Observed Signal · Apr 7, 2026 · Contract Expansion · Source: techcrunch · Impact: 4/5 · Sentiment: Positive
Uber Expands AWS Use, Trials Amazon Trainium3 Chips
Uber is expanding its AWS cloud contract to run more ride‑sharing features on Amazon-designed chips. The company will increase use of AWS Graviton (an ARM-based, low-power server CPU) and begin a trial of Trainium3, AWS’s in-house AI accelerator positioned as an alternative to Nvidia. The move follows Uber’s 2023 multi-year cloud deals with Google and Oracle to move infrastructure off its own data centers. Amazon has highlighted that its custom chips have attracted major customers — including Anthropic, OpenAI and Apple — and CEO Andy Jassy has said Trainium is already a multibillion-dollar business.
Major cloud provider (AWS) winning expanded contracts and promoting proprietary AI chips affects cloud infrastructure competition and AI inference capacity that underpins advertising and martech platforms.
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Key Takeaways & Evidence Grounding
- Uber is expanding its AWS contract to run more ride‑sharing features on Amazon’s chips.
- Uber will expand use of AWS Graviton (ARM-based server CPU) and start a trial of Trainium3, AWS’s AI chip.
- Uber previously signed multi-year cloud deals with Oracle and Google in 2023 to migrate infrastructure off its own data centers.
- Amazon cites other major customers using its chips, including Anthropic, OpenAI and Apple; CEO Andy Jassy said Trainium was a multibillion-dollar business as of December.
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AWS Exploring Sale of Trainium AI Chips to Third Parties
Amazon Web Services is in early talks to sell its in-house AI chip Trainium to other companies for data-center use, AWS AI chief Peter DeSantis told Bloomberg. Amazon says discussions are preliminary and DeSantis declined to name potential buyers. The idea follows Amazon CEO Andy Jassy’s shareholder letter estimating that a standalone chips business selling to AWS and third parties could have an annual run rate of about $50 billion. AWS has historically prioritized serving its own cloud customers and says current Trainium capacity — and capacity for the next-generation Trainium4 — has sold out; Trainium4 is not expected to be available for over a year. AWS would likely need surplus manufacturing via partners such as TSMC to sell chips externally. AWS spokesperson Doron Aronson also confirmed the company may sell racks of its chips to third parties in the future.
Inside AWS Trainium Lab: Amazon's AI Chip Push
TechCrunch toured AWS’s Austin chip lab where Amazon develops its Trainium AI chips. The article describes Trainium’s evolution from training-focused silicon to inference-optimized processors, the deployment of roughly 1.4 million Trainium chips across three generations, and Amazon’s commitments under a recent OpenAI deal that include supplying 2 gigawatts of Trainium capacity. It highlights Trainium3 (a 3nm chip produced by TSMC) and related system innovations — Neuron switches and Trn3 UltraServers — that AWS says cut inference cost for comparable workloads. The piece notes major customers (Anthropic, OpenAI), AWS partnerships (Cerebras Systems), PyTorch support to ease migration from Nvidia GPUs, and operational details from the lab’s engineers about chip “bring-up,” sleds, and liquid cooling.
Amazon’s Trainium3: Rack‑Level Parity and Trainium4 Plan
This newsletter deep-dive explains why Amazon’s quietly developed custom silicon and infrastructure may be an undervalued AI strategic asset. AWS launched Trainium3 (first 3nm AWS ML accelerator) and a liquid-cooled Trn3 UltraServer rack that packs 144 Trainium3 chips to deliver ~0.36 ExaFLOPS FP8 (rising to 1.4 ExaFLOPS with structured sparsity). Trainium3 chips are specified at ~2.5 petaFLOPS FP8 with 144 GB HBM3E and ~4.9 TB/s bandwidth; AWS claims 30–40% better price-performance vs comparable Nvidia instances and lower power draw (~1,000W vs ~1,400W). Anthropic and several production customers use Trainium. AWS acknowledges software and ecosystem gaps vs CUDA and plans to open-source PyTorch backend/compilers. AWS also announced Trainium4, expected late 2026, which will support NVLink Fusion to enable mixed racks of Trainium, Graviton and Nvidia GPUs and promise large FP8/FP4 and memory improvements. The piece frames Annapurna Labs’ 2015 acquisition as foundational to this capability.
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