Is Meta's In-House Chip Strategy a Smart Move for Its Shares?

Meta Platforms is making strides by testing its own in-house chips for artificial intelligence (AI) training. The company aims to reduce its dependency on NVIDIA, a major graphics processing unit (GPU) supplier, and cut costs associated with AI technologies.

Current Meta Platforms Stock
$596.25 +10.25 (+1.75%)

As of 03/21/2025 04:00 PM Eastern

Meta's testing program is part of a broader initiative to manage costs for AI operations, which could have long-term benefits for its stock performance.

Understanding In-House Chips: Meta’s Strategy Against NVIDIA

Meta is currently testing its first custom chip designed for AI training, which marks a significant step in its efforts to streamline costs. Training an AI model requires considerable computational resources and energy, which can lead to high expenses. Meanwhile, "inference" is the process of an AI model responding to specific queries following training. While inference incurs low costs over time, the initial training phase is far more resource-intensive.

With in-house chips, Meta hopes to reduce costs associated with both training and inference, allowing for better model development and higher interaction rates across its apps like Facebook and Instagram. Given the high prices of NVIDIA’s GPUs—where NVIDIA reported a gross margin of nearly 74%—the creation of its own chips offers Meta the opportunity to inject competition into the market, potentially lowering its price obligations.

The Importance of Energy Efficiency in Meta's Chip Development

Another advantage of custom-designed chips is their ability to perform faster while consuming less power compared to traditional GPUs. Companies like Amazon have noted that their custom chips can offer better price-to-performance ratios than NVIDIA’s top products, suggesting potential gains for Meta as it develops its technology.

With growing concerns about energy consumption in data centers—expected to rise dramatically in the coming years—Meta's push for energy-efficient chips could not only lower operating costs but also address broader environmental impacts. Currently, data centers represent about 1% to 2% of global energy demand, a number that might spike due to increased AI activity.

Right now, Meta is using its inference chip, called Artemis, for tasks such as recommending content and advertisements. However, it is still at the early stages of deploying its AI training chip, with plans potentially set for testing in 2026. Initial efforts will focus on training systems to enhance recommendations before advancing to more complex AI tasks.

Long-Term Implications for Shares as Meta Cultivates Cost-Cutting

Potential Outlook for Meta’s Stock

Meta’s initiative to develop its own chips could provide a positive boost to its stock in the long run. By minimizing operational costs, the company could witness an improvement in profit margins. However, successful testing and implementation are essential before shareholders can fully gauge the financial impact. Additionally, scaling production of these chips will require significant investment, which may affect margins in the near term.

Investors will need to monitor further updates on Meta's chip testing and rollout strategy to understand the full potential of this undertaking.

Investment Considerations for Meta Platforms

Before committing any investment, interested parties should consider recent analyst sentiments surrounding Meta Platforms. Although it is categorized as a "Moderate Buy," other stocks may currently be viewed as more compelling opportunities.

Meta, AI, Chip