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Valeo presents aluminum cooling technology for data centers in California. Valeo will present the validation status of its aluminum heat exchanger technology for data center thermal management with OVH Cloud at the Open Compute Project Global Summit 2026 in California. The technology is designed for rear-door cooling modules of racks or fan walls in data center white space. According to Valeo, its aluminum solution delivers a 38% increase in cooling capacity and 65% higher power density compared with traditional copper solutions, while reducing fan electrical consumption by 25%. The company says the technology can be used for retrofitting existing racks or for new racks. Valeo has implemented accelerated long-run testing to assess reliability and aluminum corrosion, while OVH Cloud has conducted tests under real-time conditions for several months. Testing of Valeo's specific coating has reached the equivalent of more than 10 years of operating life, and Valeo is continuing tests up to 15 years. The aluminum heat exchanger is part of Valeo's wider liquid cooling portfolio. This includes Direct-to-Chip systems in single-phase and two-phase configurations for high-density racks, as well as Zero-Water Immersion Units for Micro-Edge compute applications. "Over the past two years, we have made significant progress in evaluating how aluminum liquid cooling can meet the rigorous reliability and performance standards required by modern high-density facilities," said Pascale Herman, Data center & BESS Vice President at Valeo. "By combining these early testing metrics with our decades of automotive thermal management expertise, we are working to develop scalable thermal solutions to support the industry's evolving infrastructure needs responsibly and effectively." Topics: HVAC Was this helpful to you?

Valeo collaborates with NVIDIA to develop autonomous driving compute platforms. * By MT Bureau * October 22, 2026 French tier 1 supplier Valeo has announced plans to develop and industrialise high-performance compute platforms utilising the NVIDIA Hyperion reference architecture and NVIDIA Halos safety system. The initiative combines Valeo's vehicle integration capabilities with centralised computing architectures to support software-defined mobility systems. The platform utilises the NVIDIA DRIVE AGX Thor architecture to deliver processing performance and redundancy for Level 4 autonomous driving, commercial trucking and robotaxi applications. The development framework enables automakers to integrate features and deploy autonomous technologies across vehicle models. Marc Vrecko, Chief Executive Officer, Valeo Brain Division, said, "Our work with NVIDIA marks a significant milestone in our mission to make autonomous driving a safe, scalable reality. By seamlessly integrating NVIDIA's advanced compute power into our next-generation platforms, we are delivering the flexible, centralised intelligence vehicle manufacturers require to redefine the driving experience and meet the evolving demands of tomorrow's intelligent vehicles." Rishi Dhall, Vice-President of Automotive, NVIDIA, said, "Valeo's expertise in high-performance computing, vehicle integration and automotive-scale industrialisation will help automakers adopt NVIDIA Hyperion and NVIDIA Halos at production scale. Together, NVIDIA and Valeo are providing the AI performance, functional safety and scalability needed to accelerate the development of Level 4 autonomous vehicles and robotaxis."

Valeo and KE:SAI join forces to develop open AI driving system. The collaboration aims to release code, trained models and datasets for a generalist AI driving system, giving automakers, suppliers and researchers a shared platform for benchmarking and development. Prefer on Google Valeo has partnered with KE, a non-profit research lab founded by Kyutai and the ELLIS Institute Tübingen, to develop a generalist artificial intelligence driving system and release its code, trained models and datasets openly. The project aims to establish a reproducible reference system that automakers, automotive suppliers and researchers can use to develop, evaluate and benchmark automated-driving technologies. The partners plan to match and eventually surpass leading results on major public driving benchmarks. Discover more electric vehicle Electrified The initiative addresses the limited availability of open driving models. According to the partners, most automated-driving systems used in production remain proprietary, restricting automakers' ability to inspect, modify and independently reproduce the underlying software. Developing a generalist driving model. The collaboration brings together Valeo's Paris-based research laboratory, valeo.ai, and the Tübingen-based KE team, which has experience with driving datasets and benchmarks including KITTI and NAVSIM. Valeo's research work includes the DrivoR and VaVAM models. The new project aims to build on this research to develop a compact model capable of operating across different vehicles, sensor configurations and driving environments. The partners intend to make the resulting system and its development resources available to the wider research and automotive communities. This approach would allow external teams to examine the technology, reproduce experiments and compare their own systems against a common reference. Training with real-world data and simulation. The development process will begin with training on real-world driving data. The partners then plan to use simulation-based reinforcement learning and learned world models to expose the system to a wider range of driving situations. Simulation is intended to help address a limitation of recorded driving data: rare and safety-critical events may occur too infrequently in real-world datasets to provide sufficient examples for training. Discover more By generating and exploring additional scenarios, the research team aims to improve the model's ability to respond to varied conditions. However, performance on research benchmarks alone would not establish that a system is ready for deployment in production vehicles. Marc Vrecko, CEO of Valeo Brain, said: This open-science collaboration with KE marks a strategic milestone for Valeo and the broader European automotive ecosystem. By combining its deep industrial and technological expertise in autonomous driving with world-class open-source research, EVMagz is establishing a shared foundation to accelerate the development of physical AI and democratize next-generation self-driving technologies. For language models, open source compressed years of progress into months, yet frontier physical AI is still largely closed. Valeo shares its belief that open foundations create more innovation than any closed system ever could. Everything EVMagz develop will be open. Every step forward is a step forward for everyone. Open research and automotive independence. The project could provide European automakers and suppliers with greater access to the underlying technology used in automated-driving development. Open code, model weights and datasets can allow organizations to evaluate systems independently rather than relying exclusively on externally controlled software platforms. For European manufacturers, this approach may also support efforts to maintain greater control over strategic automotive technologies. Access to open models could give research teams more flexibility to adapt software to their own vehicles, sensors and computing platforms. Discover more Electrify America Charging Electrify America Valeo's participation also connects open AI research with an established automotive supplier's industrial expertise. While the model layer would be made available openly, companies would still need to develop and validate the hardware, integration and vehicle-level systems required for commercial use. Benchmark performance is only one step. The partners' objective to match and surpass leading public benchmarks provides a measurable target for the research. Benchmarks such as NAVSIM can help researchers compare driving-system performance under standardized conditions. However, benchmark results do not replace production validation. Automakers adopting open models would still need to assess system reliability, performance across operating conditions, functional safety, regulatory compliance and liability before deploying the technology in vehicles. The collaboration's practical impact will therefore depend not only on its benchmark results but also on the quality and usability of its public releases, the extent of independent adoption and the ability of developers to translate research results into validated automotive systems. The partners have positioned the initiative as an open foundation for further research and development. Specific release dates, model specifications and benchmark targets have not been detailed in the supplied announcement.