Meta teams with Nvidia in long‑term AI infrastructure partnership, launches AI Daily

Meta’s partnership with Nvidia will give the social‑media giant access to Nvidia’s H100 Tensor Core GPUs and the DGX Cloud service, enabling Meta to run its generative‑AI workloads at “petabyte‑scale” across its data‑center fleet, according to Bloomberg. The agreement is described as “long‑term,” suggesting that Meta will rely on Nvidia’s hardware roadmap for the next several years rather than a one‑off purchase. By tying its AI stack to Nvidia’s CUDA software ecosystem, Meta can reuse existing model‑training pipelines that were originally built for its LLaMA family of large language models, reducing the engineering effort required to migrate to newer architectures.

The partnership also underpins the launch of AI Daily, Meta’s new platform for delivering AI‑generated content and services. AI Daily will expose a suite of APIs that let developers query Meta’s internal models for tasks such as text generation, image synthesis, and multimodal reasoning. Bloomberg reports that the platform will be powered by the same Nvidia GPUs used for Meta’s internal research, meaning latency‑critical workloads—like real‑time chat or recommendation generation—can be served from the same hardware that trains the models. This co‑location of training and inference hardware is intended to cut data movement costs and improve throughput for high‑frequency API calls.

In parallel with the infrastructure rollout, Meta is testing an AI‑driven shopping assistant that integrates directly into its Messenger and Instagram interfaces. Bloomberg notes that the chatbot returns a carousel of product images, each annotated with brand, website, and price information, effectively turning a conversational exchange into a visual storefront. The system leverages Meta’s proprietary language model to interpret user intent and then queries a product‑catalog database to surface relevant items. By embedding the shopping flow within the chat UI, Meta hopes to keep users inside its ecosystem while offering a “ChatGPT‑style” experience for e‑commerce.

Meta’s move comes as competitors such as Google and Perplexity AI are also embedding shopping features into their AI products, according to Bloomberg’s coverage of Google’s Gemini chatbot. The competitive pressure highlights a broader industry trend: large language models are being repurposed as front‑ends for transactional services, not just information retrieval. Meta’s strategy of coupling a dedicated hardware partnership with a developer‑facing API platform positions it to compete on both performance and ecosystem breadth.

Finally, Bloomberg reports that Meta will begin mining user conversations with its AI assistants to improve model accuracy and personalization. The data collection will be scoped to “discussions with its artificial intelligence,” implying that only interactions that explicitly invoke Meta’s AI features will be harvested. This approach aims to create a feedback loop where real‑world usage informs model updates, while the partnership with Nvidia ensures the compute capacity needed to process the resulting training data at scale.