Amazon’s public declarations of a deep, long-standing partnership with Nvidia represent a masterful piece of strategic diplomacy, yet they obscure a more critical reality unfolding within AWS. While relying on Nvidia’s market-leading GPUs to satisfy voracious near-term demand for AI model training, Amazon is executing a patient, long-game strategy centred on its own custom silicon. This dual approach is not a contradiction but a calculated response to the shifting economics of artificial intelligence, where the immense, recurring cost of inference, not training, will ultimately determine leadership in the cloud.
Key Takeaways
- Amazon’s strategy involves a necessary short-term reliance on Nvidia for high-performance training, coupled with a long-term focus on proprietary chips to win the inference market.
- The economics of AI are bifurcated; inference is projected to consume up to 90% of the total compute cost over a model’s lifecycle, making it the key battleground for cloud providers.
- Custom chips like Amazon’s Inferentia are not designed to outperform Nvidia on raw power but to deliver “good enough” performance for inference at a significantly lower cost and power consumption.
- This is an industry-wide trend, with Google (TPU) and Microsoft (Maia) also developing in-house silicon to control costs and optimise their cloud ecosystems.
- The endgame for Amazon is not to replace Nvidia, but to create a ceiling on AI compute costs within AWS, protecting its margins and locking in customers with a cost-effective, integrated hardware stack.
The Diplomatic Alliance
Andy Jassy’s reassurance of a “deep partnership with Nvidia for a long time” is, for now, a statement of fact born from necessity.1 Nvidia controls over 90% of the market for the powerful GPUs required for training large, complex foundation models.2 For Amazon Web Services (AWS) to remain competitive, it has no choice but to offer its clients access to the best tools available, which currently means Nvidia’s H100 and forthcoming Blackwell series chips. To do otherwise would be to cede the most demanding and highest-margin AI workloads to rivals like Microsoft Azure and Google Cloud.
This partnership is therefore a pragmatic and essential bridge. It allows AWS to capture the initial, capital-intensive wave of AI adoption, where enterprises are focused on building and training their models. However, viewing this as the final strategic position would be a profound misreading of Amazon’s intentions.
The Inference Imperative
The true long-term prize in the AI economy is not training, but inference. Training is a spectacular but relatively infrequent event. Inference, the process of using a trained model to generate predictions or content, is a constant, high-volume operational cost that scales directly with an application’s success. Some industry analyses suggest that over the lifetime of a mature AI model, inference can account for as much as 90% of the total compute expenditure.3
This is where Amazon’s custom silicon, particularly its Inferentia chips, becomes central to the strategy. While its Trainium chips are designed to offer a cost-effective alternative for training, the real economic leverage lies with Inferentia. These chips are purpose-built for high-throughput, low-latency inference. They do not need to match the sheer computational force of an Nvidia H100; they need to perform a narrower set of tasks with maximum efficiency and minimal power draw. For an AWS customer running a popular chatbot or code-generation tool, the cumulative cost savings from running on optimised Inferentia chips versus general-purpose (and expensive) GPUs could be substantial, directly impacting their gross margins and their loyalty to the AWS platform.
A Widening Silicon Moat
Amazon’s strategy is not occurring in a vacuum. The largest cloud providers have all recognised the existential risk of being entirely dependent on a single external supplier for a core component of their future growth. This has led to a costly but necessary parallel track of in-house silicon development.
| Company | Custom AI Chips | Primary Focus | Strategic Goal |
|---|---|---|---|
| Amazon (AWS) | Trainium, Inferentia | Cost-efficient training and inference | Reduce long-term operational costs for customers; protect AWS margins. |
| Google (GCP) | Tensor Processing Unit (TPU) | High-performance training and inference | Optimise for Google’s internal AI needs (Search, Ads) and offer a differentiated product on GCP. |
| Microsoft (Azure) | Maia (AI), Cobalt (CPU) | Optimising workloads for the Azure stack | Full-stack optimisation from silicon to software to reduce dependency and improve efficiency. |
This table illustrates a clear trend: hyperscalers are vertically integrating to control their own destiny. By designing chips tailored to their specific software and infrastructure, they can achieve performance-per-watt and performance-per-dollar metrics that a general-purpose chip from an outside vendor may struggle to match. For AWS, whose operating income is the primary driver of Amazon’s overall profitability, controlling these costs is not just a competitive advantage; it is a financial necessity.
Forward Guidance and A Testable Hypothesis
Investors should not interpret Amazon’s public deference to Nvidia as a sign of a dependant relationship. It is the intelligent management of a supplier who currently holds immense power. The key metrics to watch are not announcements of more Nvidia GPU availability on AWS, but the adoption rates of Trainium and Inferentia by AWS customers. Every major client that Amazon highlights as using its proprietary chips, such as Anthropic, is a validation of the strategy.4
Herein lies a speculative hypothesis: Amazon’s goal is not to kill Nvidia, but to domesticate it. By successfully funnelling a significant portion of the vast, recurring inference market onto its own low-cost silicon, AWS can effectively cap the price it is willing to pay for third-party inference hardware. This creates a powerful negotiating position and forces Nvidia to compete on price for inference workloads, a market segment that may prove less profitable than its current high-margin training dominance. The success of Amazon’s silicon gamble will not be measured by whether it builds a better GPU than Nvidia, but by whether it can fundamentally reshape the cost structure of running AI at scale.
References
- Jassy, A. (2024). Andy Jassy Touts ‘Deep Partnership’ With Nvidia, But Amazon Is Doubling Down On In-House Custom Silicon. *Benzinga*.
- Goldman Sachs. (2023). *The AI Revolution: Investing in the Future of Intelligence*. Retrieved from various financial news summaries of the report.
- Ark Invest. (2023). *Big Ideas 2023*. The report notes that AI inference costs could fall 70% annually, while training costs fall 47%, highlighting the growing importance and scale of inference.
- Amazon News. (2023, November). *AWS and Anthropic broaden strategic collaboration to advance generative AI*. Amazon.
- StockSavvyShay. (2024, November 1). *WE’LL HAVE A DEEP PARTNERSHIP WITH $NVDA FOR A LONG TIME,” SAYS $AMZN CEO ANDY JASSY But with inference demand exploding — Amazon is doubling down on custom training chips to power the next wave of AI*. Retrieved from https://x.com/StockSavvyShay/status/1856336966524252437
