Intel mobile processors with built-in Nvidia RTX graphics are coming—here’s what that could mean for your next laptop.
Intel and Nvidia announced a stunning $5 billion investment deal that signals a new strategic alliance between two of the biggest chip makers on the planet. Longtime frenemies in the microprocessor world, both companies have had their respective CPU and GPU hardware in the same laptop and desktop products for decades. Now, Nvidia has bought a $5 billion stake in Intel, taking ownership of 4% to 5% of the company through common stock. But this isn’t merely an investment deal.
The duo just announced that it will co-develop several new products that combine the companies‘ strengths. Intel will design and manufacture custom x86-based CPUs for data centers and AI infrastructure that can use Nvidia’s NVLink processor connection technology. At the same time, Nvidia will make GeForce RTX GPU chiplets to fit onto Intel system-on-chip (SoC) processors for consumer PCs.
Neither company will say when new products from this partnership will reach the market. But in a press conference on September 18, Nvidia CEO Jensen Huang revealed that Intel and Nvidia teams have already collaborated for a year on planning a shared architecture for these new SoCs. So silicon resulting from this partnership could be here sooner than expected.
Our news coverage of the deal, linked above, gets into more of the agreement details and the business impact of the partnership. However, my thoughts immediately went to consumer PCs and how this new team-up could shake up the world of laptops and desktops. If there’s a new line of Intel-Nvidia hybrid chips in the offing, what does that mean? How might specific Nvidia technologies play into new Intel systems? And where will this kind of CPU fit into the familiar categories shoppers already know?Nvidia’s $5 Billion Intel Investment Is Not Just About the Money: It’s Tech Synergies, Too
Nvidia has several technologies that Intel can benefit from in this new partnership. I’ll break them down one by one.NVLink: Faster Internal Chip Connections
One mentioned explicitly in the announcement is NVLink, Nvidia’s high-bandwidth, low-latency, proprietary interconnect that supports massive amounts of bandwidth for moving data between processor elements. (NVLink, on a bigger scale, is also a key enabling technology in Nvidia’s data center strategy.) With current GPU implementations reaching up to 1.8 terabytes (TB) per second (albeit, across 18 100GB-per-second connections), NVLink dwarfs the bandwidth of PCI Express 4.0, which has a maximum aggregate bandwidth of 64GB per second, and PCI Express 5.0, which doubles that. Not knowing how many connections a hybrid chip would actually use, the difference could be pretty significant.
Here, NVLink would be implemented as a connection between the Nvidia RTX graphics chiplet on the SoC and the chiplet with the Intel CPU cores. That sort of stable point-to-point connectivity promises to reduce bottlenecks for data-intensive uses, like graphics workloads, and it could mean faster AI inference performance and multitasking when incorporated into an SoC.CUDA and Tensor Cores: Supplanting the NPU?
Nvidia GPUs rely on CUDA processing cores, specialized parallel processors that efficiently handle the thousands of simultaneous operations needed for modern graphics rendering. But they can also handle floating-point and integer calculations, making them extremely useful for AI workloads.
Tensor cores comprise the rest of Nvidia’s silicon offering. These are specialized accelerators for the matrix math needed for AI. Where CUDA cores handle the bulk of computations, Tensor cores speed up deep learning and AI tasks, boosting speed for specific functions that need neural network training and inference. These specialized cores also serve as the AI engine behind Nvidia’s DLSS tech, which I’ll get to shortly.
