AI News · $100M+ rounds ·
Tenstorrent raises $1.5 billion at a $6.2 billion valuation for inference chips
AI chip startup Tenstorrent raised $1.5 billion at a $6.2 billion valuation, more than double the $2.6 billion it reached in late 2024. The round was led by Trace Capital and Innovation X, with Fidelity, T. Rowe Price, Eclipse, Cadence Ventures and SanDisk also investing. The money will go toward scaling chips for serving open-weight AI models and video generation.
Key points
- Tenstorrent raised $1.5 billion at a $6.2 billion valuation.
- Trace Capital and Innovation X led the round, joined by Fidelity and T. Rowe Price.
- Funds will scale chips for serving open-weight models and video generation.
What happened: Tenstorrent, a chip startup focused on AI inference, raised $1.5 billion at a $6.2 billion valuation, TechCrunch reported. The valuation has more than doubled from the $2.6 billion the company reached in late 2024.
The details: The round was led by Trace Capital, a new firm founded by Quiet Capital co-founder Ben Mahdavi, and Innovation X. Other investors include Cadence Ventures, Eclipse, Fidelity, SanDisk and T. Rowe Price. The company said the new funding will go toward scaling chips for serving open-weight AI models and for video generation.
Background: The raise came five months after reports that Intel and Qualcomm had shown acquisition interest at valuations of up to $5 billion. CEO Jim Keller told Bloomberg that the company chose to stay independent and raise more capital after its Galaxy servers showed they could run DeepSeek models. The report did not say whether Intel or Qualcomm made formal offers, and Tenstorrent has not disclosed how the new capital will be split between chips and servers.
What to watch: Tenstorrent is one of several hardware startups challenging Nvidia's dominance in AI chips. TechCrunch noted that Etched was reportedly fielding investment offers at valuations of $40 billion or more earlier this month. Tenstorrent has not disclosed revenue figures or specific customer deployments in the report.
Our take
More funded alternatives to Nvidia for running open-weight models could widen hardware and pricing options for teams that serve their own models.