TensorWave raises $350M in Series B funding led by AMD and Magnetar Capital
The AMD-exclusive cloud provider hits a $1.55 billion valuation as investors bet on an alternative to Nvidia's dominance in AI infrastructure.
TensorWave, a Las Vegas-based AI cloud provider that has built its entire business around AMD chips, just closed a $350 million Series B round. The raise values the company at $1.55 billion, roughly tripling its implied valuation from just over a year ago.
AMD Ventures and Magnetar Capital co-led the round, with participation from Maverick Silicon, Nexus Venture Partners, and Western Frontier. Total funding for the company now sits at approximately $493 million since its founding in 2023.
The anti-Nvidia play
Founded by CEO Darrick Horton, a Forbes 30 Under 30 alum, and co-founder Jeff Tatarchuk, TensorWave has committed exclusively to AMD’s Instinct GPU lineup. That includes the MI300X, MI325X, and MI355X series, chips that AMD has positioned as direct competitors to Nvidia’s data center GPUs.
The company has already deployed 8,192 MI325X GPUs across its infrastructure. The $350 million will go toward expanding global data center capacity and scaling that AMD-powered infrastructure, with a particular focus on memory-intensive AI workloads. AMD’s MI300X chips carry 192GB of HBM3 memory, a spec that has caught the attention of cost-conscious AI teams.
A rapid funding trajectory
TensorWave’s ascent has been unusually fast, even by AI startup standards. The company raised a $43 million seed round in October 2024, then followed up with a $100 million Series A in May 2025. Now, barely a year after that Series A, it has secured $350 million more.
In February 2026, TensorWave established a partnership with Credo, a networking technology company, to improve network reliability for large-scale GPU clusters.
AMD’s direct investment through its venture arm is also notable. It signals that AMD views TensorWave as more than just a customer, but as a strategic channel for proving that AMD silicon can compete at scale in production AI workloads.
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