Upscale AI Challenges Nvidia’s Scale-Up Dominance with SkyHammer Fabric and $2 Billion Valuation

When it comes to memory domain scalability, SkyHammer is designed to support up to 576 accelerators within a single networking tier. By utilizing multiple tiers, the fabric can scale to support thousands of devices. For comparison, the UALink consortium—a rival standards-based effort—is aiming for 1,024 XPUs in a single tier, while Nvidia’s upcoming "Rubin Ultra" generation is expected to push NVSwitch to 1,152 GPUs across a multi-tier network.

However, a raw comparison of bandwidth reveals a significant gap between Upscale AI’s current offering and Nvidia’s top-tier hardware. If one calculates the bandwidth per device in a 576-accelerator SkyHammer configuration, each device receives approximately 200 Gb/sec. This is considerably lower than the 1.8 TB/sec offered by Nvidia’s NVSwitch 5/NVLink 5 generation used in the Grace-Blackwell NVL72 systems, and even further behind the 3.6 TB/sec promised for the Vera-Rubin generation.

Despite this disparity in raw throughput, Upscale AI argues that bandwidth is not the only metric that matters in the AI era. For AI inference workloads, in particular, the predictability of latency is often more critical than the sheer volume of data being moved. Arvind Srikumar, Senior Vice President of Product and Marketing at Upscale AI, emphasizes that the company has built "predictable jitter" and "transmission reliability" directly into the ASIC.

Srikumar notes that while traditional Ethernet fabrics often rely on a fixed pipeline arrangement with limited parallelism, SkyHammer was designed from a "clean sheet of paper." The goal was to avoid the pitfalls of retrofitting existing Ethernet protocols for scale-up tasks. Instead, SkyHammer utilizes a mechanism to ensure the fabric is 100 percent lossless within the domain, providing what the company calls "reliable and predictable latency."

Technically, SkyHammer is an Ethernet-based switch ASIC, but it runs specialized protocols such as UALoE (UALink over Ethernet) and ESUN. This choice highlights a broader industry trend: the convergence of Ethernet and high-performance interconnects. While InfiniBand has long been the gold standard for low-latency HPC and AI training, Ethernet is rapidly closing the gap. With the emergence of the Ultra Ethernet Consortium (UEC) specifications, many believe Ethernet will eventually supersede InfiniBand due to its superior ability to scale out and its broader ecosystem of support.