Moving to Control More of the AI Server Stack to Boost Margins

NVIDIA has been working towards a shift into its AI rack-scale server strategy, and instead of just being responsible for a piece of the supply chain, Team Green looks to get the ‘whole pie’.

NVIDIA Would Now Supply Entire Systems To Suppliers, Unifying Rack Designs For Faster Deployment

For those unaware, NVIDIA’s AI supply chain is built upon several partners responsible for various elements of the end products. However, when it comes to AI server racks, Taiwanese firms such as Foxconn, Quanta, and Wistron account for a major portion of the manufacturing stages. In the conventional approach, NVIDIA would only supply components such as AI GPUs or the boards required for server configurations such as the Bianca Port UPB. However, during Wistron’s Q3 earnings call (via Ray Wang), a JPMorgan analyst mentioned that NVIDIA is moving towards “directly supplying” entire systems.

The traditional approach for NVIDIA has been to keep the more essential elements of its server rack, such as GPUs and the board, in-house, while assigning the rest of the rack architecture to suppliers like Foxconn and Quanta. While this approach was initially fruitful for the firm, as rack-scale configurations weren’t as large as they are now, it appears that Team Green is looking to switch things up. JPMorgan analyst mentions that NVIDIA will now directly supply Level-10 systems to partners, with the intention of unifying rack designs and ensuring a significant reduction in time-to-market.

If we sum up this development, it appears that NVIDIA will now provide ‘blueprints’ to companies like Foxconn and Quanta, which they will adhere to in producing AI systems. This will prevent suppliers from designing individual rack architectures. It would not be incorrect to say that NVIDIA had already intended to adopt this approach once it introduced its ‘MGX architecture’, which defines the entire server’s physical and electrical architecture, transitioning from a single node to complete rack-scale ‘AI factories’.

With this approach, NVIDIA essentially reduces deployment times from 9 to 12 months to just 90 days, since 80% of the system is pre-defined and validated by NVIDIA. This means that newer architectures, such as Rubin/Rubin CPX racks, will be delivered to customers a lot more quickly. This also brings in higher margins for Team Green, as it is responsible for full system sales, and also helps the firm expand its TAM figures. As Wistron mentions, this approach also brings benefits to suppliers.

What I can say is that regardless of whether it’s NVIDIA or any of our other customers, and regardless of who the end customer is, the work is still done by Wistron. So from Wistron’s perspective, this business model doesn’t really create a major impact. In fact, I believe this is all positive for Wistron.

It appears that NVIDIA intends to transition from solely the ‘AI chip’ business to the entire infrastructure being developed by the firm, which, based on what we are seeing, will speed up the deployment of AI systems out in the market. For now, it’s essential to note that NVIDIA has not officially confirmed this transition. As it’s an internal matter among suppliers, we’ll have to wait and see how it develops.

https://wccftech.com/nvidia-reportedly-planning-a-major-shift-in-its-ai-business-model/amp/