Nobody really expected Nvidia to release something like the GB10. After all, why would a tech company that transformed itself into the most valuable firm ever by selling parts that cost hundreds of thousands of dollars, suddenly decide to sell an entire system for a fraction of the price?
Nvidia aims to revolutionize computing much like IBM did with the original IBM PC nearly 45 years ago. The release of Project DIGITS marks a significant shift in their strategy towards democratizing AI performance while maintaining a stronghold in the industry.
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Project DIGITS Overview
Project DIGITS is a fully formed, off-the-shelf supercomputer compacted into the size of a mini PC. This device serves as a more accessible alternative to the DGX-1, which was launched almost a decade ago at a jaw-dropping price of $129,000. In contrast, Digits comes at a significantly reduced price of $3,000.
The heart of Digits is the GB10 SoC, featuring 20 Arm cores and a Blackwell GPU. Currently, it boasts an AI performance of 1,000 Teraflops at FP4 precision. While it’s difficult to make direct comparisons, estimates suggest that this compact supercomputer has about half the processing power of a fully loaded, larger DGX-1 system.
For network expansion, users can connect two devices via Nvidia’s proprietary ConnectX technology, allowing for collaborative processing of larger LLMs such as Meta’s Llama 3.1 405B. However, fitting multiple mini PCs into a 42U rack may be impractical against Nvidia’s more lucrative systems.
All About the Moat
The motivation behind Nvidia’s venture into Project DIGITS lies in reinforcing its competitive moat. This strategy involves creating products so integral and dependable that switching to competitors becomes daunting. Similar tactics have proven successful for other tech giants, including Microsoft and Apple.
Nvidia’s dominance in CUDA allows it to control market standards, making it challenging for competitors to innovate without losing ground. The company’s recent move to FP4 for inference capabilities enables it to present benchmarks like “Blackwell delivers 2.5x its predecessor’s performance in FP8 for training, per chip, and 5x with FP4 for inference.” AMD does not currently support FP4 computation, giving Nvidia a unique position in the market.
Nvidia CEO Jensen Huang’s ambition to expand beyond traditional markets is clear: “AI will be mainstream in every application for every industry. With Project DIGITS, the Grace Blackwell Superchip comes to millions of developers, placing an AI supercomputer on the desks of every data scientist, AI researcher and student.” His vision is to establish Nvidia as synonymous with AI, akin to how certain brands represent specific products.
Why Mediatek?
The collaboration with Mediatek may seem puzzling initially, but the rationale lies in the Taiwanese company’s expertise in Arm-based SoC performance and power efficiency. According to a Mediatek press release, this partnership is designed to produce groundbreaking devices for AI researchers and developers.
Despite this partnership, Nvidia may still choose to operate independently concerning consumer-targeted products. Huawei has suggested Nvidia’s main focus moving forward will be on high-quality software and professional markets, rather than engaging directly with consumer competition.
Huang noted that Mediatek could also leverage Nvidia’s designs for their segments, creating a beneficial win-win scenario for both companies.
Gazing in My Crystal Ball
One emerging theory during research indicated that many data scientists are gravitating towards Apple’s Mac platform due to its balanced approach to performance and pricing. For instance, the Mac Studio currently retails for $5,799 with impressive specifications.
Where does Nvidia go from here? A potential direction might involve integrating memory onto the SoC, similar to Apple’s M series chips, which could enhance performance while reducing costs. This could enable Nvidia to offer better options without compromising price competitiveness.
Nvidia’s trajectory may look to emulate Intel’s past initiatives by encouraging partners like PNY, Gigabyte, and Asus to explore similar market options as they did with their NUC line. Additionally, the future of the Jetson Orin family remains intriguing as this platform continues to cater to diverse use cases, especially in the DIY and edge computing segments.
Ultimately, Nvidia seems poised to disrupt its own market before competitors get the chance—raising questions about how far they can extend their innovation and leadership.
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