A fast growing semiconductor startup has raised fresh capital as global demand for alternative AI chips continues to rise. Positron has secured $230 million in a Series B funding round, according to sources familiar with the deal. The funds will be used to speed up the rollout of its high speed memory chips, which play a key role in powering AI workloads.
Investors in the round include Qatar Investment Authority, the country’s sovereign wealth fund, which has been stepping up its focus on AI infrastructure. Sources said Qatar sees computing power as a strategic asset and is pushing to build large scale “sovereign” AI infrastructure. This approach was highlighted again during the Web Summit Qatar held in Doha this week, where the country positioned itself as a future AI services hub for the Middle East.
The funding comes at a time when hyperscalers and major AI firms are actively looking to reduce their dependence on Nvidia, the long standing leader in AI chips. OpenAI, one of Nvidia’s biggest customers, has reportedly been unhappy with some of the company’s recent AI chip offerings and has been exploring alternatives since last year. This shift has created new opportunities for startups like Positron to enter the market.
Qatar’s broader AI push is already visible through major investments, including a $20 billion AI infrastructure joint venture with Brookfield Asset Management announced in September. Positron’s latest funding brings its total capital raised to just over $300 million. The Reno based company earlier raised $75 million last year from investors including Valor Equity Partners, Atreides Management, DFJ Growth, Flume Ventures and Resilience Reserve.
Positron says its first generation chip Atlas, manufactured in Arizona, can match the performance of Nvidia’s H100 GPUs while using less than one third of the power. The company is focused on inference, which is the computing required to run AI models in real world use, rather than training large language models. This focus aligns with rising demand as businesses move from building AI models to deploying them at scale.
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