Nvidia and Meta have expanded their partnership to improve the performance of Muse, Meta’s artificial intelligence assistant, and its family of AI models. The agreement aims to make AI agents faster, more efficient and more affordable as demand for automated digital assistants continues to grow.
Announced on October 27, 2026, the collaboration will focus on optimising AI model training and inference. Nvidia will also invest $3 billion in Meta’s equity offering, strengthening its financial ties with the technology company.
Partnership Targets Better AI Performance
Meta plans to expand its use of Nvidia’s computing infrastructure, including Vera Rubin systems and Vera CPUs, to support its next-generation AI agents. It will also deploy Nvidia’s Spectrum-X Ethernet networking and BlueField-4 technology to improve data transfer and computing efficiency.
Meanwhile, both companies will work on optimising workflows to improve performance while reducing operating costs. Muse models already run on Nvidia infrastructure, and the expanded partnership will help Meta scale its AI services for a wider audience.
The companies are also considering collaboration on Nvidia’s OpenShell framework and Sentry monitoring technology. These tools could help organisations deploy AI agents more securely and monitor their behaviour.
Nvidia Investment Strengthens Strategic Ties
Nvidia’s $3 billion investment forms part of Meta’s equity offering to support its growing artificial intelligence ambitions. The deal also reinforces a partnership that combines Nvidia’s computing technology with Meta’s AI models and consumer platforms.
Nvidia chief executive Jensen Huang said AI agents represent a new computing approach in which software can understand goals, reason, use tools and complete tasks for users. Meta chief executive Mark Zuckerberg has also positioned Muse as part of his vision for delivering personal AI assistance to billions of people.
As competition intensifies across the AI industry, the partnership highlights the growing importance of computing infrastructure, efficient model development and secure AI agents. However, the companies have not disclosed a specific timeline for delivering the improvements.
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