In a significant new breakthrough of AI later stage infrastructure evolution, Tachyun, a semiconductor development company has taken the membership of the Ultra Accelerator link (UALink) Consortium. This helps to build on industry work to develop an open framework for how to connect one AI accelerator to another inside of a data center; as workloads become increasingly data driven and complex, this is the next necessary step.
Understanding UALink’s Mission
Formation of the UALink Consortium occurred in late in 2024 by a group of technological companies that include AMD, Broadcom, Cisco, Google, Hewlett-Packard Enterprise (HPE), Intel, Meta and Microsoft among others. These companies have joined hands to set guidelines for high-speed interconnect in the form of specifications, to support the direct communication of the computing nodes or accelerators within the functional system.
The UALink Consortium started at the end of the year 2024 with some of the industry players, which are AMD, Broadcom, Cisco, Google, Hewlett Packard Enterprise (HPE), Intel, Meta, and Microsoft. These companies are now developing open and specified architecture at system level for the high speed systems which are used for connecting AI accelerators for direct connection internally in the computer nodes and at inter node level.
With AI training models like GPT-5 and Gemini Ultra pushing the limits of today’s infrastructure, data centers increasingly rely on accelerator-rich architectures. UALink is designed to be the backbone of such environments, solving bottlenecks caused by closed, proprietary interconnect technologies and enabling cross-vendor compatibility.
The consortium’s first specification, UALink 1.0, enables direct memory access and coherent interconnectivity between up to 1,024 accelerators in a single AI pod. With a data transfer rate of up to 200 Gbps per channel, it quickly improves the ability to scale training and inference tasks across multi-GPU or multi-chiplet systems.
The UALink model is widely seen as a direct response to NVIDIA’s dominance in the accelerator interconnect space with its NVLink and NVSwitch technologies, which until now have lacked meaningful open alternatives.
Tachyum’s Role and Contributions
This move also shows a passion for the transformation of the computing sector that Tachyum made by joining the UALink Consortium. The Prodigy Universal Processor which is the major product of the company aims to eliminate the conventional CPUs, GPUs and TPUs in favor of only one chip. It cuts down the implementational work by a huge amount and comes with the benefits of better power efficiency and scalability in different computing tasks, especially Artificial Intelligence (AI) and High-Performance Computing (HPC).
Prodigy is projected to deliver up to 10x the performance of current processors for AI training and inference tasks while consuming significantly less power. It supports FP64, FP32, TF32, BF16, INT8, and other precision modes commonly used in AI models. With built-in virtualization and direct memory access, Prodigy is particularly well suited for the UALink topology.
Through its participation in UALink, Tachyum will help shape the development of interoperable standards for hardware and communication protocols. These will make sure that Prodigy-powered systems can easily integrate into AI clusters built using technologies from other vendors, a key step toward more open and flexible infrastructure.
“Tachyum is proud to support UALink’s mission to drive open and high-performance connectivity solutions for AI workloads,” said Dr. Radoslav Danilak, CEO of Tachyum. “With Prodigy, we aim to empower a future where data centers are not only faster and more efficient, but truly universal in design.”
A Shared Vision: Open Standards for AI Infrastructure
The desire for open interconnect standards is not just a request in the technology field. It is a complete strategic turn in the AI industry. Ever since the AI accelerator area was born, there has been a lot of proprietary technology which have made each ecosystem to proprietary hardware. This has caused chain phases to innovation to be put a halt, has also constrained scalability, and has also made the costs for organizations that use artificial intelligence at large to increase.
UALink thus provides an efficient way to directly connect different types of accelerators, similar to how USB sorted out connectors for different types of devices in consumer electronics. The goal is to allow building a difficult heterogenous AI system from different components provided by various manufacturers, which are best suited for the particular task at hand.
This promotes different players and variety in the chip design, an important counterbalance to consolidation that characterizes the AI hardware industry. It also encourages the increased involvement of startups and other small players; they can see that if they design specialized accelerators, they will deal with primary data center architectures.
How This Impacts AI Development and Deployment
The implications of UALink—and Tachyum’s contributions—are far-reaching, particularly when it comes to how AI is developed and deployed at scale. One of the most immediate benefits is faster AI training. With the ability to interconnect thousands of AI accelerators using high-bandwidth, low-latency links, UALink enables rapid data sharing and coordination across compute units. This dramatically shortens the training times for complex AI models, such as those used in generative AI, computer vision, and large language models, accelerating innovation and reducing time-to-market.
One more benefit of cloud computing is the increased build-up of many-tenant data centers. In the case of standardized interconnects, cloud providers can better manage the differentiation of the accelerator resource and carve it into slices to be sold to various customers; this optimizes the amount of hardware under utilization. This not only improves organizational productivity but also minimizes energy wastage, which is a big issue in today’s world of the green technology industry.
In the financial aspect, the cost of ownership is approved to be low through being linked with UALink. This also helps in avoiding the problem of compatibility with the specific vendor’s products and allows an enterprise to select hardware that is compatible with the interconnect. This helps make it possible to have flexible terms of competition in the market and helps create more flexibility when developing and extending artificial intelligence systems.
Finally, UALink supports the shift toward sustainable computing. With more streamlined communication between accelerators and fewer redundant system components, data centers can reduce their power draw significantly.
This efficiency helps the world in reducing carbon use and goes well with the future goals and intentions of making data center to be environmentally friendly. These advancements that Tachyum has contributed to through its Prodigy processor especially the energy efficient kind help advance the quest for a sustainable AI infrastructure that is powerful, scalable as well as environmentally sustainable.
The Competitive Landscape
While UALink is gaining momentum, its rise comes at a time when NVIDIA’s AI stack—including CUDA, NVLink, and Hopper architecture—is deeply improved in enterprise and cloud environments. However, cracks are beginning to show.
Major hyperscalers such as Microsoft Azure and Google Cloud have shown interest in diversifying away from NVIDIA by investing in alternative AI chip startups (like Groq, Graphcore, and Cerebras) and embracing open accelerator ecosystems. With UALink, these ambitions take a concrete form.
Other similar efforts that are being implemented in the broader market are some organizations like the Open Compute Project and UCIe Consortium that are also developing goals for the chiplets and packaging to allow modularity in AI computing.
If these groups and UALink work together, the next generation of the AI data center could be formed with open, high-performance, and environment-friendly assets.
