UK-based AI hyperscaler Nscale has raised $155m in a Series A funding round to drive the expansion of its AI infrastructure.
The oversubscribed funding round was led by Sandton Capital Partners, with support from Kestrel 0x1, Blue Sky Capital Managers, and Florence Capital.
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The company plans to use the proceeds to fuel expansion in Europe and North America.
The company is engaged in creating sustainable AI-ready data centres, deploying large-scale GPU infrastructure, and offering high-performance AI cloud services.
Nscale’s full-stack approach integrates data centre design with GPU supercluster infrastructure to optimise speed, performance, and efficiency for AI workloads.
The company’s AI private cloud platform is designed to support the full generative AI lifecycle, providing developers with the necessary tools to run and train AI models.
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By GlobalDataThe company plans to introduce a public cloud service in the first quarter of 2025. The service will offer access for developers to customised inference and training solutions.
Nscale CEO Joshua Payne said: “Nscale manages every layer of infrastructure in the value chain to meet the intensive needs of large-scale AI customers. In particular, the largest risk to the market’s ability to scale is the large contiguous tranches of electricity required to power these large GPU superclusters.
“Nscale has a 1.3GW pipeline of sites in our portfolio, which allows us to design from the ground up, the data centre, the supercluster and the cloud environment end-to-end for our customers. This means we can deploy bespoke GPU clusters tailored to customers’ requirements, do this faster than competitors and at any scale, all while delivering superior unit economics.
“With the support of our investors, we can now reach more customers globally with bespoke designed, sustainable and cost-effective AI infrastructure that unlocks new AI capabilities, products, and services.”
In December 2023, Nscale secured $30m in a seed round.
Recently, Nscale has partnered with Open Innovation AI to deploy 30,000 GPUs over three years.
