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Google in talks with Marvell to build new AI chips for TPUs, aiming to rival Nvidia GPUs

u/callsonreddit · Reddit — r/wallstreetbets · April 19, 2026 at 16:14 · ⬆ 111 pts · 💬 34 comments  | View on Reddit ↗
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Summary

  • The post shares a news report indicating that Google is in discussions with Marvell Technology to develop two new custom AI chips, including a memory processing unit and a new TPU.
  • The strategic goal for Google is to make its TPUs a more efficient and viable alternative to Nvidia's GPUs for AI inference, boosting its cloud revenue.
  • Quality assessment: This is a news catalyst/report (citing The Information/Reuters) rather than original DD, but it contains highly actionable fundamental information regarding hyperscaler semiconductor supply chains.
Score 111
Comments 34
Upvote % 97%
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Ideas
u/callsonreddit Reddit r/wallstreetbets
Google is in talks with Marvell to design two new AI chips, including a memory processing unit and a new TPU. Securing a custom silicon (ASIC) design win with a major hyperscaler like Google represents a massive future revenue stream and validates Marvell's custom compute capabilities. Go long on Marvell as it captures a larger share of the hyperscaler custom AI chip market. The report is currently unverified; talks could fall through or face design delays.
u/callsonreddit Reddit r/wallstreetbets
Google is aggressively developing custom silicon to make its TPUs a viable alternative to Nvidia GPUs. Vertically integrating AI hardware lowers inference costs, improves margins, and makes Google Cloud more attractive to enterprise AI customers. Long Google as it improves its AI infrastructure ROI and reduces reliance on expensive third-party GPUs. Custom silicon development is capital intensive and may still lag behind Nvidia's rapid innovation cycle.
u/callsonreddit Reddit r/wallstreetbets
Google is partnering with Marvell to build chips specifically aimed at rivaling Nvidia's dominant GPUs. As hyperscalers successfully develop and deploy their own custom silicon (TPUs, ASICs), it poses a long-term threat to Nvidia's pricing power and market share in AI inference. Monitor Nvidia's market share in hyperscaler inference as custom silicon alternatives mature. Nvidia's CUDA ecosystem moat remains incredibly strong, and custom chips often fail to match their general-purpose performance.
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