NVIDIA Aims to Out Innovate Competition and Counter Energy Constraints with Silicon Photonics

The U.S. declaration of a national energy emergency justified by seeking to power the next generation of technology growth and signing several executive orders to realign federal energy priorities in early 2025 has elevated the discourse around energy limitations for data center growth to being part of international public discourse. Power consumption demands from Artificial Intelligence (AI) data centers, which are novel facilities with optimized and accelerated computing and networking infrastructure designed for AI processing also referred to as AI factories by NVIDIA, continue to outgrow efficiency benefits, e.g., from hyper-effective cooling solutions. NVIDIA heads off industry concerns about rack power and scaling requirements of announced Rubin Ultra Graphics Processing Units (GPUs) requiring 600 Kilowatts (kW) of power per rack, by showcasing CPO with its Spectrum-X Photonics Ethernet and Quantum-X Photonics InfiniBand networking platform to be released in 2H 2026 and 2H 2025, respectively, during NVIDIA’s GTC 2025 conference. Finding efficiencies around energy, performance, cost, operational, space, and security remains a key focus and potential disruption throughout the ecosystem, highlighted by compute-efficient AI model development like DeepSeek. Despite leading hyperscalers continuously dedicating record levels of Capital Expenditure (CAPEX) investments in AI hardware, power management, and across utilities and storage providers into the grid to optimize power consumption in AI infrastructure, limitations to abundant, reliable, and affordable electricity at scale persist.

NVIDIA’s entry has added momentum behind CPO by spearheading where silicon photonics could fit into modern systems design and showcasing how optically-linked GPU systems could shape out. So far, energy constraints continue to be defined by and shape the parameters in which these advancements can be carried out. Until recently, we did not really have to resort to silicon photonics to provide a much faster and more efficient alternative to current copper-based electrical methods for data transmission. NVIDIA integrating TSMC’s CPO technology into its Ethernet and InfiniBand networking solutions serves as an important proof of technology to cut power used by the transceivers running between GPUs. Even though Intel and others have been working on embedding silicon photonics into Central Processing Unit (CPU) packages for some time, no other packaging concept has been fully commercialized yet due to manufacturing complexities added by a need for high precision, yield issues adding to development costs, and limited market readiness inhibiting scaling. Additionally, a lack of standardization around optical interconnect technology in data centers exists today, given the limited testing and deployment among the hyperscale cloud service providers in large-scale environments apart from pilot projects or research initiatives, though energy costs for networking and the energy-intensive communication between GPUs in AI data centers have been an openly discussed concern for Meta and Amazon Web Services (AWS).

Given the growing reliance on AI across numerous sectors, it’s crucial that energy solutions are adaptable to future developments in AI hardware, thus ensuring that energy management remains ahead of the technology curve. Focusing on reducing power consumption and operational costs, while identifying new energy-efficient solutions that power AI data centers and optimized AI workloads are no longer optional, but critical to economic feasibility of any large-scale AI deployment. By refining and scaling CPO-based solutions and pushing forward with the development of optical interconnects, NVIDIA and its partner ecosystem can help overcome the current barriers to large-scale implementation and set industry standards and directionality for energy-efficient, high-bandwidth optical interconnects.

Scaling-out with CPO-based networking switches is a comparatively low-risk entry point, whereas the scale-up of optical interconnects for its GPU NVLink is more pivotal. NVIDIA is expected to develop a broader spectrum of new optical technologies, given how critical its scale-up capabilities are in expanding high bandwidth interconnects beyond a single rack. With NVIDIA’s 2028 time frame guidance for NVLink CPO, it now has two generations to prove its technological viability in scale-out networks with its Original Equipment Manufacturer (ODM), Original Design Manufacturer (ODM), and hyperscale partners. NVIDIA will play a pivotal role in directing investments and securing partnerships that are likely to accelerate the industry shift toward optical interconnects by continuing to innovate, driving its large-scale adoption in data centers, and collaborating with key stakeholders such as hardware manufacturers, cloud providers, and energy providers.

NVIDIA’s advancements are not only a technological advancement, but equally a strategic business play to differentiate by functioning as both a leader in accelerator innovation and an enabler of scalable, energy-efficient infrastructure critical to the growth of AI workloads. CPO-based network switches in NVIDIA’s future hardware solutions are just the beginning. Bringing optical interconnects all the way to the GPU is now foreseeable and it’s become much more a question of when, rather than if. To effectively address the increasing energy demands of AI workloads, companies and regulators need to accelerate investments in both hardware innovations like silicon photonics and grid-level infrastructure that can accommodate the increase in power requirements, because the solutions being developed now will influence the viability, cost reductions, operational efficiency, and scalability of future AI applications during the Rubin time frame.