Amazon’s Project Greenland: A GPU Management Strategy
In mid-2024, Amazon found itself in a surprising bind: despite AWS operating one of the world’s largest fleets of AI chips—including Nvidia H100s and custom Trainium processors—the company’s retail division was struggling with a severe GPU shortage. Over 160 AI-driven initiatives stalled due to a lack of access to compute, prompting the internal launch of Project Greenland, a centralized platform designed to improve GPU utilization across business units radically.
What Sparked Project Greenland
According to Business Insider (source), Amazon’s internal AI teams, particularly those in retail, were unable to access sufficient GPU capacity, even as AWS was scaling to over 250,000 H100-equivalent units globally. Projects in computer vision, search, and logistics were delayed due to a lack of planning, idle reservations, and inefficient internal allocation models.
In response, Project Greenland was started in July 2024 as a GPU orchestration system with strict allocation governance. It imposed disciplined, ROI-driven distribution of GPU resources, effectively bringing order to Amazon's previously chaotic internal resource management.
Key Features and Principles
Project Greenland centralizes GPU management and prioritizes high-impact, ready-to-deploy AI workloads. Key elements include:
Central GPU Pooling: All requests are routed through a centralized interface with full visibility.
Usage Monitoring: Utilization rates are tracked across divisions in near real-time.
Clawback & Reallocation: Underused GPUs are reallocated to projects showing higher value per chip.
ROI-Based Prioritization: Only projects demonstrating significant return on investment and delivery readiness receive priority access.
Amazon adopted eight guiding principles for GPU governance, pushing teams to justify their requests and demonstrate operational efficiency. This marked a significant cultural shift away from the earlier “land-grab” approach to hardware.
Infrastructure Context
Alongside Greenland, Amazon has been ramping up its infrastructure investments:
Over 360,000 Nvidia GB200 GPUs are expected to be procured in 2025.
Internal budgets for AWS services rose to $5.7 billion, with a focus on AI scalability.
Amazon’s Trainium2 hardware is also being deployed, though widespread benefits may not be realized until late 2025.
Despite these expansions, Project Greenland remains critical for ensuring the company’s existing AI hardware is fully optimized.
Results and Business Impact
By Q1 2025, Amazon declared its internal GPU shortage resolved. Greenland-enabled optimizations contributed:
$2.5 billion in incremental operating project returns
$670 million in variable cost savings in retail operations alone
As reported by Business Insider, these results came not only from increased access but also from redistributing underutilized resources and stopping low-priority projects from monopolizing valuable chips (source).
Lessons for the Industry
Amazon’s experience with Project Greenland highlights a crucial reality: even companies with massive infrastructure can face internal shortages without centralized resource governance. For businesses navigating their own GPU strategies—especially those in e-commerce, logistics, or AI startups—Amazon’s model offers a compelling template.
Additionally, when GPU hardware becomes surplus, obsolete, or poorly utilized, it makes sense to explore IT asset recovery. Services like Sell Graphics Card from BuySellRam.com help businesses recapture value from underused or decommissioned GPU inventory. With the high demand for AI compute, even used GPUs can hold significant resale value if adequately handled.
Project Greenland has positioned Amazon as not just a seller of cloud compute, but a disciplined internal consumer of it. The project stands as a case study in effective AI hardware management, blending infrastructure investment with centralized orchestration and ROI accountability. As AI demand continues to rise across industries, strategies like Greenland—and services that support GPU lifecycle management—will only grow more relevant.
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