Analysis maps six AI chip camps: NVIDIA GPUs vs in-house ASICs
A new analysis compares the semiconductor strategies of six major tech players, highlighting the trade-offs between NVIDIA's high-power GPUs and custom in-house ASICs. The report notes that while NVIDIA's Blackwell Ultra generation reaches 1,400W per unit requiring liquid cooling, custom chips from Google, AWS, Meta, Microsoft, and OpenAI typically operate between 300W and 700W. This lower power…
Key points
- NVIDIA GPUs reach 1,400W TDP, while in-house ASICs operate between 300W and 700W.
- Google operates 3.8 million TPUs; AWS is scaling Trainium2 with Anthropic.
- Microsoft plans gigawatt-scale ASIC deployment from 2027; OpenAI validates 'Jalapeño'.
The analysis identifies a clear split in deployment maturity. Google and AWS are leading with large-scale operations, with Google running a fleet of 3.8 million TPUs and AWS accelerating Trainium2 deployment in partnership with Anthropic. Meanwhile, Meta, Microsoft, and OpenAI are in earlier stages, with Microsoft planning gigawatt-scale deployment from 2027 and OpenAI validating its 'Jalapeño' chip for reduced power consumption.
The report concludes that a hybrid model is emerging as the standard. NVIDIA GPUs remain essential for cutting-edge model training due to their software ecosystem, while ASICs handle routine inference tasks for established services. This division of labor allows companies to use peak computing power where needed and efficient hardware for repetitive calculations.
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[AI Analysis] A Quick Guide to the 6 Major AI Semiconductor Camps: The Power Map of In-House ASICs vs. NVIDIA Infrastructure
note.com · 29 October 2026
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