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Tenstorrent and the State of AI Hardware Startups

Dec 15, 2024 - irrationalanalysis.substack.com
The article discusses the author's experience and insights after attending Tenstorrent's Hot Chips 2024 presentation and subsequent meeting with the company's leadership. Initially skeptical, the author was impressed by Tenstorrent's technical capabilities and open-source approach, particularly their use of RISC-V architecture and focus on AI hardware. The article highlights Tenstorrent's potential in the AI hardware market, emphasizing their unique position as a high-performance RISC-V IP provider and their strategic use of Samsung Foundry SF4X to keep costs low. Despite challenges like latency and competition from established players like Nvidia, the author believes Tenstorrent is well-positioned to succeed in the AI inference market.

The article also critiques the broader AI hardware startup landscape, noting the difficulties these companies face in competing with established giants and semi-custom solutions from major tech companies. The author argues that Tenstorrent stands out as a promising investment due to its innovative architecture, strong leadership, and commitment to open-source development. Additionally, the article briefly mentions other startups like SambaNova and Positron, highlighting their unique approaches and potential in the AI hardware space.

Key takeaways:

  • Tenstorrent is positioned as a promising AI hardware startup, focusing on open-source development and leveraging a unique architecture that combines RISC-V CPU cores with AI cores.
  • The company is not currently facing significant customer demand for mixed AI workloads, but it remains hopeful due to its strong CPU microarchitecture team and open-source strategy.
  • Tenstorrent's valuation is considered reasonable, especially in comparison to ARM and other AI hardware startups like Cerebras, due to its potential in the RISC-V IP market.
  • The article emphasizes the challenges AI hardware startups face in competing with established players like Nvidia and semi-custom solutions, suggesting a focus on inference rather than training for future success.
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