NVIDIA and Meta Platforms, Inc. have announced a multi-year strategic partnership, under which Meta will purchase and deploy millions of NVIDIA chips. This agreement extends beyond current Blackwell architecture and the next-generation Rubin GPUs, marking the first time Meta will deploy NVIDIA's Grace CPU and the future Vera CPU at a massive scale independently. The objective is to double AI inference performance and energy efficiency.
Analysts project this deal will substantially support Meta's ambitious plan to invest $600 billion by 2028 to construct 30 data centers. This powerful alliance reinforces NVIDIA's dominance in AI infrastructure and signals Meta's accelerated push towards the era of personal super-intelligence. It also strongly suggests that Meta may have completely abandoned its in-house chip development efforts.
A noteworthy new component is the Grace CPU, which is being sold separately in this deal, not bundled with a GPU. This is not a minor transaction; as indicated, the deal is valued at tens of billions of dollars. Meta alone has planned AI-related capital expenditure to surge to $135 billion in 2026, with the social media giant also set to invest $60 billion in data center construction across the United States between 2026 and 2028. NVIDIA is securing a significant portion of this expenditure, indicating that Mark Zuckerberg is placing a major bet on Jensen Huang.
While NVIDIA has sold CPUs before, they were typically bundled with GPUs into superchips for high-performance computing clients. Meta is the first major tech giant to deploy Grace CPUs independently at such a large scale, with plans to adopt the next-generation Vera CPU by 2027.
Why would a social media company require so many CPUs? This relates to the AI inference phase. Many conflate AI training and inference, but they are entirely different processes. Training involves teaching a model using the parallel computing brute force of GPUs. Inference is when the model runs live to answer queries, requiring different computational patterns and having far more stringent power consumption requirements.
Over the past two years, the focus has been on training, leading to GPU shortages. However, the industry is now transitioning into the inference era. Meta's AI serves billions of users daily, handling tasks like content recommendation, message processing, and generating replies—all inference tasks. Running these solely on GPUs would be inefficient and economically unsustainable due to power costs. The advantage of the Grace CPU lies precisely here: it offers superior performance per watt, accomplishing more work for the same amount of electricity.
Analysts note the shift from the training era to the inference era necessitates entirely different computing approaches. By enabling Meta to use Grace CPUs, NVIDIA is establishing a significant foothold in this other critical frontier of AI computing.
Traditional CPU giants likely view this development with concern, and the pressure is even greater for startups specializing in inference chips, especially since NVIDIA recruited talent from Groq, an inference chip company, in December 2025.
Another significant application involves WhatsApp. Meta announced it will use NVIDIA's confidential computing technology to add AI features to the messaging platform. Confidential computing is a security feature that keeps data encrypted even during processing, making it inaccessible to anyone, including the servers running the computations.
This is crucial for a social application. WhatsApp's core selling point has always been end-to-end encryption. Introducing AI features that required reading user messages would undermine this fundamental promise. With confidential computing, Meta can run AI models without accessing the actual content of user chats. This could enable features like automatic reply summarization, smart reminders for tasks, or having a future AI assistant discreetly provide information during a conversation.
The implications extend beyond WhatsApp. It demonstrates that AI can be integrated into scenarios with extreme privacy sensitivity without sacrificing functionality.
Separately, it is known that Meta has been developing its own AI chips over the past year, collaborating with AMD, and was reportedly in discussions with Google about using its TPUs as recently as November 2025. Consequently, analysts suggest Meta will continue to purchase chips from AMD. This reflects a typical strategy for a major customer: maintaining alternative suppliers to avoid dependency on a single vendor.
Furthermore, NVIDIA's GPUs remain in short supply. The Rubin series is just entering mass production, while the Blackwell series still faces allocation queues. For a company of Meta's scale, securing a significant portion of NVIDIA's production capacity upfront is a more pragmatic strategy than waiting for its own chips to mature slowly. This ensures its massive $135 billion budget for 2026 can be deployed effectively. This serves as a cautionary tale for other large companies that might assume they can develop their own chips and adopt a "we don't need NVIDIA" stance; such a position may require careful reconsideration.
Another critical aspect is networking. Meta is adopting NVIDIA's Spectrum-X Ethernet platform, which enables millions of chips to communicate with each other at high speeds. Training a large AI model is akin to coordinating work among millions of individuals; if communication is inefficient, even the fastest chips are hampered. NVIDIA's earlier acquisition of Mellanox was a strategic move for this very purpose. If chips are the brain, the network is the nervous system, and NVIDIA is consolidating control over both.
Meta's planned $135 billion AI expenditure for 2026 represents a near-doubling from 2025. For context, the entire Apollo moon landing program cost approximately $150 billion. This means the combined AI investments this year by Meta, Microsoft, Google, and Amazon would be sufficient to fund several moon missions.
For NVIDIA, this deal's significance transcends monetary value. It signifies Meta's evolution from a customer purchasing GPUs to a strategic partner adopting NVIDIA's full technology stack, encompassing CPUs, GPUs, networking, and comprehensive software. Whether Zuckerberg can realize his AI ambitions will likely become clear within the next few years.
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