Upscale AI Unveils SkyHammer Architecture to Challenge Nvidia’s Scale-Up Networking Monopoly

In the history of the semiconductor industry, the desire for a "universal glue"—a standardized interconnect that allows multiple processors to function as a single, massive compute entity—has been a recurring theme. Historically, every major CPU manufacturer maintained its own proprietary methods for linking processors into shared memory clusters. Despite the valiant efforts of organizations like the CCIX consortium and the CXL consortium to create an industry-wide standard, the world of high-performance computing (HPC) remained largely fragmented into proprietary silos.

The failure to establish a universal standard for Symmetric Multiprocessing (SMP) and Non-Uniform Memory Access (NUMA) was not due to a lack of engineering talent, but rather a lack of market alignment. Customers may have desired the flexibility to mix and match CPU architectures from different vendors, but the practical reality of such workloads was always niche. Furthermore, silicon vendors found that proprietary chipsets allowed them to innovate at their own pace, independent of the slow-moving committees of standards bodies. By embedding these interconnects directly into the CPUs, they could differentiate their products and lock in their ecosystems.

However, the explosive rise of generative AI and large-scale model training has reignited the push for standardized "scale-up" networking. Today, the focus has shifted from CPUs to GPUs and specialized accelerators (XPUs). While the players have changed, the fundamental economic tension remains: the industry craves competition to drive down costs, while the market leader, Nvidia, continues to reap the rewards of a highly optimized, proprietary stack.

Nvidia’s dominance is currently anchored by its NVLink and NVSwitch technologies. These proprietary ports and switches offer unparalleled bandwidth and ultra-low latency, enabling the company to glue together as many as 72 GPUs into a single shared-memory system known as the NVL72 rackscale machine. The premium Nvidia charges for this "scale-up" fabric is significant, reflecting the lack of viable alternatives. While Nvidia has recently shown a willingness to license NVLink ports through its NVLink Fusion effort—allowing custom CPUs and third-party XPUs to hook into its memory space—it has stopped short of allowing customers to build their own independent shared-memory systems using the NVLink protocol without buying Nvidia’s own silicon.

This landscape provides the backdrop for the emergence of Upscale AI, a startup that is rapidly becoming one of the most watched players in the AI infrastructure space. Since its Series A funding round in January, where it raised $200 million, Upscale AI has seen its total capital grow to $500 million, its valuation double to $2 billion, and its workforce swell to more than 300 employees. This week, the company’s leadership provided the most detailed look yet at its plans to disrupt the AI networking market with its "SkyHammer" scale-up switch ASIC.

The leadership team at Upscale AI brings a wealth of experience to the table. Co-founder and executive chairman Rajiv Khemani and CEO Barun Kar are veterans of the high-performance networking industry, and their strategy reflects a deep understanding of where the current bottlenecks in AI clusters lie.