According to a report from tom’s Hardware, Jack Huynh, AMD’s senior vice president and general manager of its Computing and Graphics Business Group, announced at IFA 2024 in Berlin that AMD will unify its consumer microarchitecture “RDNA” and data center microarchitecture “CDNA” under a single name: “UDNA.” This move is expected to compete with NVIDIA’s CUDA ecosystem.

Previously, AMD used the same architecture for both gaming and compute GPUs, known as “GCN.” However, since 2019, the company decided to split the microarchitectures into two distinct designs: RDNA for consumer gaming GPUs and CDNA for data center computing.

Reportedly, Jack Huynh stated that the consolidation into the unified “UDNA” architecture will make it easier for developers to work with, eliminating the need to choose between different architectures without added value.

When asked if future desktop GPUs will have the same architecture as the MI300X, Huynh mentioned that this is part of a strategy to unify from cloud to client. With a single team working on it, the company is making efforts to standardize, acknowledging that while there may be minor conflicts, it is the right approach.

While high-end chips can establish a market presence, the report from tom’s hardware further addressed that the ultimate success depends on software support. NVIDIA built a strong moat 18 years ago with its CUDA architecture, and one of its fundamental advantages is the “U” in CUDA, which stands for Unified.

NVIDIA’s single CUDA platform serves all purposes, using the same underlying microarchitecture for AI, HPC, and gaming.

Jack Huynh revealed that CUDA has around 4 million developers, and his goal is to pave the way for AMD to achieve similar success.

However, AMD relies on the open-source ROCm software stack, which depends on support from users and the open-source community. If AMD can simplify this process, even if it means optimizing for specific applications or games, it will help accelerate the ecosystem.

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(Photo credit: AMD)

NVIDIA’s market leadership has garnered significant attention from other industry players. According to a report from Financial Times, several smaller companies, including Cerebras, d-Matrix, and Grog, have raised hundreds of millions of dollars and are launching new products, hoping to carve out a niche in the market.

Cerebras, founded in 2016, recently unveiled its new platform, Cerebras Inference, based on its CS-3 chip. The company even claims its solution is 20 times faster than NVIDIA’s current generation Hopper for AI inference, and at a fraction of the cost.

Per another report from the Economic Daily News, in March this year, Cerebras also launched the WSE-3 processor designed for training AI models, manufactured using TSMC’s 5nm process. At that time, Cerebras confirmed plans for an IPO and has confidentially filed a registration statement with the U.S. Securities and Exchange Commission.

Notably, Andrew Feldman, CEO of Cerebras, further noted that they have already secured meaningful customers from NVIDIA.

d-Matrix, established five years ago, is launching a new funding round with a target of raising over USD 20 million. This follows their USD 11 million Series B round led by Temasek, completed less than a year ago.

The company plans to fully launch its Corsair platform by the end of the year and is integrating its products with open-source software, including Triton, which competes with NVIDIA’s CUDA. Several of NVIDIA’s largest customers support the use of open-source software.

Groq, founded in the same year as Cerebras and led by a team from Google’s Tensor Processing Unit division, recently raised $64 million from investors including BlackRock Private Equity Partners, giving it a valuation of $2.8 billion.

Despite the rush to find and support the next NVIDIA, semiconductor startups are facing significant challenges, according to the Financial Times.

For example, chipmaker Graphcore was acquired by SoftBank last month for just over USD 6 billion, falling short of the approximately USD 7 billion it had raised from venture capital since its founding in 2016.

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(Photo credit: Cerebras)

While NVIDIA is likely to face accusations from the French antitrust regulators, the Non-NVIDIA Alliance like the UALink (Ultra Accelerator Link) Alliance and the UXL Foundation are reportedly launching a counterattack, significantly increasing their efforts in developing specialized ASICs.

According to a report from Commercial Times, relevant semiconductor intellectual property (IP) is expected to be widely adopted. The sources cited by the report point out that Taiwanese manufacturers, benefiting from their leading position in wafer foundry and comprehensive ASIC and IP layout, are poised to capitalize on the rise of the Non-NVIDIA Alliance.

The report further cites sources, indicating that major Taiwanese ASIC manufacturers such as Global Unichip, Faraday Technology, and Progate Group Corporation (PGC), along with silicon IP companies M31 Technology Corporation, eMemory, and the Egis Technology Group, are actively expanding in this field.

In order to challenge NVIDIA’s dominance in the market, UALink (Ultra Accelerator Link) Alliance, led by tech giants such Intel and AMD, was formed in May. The alliance aims to establish a new standard for AI accelerator links, aiming to challenge NVIDIA’s  NVLink.

Furthermore, the UXL Foundation’s Open Source Software Project, supported by tech giants Qualcomm, Google, and Intel, is said to be looking to rival NVIDIA’s CUDA software. By providing alternative software solutions, it aims to diminsh NVIDIA’s dominance in the AI field.

Semiconductor industry sources cited in the same report also note that CSPs are accelerating the development of their own chips, with Taiwanese manufacturers actively entering the market.

Although Broadcom and Marvell currently offer diversified design services, Taiwanese manufacturers have an advantage due to the tightly-knit semiconductor supply chain. This enables complete solutions for both chip manufacturing and packaging within Taiwan, giving them a strategic edge over competitors by being close to both the market and factories, thereby enhancing their position in the ASIC sector.

Global Unichip and PGC leverage TSMC as a strong ally. Reportedly, Global Unichip holds AI-related ASIC orders from Microsoft and is gradually finalizing collaborations with major South Korean companies, with business operations expected to improve in the second half of the year.

On the other hand, Faraday Technology closely collaborates with Intel, developing SoCs using Intel’s A18 process. Meanwhile, industry sources cited by the report suggest that Intel’s Gaudi series AI chips might seek collaboration opportunities beyond just working with Alchip.

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Intel, Qualcomm, Google, and other tech giants are reportedly joining forces with over a hundred startups to challenge NVIDIA’s dominance in the market, as per a report from Reuters. Reportedly, their goal is to collectively penetrate the artificial intelligence (AI) software domain, guiding developers to migrate away from NVIDIA’s CUDA software platform.

NVIDIA’s CUDA is a parallel computing platform and programming model designed specifically to accelerate GPU computing. It allows GPU users to fully leverage their chip’s computational power in AI and other applications. As per a previous report from TrendForce, since 2006, NVIDIA has introduced the CUDA architecture, nearly ubiquitous in educational institutions. Thus, almost all AI engineers encounter CUDA during their academic tenure.

However, tech giants are now reportedly aiming to disrupt the current status quo. According to a report from Reuters on March 25th, Intel, Qualcomm, and Google are teaming up to challenge NVIDIA’s dominant position. They plan to provide alternative solutions for developers to reduce dependence on NVIDIA, encourage application migration to other platforms, and thereby break NVIDIA’s software monopoly and weaken its market influence.

The same report from Reuters further indicated that several tech companies have formed the “UXL Foundation,” named after the concept of “Unified Acceleration” (UXL), which aims to harness the power of acceleration computing using any hardware.

The project plans to leverage Intel’s oneAPI technology to develop software and tools supporting multiple AI accelerator chips. The goal is to reduce the technical barriers developers face when dealing with different hardware platforms, streamline the development process, enhance efficiency, and accelerate innovation and application of AI technology.

Vinesh Sukumar, Head of AI and Machine Learning Platform at Qualcomm, stated, “We’re actually showing developers how you migrate out from an NVIDIA platform.”

Bill Magro, Head of High-Performance Computing at Google, expressed, “It’s about specifically – in the context of machine learning frameworks – how do we create an open ecosystem, and promote productivity and choice in hardware.” The foundation is said to aim to finalize technical specifications in the first half of this year and strives to refine technical details by the end of the year.

However, CUDA software has established a solid foundation in the AI field, making it unlikely to be shaken overnight. Jay Goldberg, CEO of financial and strategic advisory firm D2D Advisory, believes that CUDA’s importance lies not only in its software capabilities but also in its 15-year history of usage. A vast amount of code has been built around it, deeply ingraining CUDA in numerous AI and high-performance computing projects. Changing this status quo would require overcoming significant inertia and dependency.

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(Photo credit: NVIDIA)

As per Chinese media mydrivers’ report, it has indicated that NVIDIA updated the EULA terms in the CUDA 11.6 installer, explicitly prohibiting third-party GPU companies from seamlessly integrating CUDA software. This move has reportedly stirred up discussions among the market.

NVIDIA updated the EULA agreement for CUDA 11.6, explicitly stating, “You may not reverse engineer, decompile or disassemble any portion of the output generated using SDK elements for the purpose of translating such output artifacts to target a non-NVIDIA platform.”

This move is reportedly speculated to target third-party projects like ZLUDA, involving Intel and AMD, as well as compatibility solutions from Chinese firms like Denglin Technology and MetaX Technology.

In response, MooreThreads issued an official statement, confirming that its MUSA and MUSIFY technologies remain unaffected. MUSA and MUSIFY are not subject to NVIDIA EULA terms, ensuring developers can use them with confidence.

In fact, since 2021, NVIDIA has prohibited other hardware platforms from running CUDA software analog layers but only warned about it in the online EULA user agreement. Currently, NVIDIA has not explicitly named any parties, issuing warnings in the agreement without taking further action, although further measures cannot be ruled out in the future.

In the past, CUDA development and its ecosystem were widely regarded as NVIDIA’s moat. However, processor architect Jim Keller previously criticized CUDA on the X platform, stating, “CUDA is a swamp, not a moat,” adding, “CUDA is not beautiful. It was built by piling on one thing at a time.”

Meanwhile, according to the account rgznai100 posting on Chinese blog CSDN, NVIDIA’s actions will have a significant impact on AI chip/GPGPU companies that previously adopted CUDA-compatible solutions. NVIDIA may initially resort to legal measures, such as lawsuits, against GPGPU companies following similar paths.

Therefore, Chinese enterprises should endeavor to enhance collaboration with the open-source community to establish an open AI compilation ecosystem, reducing the risks posed by NVIDIA’s market dominance.

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(Photo credit: NVIDIA)