Alphabet (NASDAQ: GOOGL) Ironwood TPU vs. AWS Trainium2 Project Rainier: Who Wins Anthropic's 1M-Chip AI Cloud?
1 min readWritten by a reader
To train and serve frontier Claude reasoning models without relying solely on merchant GPUs, Anthropic splits multi-gigawatt workloads between AWS's (NASDAQ: AMZN) Project Rainier—clustering over 1 million custom Trainium2 chips—and Alphabet's (NASDAQ: GOOGL) 7th-generation Ironwood TPUs delivering 42.5 FP8 ExaFLOPS per 9,216-chip pod.
Per SEC Form 10-K filings and U.S. NIST AI Safety benchmarks, Google's liquid-cooled Ironwood (TPU7x, co-designed with Broadcom NASDAQ: AVGO on TSMC NYSE: TSM nodes) packs 192 GiB of HBM per chip at 7,380 GB/s bandwidth linked via Optical Circuit Switches (OCS). Simultaneously, Amazon's Project Rainier EC2 UltraCluster scales Trainium2 (built with Marvell NASDAQ: MRVL) toward a 5 GW infrastructure footprint.
By contracting up to 1 million+ Google TPUs alongside its AWS Project Rainier backbone, Anthropic proved that frontier Mixture-of-Experts (MoE) and agentic coding models can run heterogeneously across rival cloud ASICs. Which custom silicon architecture will achieve the lowest per-token inference TCO by 2027?
Which custom cloud ASIC will win Anthropic's multi-gigawatt AI stack?
- Google Ironwood TPUs win MoE inference with 42.5 ExaFLOPS pods0 %
- AWS Project Rainier's 1M+ Trainium2 cluster wins training scale0 %
- Depends on Optical Circuit Switching yield and per-token TCO0 %
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