Positron AI
Positron AI is an AI Accelerators company founded in 2023 and based in Reno, USA. It has raised $305.1M in total funding, most recently a Series B in 2026 at a $1B valuation.
Founded in spring 2023 by Thomas Sohmers (Thiel Fellow, CTO), Edward Kmett (Chief Scientist), and Barrett Woodside (VP of Product) in Reno, Nevada, Positron AI is building a vertically integrated inference hardware stack to challenge Nvidia on the specific workload GPUs were never designed for: autoregressive LLM token generation. The company's flagship Atlas system is manufactured in the United States (Arizona) and claims to use less than a third of the power of an H100 while matching its throughput — positioning it for cost-sensitive, power-constrained inference deployments across cloud providers, networking, gaming, and content moderation. In February 2025 former Lambda COO Mitesh Agrawal joined as CEO to lead commercial scaling. The company raised a $23.5M seed in February 2025, a $51.6M oversubscribed Series A in July 2025 (led by Valor Equity Partners, DFJ Growth, and Atreides Management), and a $230M Series B in February 2026 at a post-money valuation above $1B — achieving unicorn status 34 months after founding. The Series B was co-led by ARENA Private Wealth, Jump Trading, and Unless, with strategic participation from Arm and the Qatar Investment Authority. Proceeds fund tape-out of Asimov, Positron's next-generation custom silicon targeting five times the energy efficiency of Nvidia's Rubin architecture, with production slated for early 2027. The follow-on Titan system will offer up to 2 TB of directly attached high-speed memory per accelerator, enabling single-system inference of models up to 16 trillion parameters.
Positron is one of the few post-seed AI chip startups that has shipped silicon to production data centers at scale, not just taped out a demo chip. Its $230M unicorn round — co-led by quantitative trading firms with deep inference cost sensitivity — suggests real commercial traction, and its Asimov roadmap directly targets Nvidia's next Rubin generation on efficiency metrics that matter most at inference scale: tokens per watt and tokens per dollar.
