Insider Brief
- Q.ANT partnered with IONOS to bring photonic AI computing into a commercial cloud environment.
- The collaboration will make Q.ANT’s photonic Native Processing Server available through IONOS infrastructure for enterprise AI workloads.
- Q.ANT said its photonic co-processors aim to improve AI computing efficiency and reduce energy consumption compared with conventional processor architectures.
PRESS RELEASE — Q.ANT, a pioneer in commercial photonic computing, today announced a collaboration with IONOS — a major European cloud and hosting provider serving approximately 6.8 million customers across 17 markets in Europe and North America — to make Q.ANT’s Native Processing Server (NPS) accessible to commercial customers through IONOS’ cloud infrastructure and customer ecosystem. The agreement brings photonic AI acceleration into a commercial cloud environment, following earlier production deployments at two of Europe’s leading supercomputing centers.
The deployment combines IONOS’ cloud infrastructure expertise with Q.ANT’s photonic processor technology, marking a commercial inflection point as hyperscalers and enterprises race to meet AI’s mounting energy demands. By 2027, individual server racks in advanced data centers could draw as much electricity as 65 households, and data center electricity demand could reach 3% of total global energy consumption by 2030. Q.ANT’s photonic processor improves energy efficiency and processing power by replacing electrons with light.
A Co-Processor for the GPU Era
At the heart of the NPS is Q.ANT’s second-generation Native Processing Unit (NPU), a photonic AI accelerator built on the company’s proprietary Thin-Film Lithium Niobate (TFLN) platform. In independent evaluation at Germany’s Leibniz Supercomputing Centre (LRZ), the second-generation NPU demonstrated up to a 50x performance increase over Q.ANT’s first generation. Q.ANT’s internal benchmarking further indicates the NPU can deliver up to 30x higher energy efficiency and up to 50x greater performance per application versus conventional processors.Critically for an AI market built around GPUs, Q.ANT positions the NPU not as a GPU replacement, but as a co-processor that works alongside existing GPU infrastructure for specific AI workloads — a pragmatic path to adoption rather than a rip-and-replace bet.
“AI infrastructure cannot scale on its current trajectory — power, cooling, and silicon economics will not keep up,” said Dr. Michael Förtsch, founder and CEO of Q.ANT. “Photonic computing changes that math. With IONOS as our first commercial customer, we are proving Q.ANT’s processors in production as a co-processor to traditional GPUs and CPUs, opening the door for enterprises to access dramatically more energy-efficient AI computing.”
From Research to Commercial Applicability
Q.ANT’s technology is already installed and running in two of Germany’s leading high-performance computing research centers: Munich’s Leibniz Supercomputing Centre (LRZ) and the Jülich Supercomputing Centre (JSC). Those are research datacenter deployments; the planned IONOS rollout later this year marks Q.ANT’s first commercial customer — a provider that will make the technology accessible to its own business users — and a key milestone in the commercialization of Q.ANT’s photonic processors and servers.
From Pilot Line to Commercial Scale
Q.ANT manufactures its photonic chips at a pilot line in Stuttgart, Germany, operated jointly with IMS CHIPS, and is expanding commercial operations through its U.S. office in Austin, Texas, to serve North American AI infrastructure customers.
“The next wave of AI will require a fundamental rethink of compute infrastructure,” said Dr. Andreas Nauerz, Chief Product Officer at IONOS. “Q.ANT’s photonic processors represent a highly promising approach to dramatically improving the energy efficiency of AI workloads while enabling scalable AI growth. We are excited to explore this technology together and bring its benefits to customers.”
The agreement was announced at the re:publica 2026 digital conference in Berlin.
