'Project Greenland': How Amazon overcame a GPU crunch

• GPU shortages delayed projects in Amazon's retail division last year.
• The company created a more efficient approval and monitoring process for internal GPU requests.
• Amazon says it now has "ample" GPU capacity across the company.

Last year, Amazon's huge retail business had a big problem: It couldn't get enough AI chips to get crucial work done.

With projects getting delayed, the Western world's largest e-commerce operation launched a radical revamp of internal processes and technology to tackle the issue, according to a trove of Amazon documents obtained by Business Insider.

The initiative offers a rare inside look at how a tech giant balances internal demand for these GPU components with supply from Nvidia and other industry sources.

Early in 2024, the generative AI boom was in full swing, with thousands of companies vying for access to the infrastructure needed to apply this powerful new technology.

Inside Amazon, some employees went months without securing GPUs, leading to delays that disrupted timely project launches across the company's retail division, a sector that spans its e-commerce platform and expansive logistics operations, according to the internal documents.

In July, Amazon launched Project Greenland, a "centralized GPU capacity pool" to better manage and allocate its limited GPU supply. The company also tightened approval protocols for internal GPU use, the documents show.

"GPUs are too valuable to be given out on a first-come, first-served basis," one of the Amazon guidelines stated. "Instead, distribution should be determined based on ROI layered with common sense considerations, and provide for the long-term growth of the Company's free cash flow."

Two years into a global shortage, GPUs remain a scarce commodity —even for some of the largest AI companies. OpenAI CEO Sam Altman, for example, said in February that the ChatGPT-maker was "out of GPUs," following a new model launch. Nvidia, the dominant GPU provider, has said it will be supply-constrained this year.

However, Amazon's efforts to tackle this problem may be paying off. By December, internal forecasts suggested the crunch would ease this year, with chip availability expected to improve, the documents showed.

In an email to BI, an Amazon spokesperson said the company's retail arm, which sources GPUs through Amazon Web Services, now has full access to the AI processors.

"Amazon has ample GPU capacity to continue innovating for our retail business and other customers across the company," the spokesperson said. "AWS recognized early on that generative AI innovations are fueling rapid adoption of cloud computing services for all our customers, including Amazon, and we quickly evaluated our customers' growing GPU needs and took steps to deliver the capacity they need to drive innovation."

"Shovel-ready"

Amazon now demands hard data and return-on-investment proof for every internal GPU request, according to the documents obtained by BI.

Initiatives are "prioritized and ranked" for GPU allocation based on several factors, including the completeness of data provided and the financial benefit per GPU. Projects must be "shovel-ready," or approved for development, and prove they are in a competitive "race to market." They also have to provide a timeline for when actual benefits will be realized.

One internal document from late 2024 stated that Amazon's retail unit planned to distribute GPUs to the "next highest priority initiatives" as more supply became available in the first quarter of 2025.

The broader priority for Amazon's retail business is to ensure its cloud infrastructure spending generates the "highest return on investment through revenue growth or cost-to-serve reduction," one of the documents added.

Amazon's new GPU "tenets"

Amazon's retail team codified its approach into official "tenets" — internal guidelines that individual teams or projects create for faster decision-making. The tenets emphasize strong ROI, selective approvals, and a push for speed and efficiency.

And if a greenlit project underdelivers, its GPUs can be pulled back.

Here are the 8 tenets for GPU allocation, according to one of the Amazon documents:

1. ROl + High Judgment thinking is required for GPU usage prioritization. GPUs are too valuable to be given out on a first-come, first-served basis. Instead, distribution should be determined based on ROl layered with common sense considerations, and provide for the long-term growth of the Company's free cash flow. Distribution can happen in bespoke infrastructure or in hours of a sharing/pooling tool.
2. Continuously learn, assess, and improve: We solicit new ideas based on continuous review and are willing to improve our approach as we learn more.
3. Avoid silo decisions: Avoid making decisions in isolation; instead, centralize the tracking of GPUs and GPU related initiatives in one place.
4. Time is critical: Scalable tooling is a key to moving fast when making distribution decisions which, in turn, allows more time for innovation and learning from our experiences.
5. Efficiency feeds innovation: Efficiency paves the way for innovation by encouraging optimal resource utilization, fostering collaboration and resource sharing.
6. Embrace risk in the pursuit of innovation: Acceptable level of risk tolerance will allow to embrace the idea of 'failing fast' and maintain an environment conducive to Research and Development.
7. Transparency and confidentiality: We encourage transparency around the GPU allocation methodology through education and updates on the wiki's while applying confidentiality around sensitive information on R&D and ROI sharable with only limited stakeholders. We celebrate wins and share lessons learned broadly.
8. GPUs previously given to fleets may be recalled if other initiatives show more value. Having a GPU doesn't mean you'll get to keep it.

Project Greenland

To address the complexity of managing GPU supply and demand, Amazon launched a new project called Greenland last year.

Greenland is described as a "centralized GPU orchestration platform to share GPU capacity across teams and maximize utilization," one of the documents said.

It can track GPU usage per initiative, share idle servers, and implement "clawbacks" to reallocate chips to more urgent projects, the documents explained. The system also offers a simplified networking setup and security updates, while alerting employees and leaders to projects with low GPU usage.

This year, Amazon employees are "mandated" to go through Greenland to obtain GPU capacity for "all future demands," and the company expects this to increase efficiency by "reducing idle capacity and optimizing cluster utilization," it added.

$1 billion investment in AI-related projects

Amazon's retail business is wasting no time putting its GPUs to work. One document listed more than 160 AI-powered initiatives, including the Rufus shopping assistant and Theia product image generator.

Other AI projects in the works include, per the document:

• A vision-assisted package retrieval (VAPR) service that uses computer-vision technology to help drivers quickly identify and pick the correct packages from vans at delivery stops.
• A service that automatically pulls in data from external websites to create consistent product information.
• A new AI model that optimizes driver routing and package handling to reduce delivery times and improve efficiency.
• An improved customer service agent that uses natural language to address customer return inquiries.
• A service that automates seller fraud investigations and verifies document compliance.

Last year, Amazon estimated that AI investments by its retail business indirectly contributed $2.5 billion in operating profits, the documents showed. Those investments also resulted in approximately $670 million in variable cost savings.

It's unclear what the 2025 estimates are for those metrics. But Amazon plans to continue spending heavily on AI.

As of early this year, Amazon's retail arm anticipated about $1 billion in investments for GPU-powered AI projects. Overall, the retail division expects to spend around $5.7 billion on AWS cloud infrastructure in 2025, up from $4.5 billion in 2024, the internal documents show.

Improving capacity

Last year, Amazon's heavy slate of AI projects put pressure on its GPU supply.

Throughout the second half of 2024, Amazon's retail unit suffered a supply shortage of more than 1,000 P5 instances, AWS's cloud server that contains up to 8 Nvidia H100 GPUs, said one of the documents from December. The P5 shortage was expected to slightly improve by early this year, and turn to a surplus later in 2025, according to those December estimates.

Amazon's spokesperson told BI those estimates are now "outdated," and there's currently no GPU shortage.

AWS's in-house AI chip Trainium was also projected to satisfy the retail division's demand by the end of 2025, but "not sooner," one of the documents said.

Amazon's improving capacity aligns with Andy Jassy's remarks from February, when he said the GPU and server constraints would "relax" by the second half of this year.

But even with these efforts, there are signs that Amazon still worries about GPU supply.

A recent job listing from the Greenland team acknowledged that explosive growth in GPU demand has become this generation's defining challenge: "How do we get more GPU capacity?"

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