How Amazon became one of the world’s top chip companies in a decade

Amazon's custom chip business, spanning Trainium and Graviton, has exceeded a $25 billion annual revenue run rate with triple-digit year-over-year growth, according to Amazon CEO Andy Jassy. Trainium3 delivers up to 40% better price-performance than Trainium2, and Graviton serves 98% of the top 1,000 EC2 customers with up to 40% better price-performance. The growth is driven by multi-year, multi-gigawatt commitments from Anthropic and OpenAI to use Trainium.

Key takeaways - Amazon's custom chips business exceeded a $25 billion annual revenue run rate with triple-digit growth year over year. - Trainium3 delivers up to 40% better price-performance than Trainium2. - Graviton serves 98% of the top 1,000 EC2 customers with up to 40% better price-performance. When Amazon acquired chip design company Annapurna Labs https://www.aboutamazon.com/news/aws/take-a-look-inside-the-lab-where-aws-makes-custom-chips in 2015, the AI boom was still years away. The bet was straightforward: if you design chips specifically for cloud workloads rather than relying on general-purpose hardware, you can deliver better performance at lower cost. A different bet on AI infrastructure A decade later, that bet has become one of the fastest-growing businesses in Amazon’s history. Amazon’s chip business https://www.aboutamazon.com/what-we-do/amazon-custom-chips —spanning Trainium https://www.aboutamazon.com/stories/ai-chips-aws-Trainium2-explain for AI training and inference and Graviton https://www.aboutamazon.com/news/aws/what-is-aws-graviton for general cloud computing and increasingly, agentic AI —all based on the Nitro System for security and networking—recently exceeded a $25 billion annual revenue run rate, growing triple-digit percentages year-over-year. We're unusually well-positioned for this AI inflection given our leading price-performance chips in both AI with Trainium and CPUs with Graviton . Andy Jassy Amazon CEO Why Amazon builds its own AI chips The AI chip https://www.aboutamazon.com/news/aws/ai-chip-terms-explained market hasn’t historically been in a hurry to lower costs. Market leaders rarely disrupt themselves. Amazon chose a different path.Rather than relying solely on off-the-shelf processors, Amazon designs chips from the ground up https://www.aboutamazon.com/news/aws/aws-trainium-graviton-ai-chips-explained for specific workloads. The approach is vertically integrated: hardware and software engineers collaborate from chip design through server deployment, working backwards from the system to create silicon tailored for the workloads customers actually run.The result is three chip families that serve distinct but complementary roles: - Trainium is purpose-built for AI training and inference—the computationally intensive work of teaching AI models and then running them at scale. - Graviton handles general cloud computing—the websites, applications, databases, and increasingly, the agentic AI workloads that power modern software. Graviton is used by 98% of our top 1,000 EC2 https://aws.amazon.com/pm/ec2 customers, and revenue commitments have increased nearly three times quarter over quarter. Graviton5 https://www.aboutamazon.com/news/aws/aws-graviton-5-cpu-amazon-ec2 , the latest generation, delivers up to 25% better performance than its predecessor, and is growing nearly two times faster than Graviton4 did. - Nitro powers the networking, storage, and security behind AWS cloud infrastructure. As CEO Andy Jassy noted on the company’s Q2 2026 earnings https://www.aboutamazon.com/news/company-news/amazon-earnings-q2-2026-report call: "Our Chips revenue run rate is now over $25 billion. We're unusually well-positioned for this AI inflection given our leading price-performance chips in both AI with Trainium and CPUs with Graviton . In addition to the two leading AI labs in the world in Anthropic and OpenAI making multi-year, multi-gigawatt commitments to Trainium, an increasing number of AI start-ups are also adopting Trainium.” What Amazon’s chips mean for AI customers For companies building with AI, the economics of chips matter enormously. Training a frontier AI model can require hundreds of thousands of chips running for weeks or months. Running that model at scale—answering millions of queries per day—demands efficient inference infrastructure. Even small improvements in price-performance translate into significant savings at scale. That’s why the world’s leading AI labs and tech companies are making major commitments to Amazon’s silicon: Anthropic has committed https://www.aboutamazon.com/news/company-news/amazon-invests-additional-5-billion-anthropic-ai to using up to five gigawatts of current and future generations of Trainium chips to train and power its Claude models, which run on more than one million Trainium2 chips. Anthropic is also using tens of millions of Graviton cores to deliver scalable performance and cost efficiency across a broad range of generative AI workloads. OpenAI has committed https://www.aboutamazon.com/news/aws/amazon-open-ai-strategic-partnership-investment to consuming two gigawatts of Trainium capacity through AWS infrastructure to power its frontier models, beginning in 2027. Meta has signed https://www.aboutamazon.com/news/aws/meta-aws-graviton-ai-partnership an agreement to deploy tens of millions of Graviton cores to power the CPU-intensive workloads behind its agentic AI efforts. Uber is using https://www.aboutamazon.com/news/aws/aws-uber-ai-trainium-graviton Graviton to match riders with drivers in fractions of a second and piloting Trainium3 to train the AI models that make every ride smarter. AI chips built to meet customer demands at unprecedented scale Amazon Bedrock https://aws.amazon.com/bedrock/ , the company’s managed AI service used by hundreds of thousands of customers, runs most of its inference on Trainium. An increasing number of AI start-ups are also adopting Trainium as well, including unicorns like Neura Robotics which chose Trainium for physical AI and Odyssey, which chose Trainium to build world models that simulate physics, getting nearly twice the useful compute per dollar compared to alternatives. They join other startups like TwelveLabs, DeCart, Poolside, Karakuri, Matagenomi, NetoAI, and Splash music, as well as larger companies Uber, and On the Graviton side, more than 130,000 customers use Graviton-based servers today. Over half of all new processing power added to AWS https://www.aboutamazon.com/what-we-do/amazon-web-services runs on Graviton chips.The demand reflects a shift in how AI infrastructure gets built. As AI systems move from answering questions to taking actions—real-time reasoning, code generation, and multi-step task orchestration—the compute required pulls heavily on both AI accelerators and CPUs. Amazon is positioned across both. But individual chips are only part of the story. Amazon connects them into increasingly powerful systems: Trn3 UltraServers https://aws.amazon.com/ec2/instance-types/trn3/ pack up to 144 Trainium3 chips into a single integrated system, delivering up to 4.4 times more compute performance than Trainium2 UltraServers. This allows customers to train models in weeks instead of months. Project Rainier https://www.aboutamazon.com/news/aws/aws-project-rainier-ai-trainium-chips-compute-cluster is the world’s largest AI computing cluster, purpose-built for training frontier models at scale. Project Rainier is running Anthropic's Claude models.- Energy efficiency: Trainium3 delivers over five times higher output tokens per megawatt than Trainium2—efficiency that matters at scale for both cost and environmental impact. AI, silicon, and quantum: The future of Amazon’s chips business Amazon’s chip roadmap continues to accelerate. Trainium4 is already in development. Graviton continues to evolve for the agentic AI era. And under Peter DeSantis, who now leads a new organization spanning AI models https://www.aboutamazon.com/news/innovation-at-amazon/peter-desantis-amazon-artificial-general-intelligence , custom silicon, and quantum computing, the company is integrating these technologies so they reinforce each other.“One of the ways that we can give ourselves an advantage in how we build these models is by using our deep investments in chips to deliver both performance and cost efficiency that will allow us to differentiate our model development,” said Peter DeSantis, SVP, Amazon AI Models, Chips, & Quantum Computing. It’s a strategy that bets on a simple premise: the companies that control their own silicon will set the pace for AI. With a chip portfolio spanning training, inference, and general computing, and a roadmap that’s already demanding customer interest years into the future—Amazon is building the infrastructure layer that