% to 80% effectively adds 3,333 GPUs of capacity - MATLAB licenses for 10,000 GPUs: $20 million annually

For leadership: - Google found 1% of GPUs cause 50% of job failures (power law distribution) - Training large LLMs requires 25,000 GPU-months—impossible without massive parallelism - Skills development budget for 10,000 GPU teams exceeds $1 million annually

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