Investigative Review of Nvidia Corporation

The Trillion-Dollar Software Prison

Nvidia Corporation is frequently misidentified as a hardware manufacturer. While the company ships physical silicon, its valuation, surpassing the GDP of most nations by 2026, does not rest solely on the transistor density of its Blackwell or Rubin GPUs. The true source of this dominance is a proprietary software ecosystem known as CUDA. This platform functions less like a tool and more like a sovereign border. It dictates who participates in the artificial intelligence economy and who remains exiled. For nearly two decades, Nvidia has constructed a around its hardware using this software. The walls of this are the subject of intense antitrust scrutiny across three continents.

The method of this control is technical the effect is purely economic. CUDA, or Compute Unified Device Architecture, launched in 2006. It allowed developers to use graphics cards for general-purpose processing. Nvidia spent years optimizing this language and building a vast library of pre-written code for mathematical operations. These libraries, such as cuDNN for deep learning and cuBLAS for linear algebra, became the standard dialect for AI development. By 2025, over 4 million developers relied on this stack. The trap lies in the compilation process. Code written in CUDA compiles down to PTX (Parallel Thread Execution) and eventually SASS (Streaming Assembler), which are instruction sets that run exclusively on Nvidia hardware. Porting this code to AMD or Intel chips is not a matter of simple translation. It requires a fundamental rewrite of the mathematical logic that underpins modern AI models.

This lock-in creates a self-reinforcing pattern. Startups and research labs choose Nvidia GPUs because the software libraries are mature. They write their own code in CUDA to interface with those libraries. This generates more CUDA-native applications, which forces the generation of hardware buyers to choose Nvidia again to ensure compatibility. The cost of exiting this ecosystem is prohibitive. A company wishing to switch to AMD Instinct accelerators must not only buy new hardware also pay engineers to refactor millions of lines of legacy code. This switching cost is the “moat” that investors celebrate. Regulators view it differently. They see it as an artificial barrier to entry maintained not by merit by exclusionary licensing.

The EULA Weaponization

The antitrust case against Nvidia pivoted sharply in 2024. Before this period, the company could that its dominance was simply a result of superior engineering. Yet the emergence of translation threatened this narrative. Projects like ZLUDA aimed to allow CUDA binaries to run on non-Nvidia hardware without modification. This technology would have allowed developers to keep their code switch their chips. It threatened to commoditize the GPU market. Nvidia responded not with better code with lawyers. The company updated its End User License Agreement (EULA) to explicitly ban the use of its software for translation purposes.

Clause 2. 8 of the updated licensing terms contains the smoking gun. It states that users 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 clause outlawed compatibility. It did not protect intellectual property in the traditional sense of preventing theft. It prevented interoperability. The legal threat was immediate and chilling. In August 2024, the developer behind ZLUDA removed the project code after AMD, which had quietly funded the effort, withdrew support citing legal risks. The message to the industry was clear. use CUDA, only if you pay the Nvidia hardware tax forever.

This contractual restriction is central to the investigations led by the French Competition Authority and the United States Department of Justice. The French regulator conducted dawn raids on Nvidia’s local offices in September 2023. By July 2024, reports confirmed that the authority was preparing a statement of objections. Their theory of harm focuses on the abuse of a dominant position. In the European Union, a company with a market share exceeding 40 percent has a special responsibility not to distort competition. With a market share in data center GPUs hovering between 86 percent and 92 percent throughout 2025, Nvidia sits well above this threshold. The French inquiry posits that the CUDA EULA restrictions serve no purpose other than to foreclose the market to competitors like AMD and Intel.

The Bundling Strategy

The Department of Justice in the United States has taken a parallel track. In September 2024, the DOJ issued subpoenas to Nvidia as part of an escalating antitrust probe. The investigation examines whether the company bundles its software and hardware in a way that penalizes customers who try to diversify their supply chain. The concern is that Nvidia uses its allocation power as a cudgel. During the chip absence of 2023 and 2024, access to H100 and Blackwell GPUs was the lifeblood of AI companies. Industry insiders alleged that Nvidia favored customers who used its entire stack, hardware, networking (InfiniBand), and software, while delaying shipments to those who flirted with competitors.

This behavior mirrors the tactics that landed Microsoft in federal court in the late 1990s. Just as Microsoft was accused of tying its web browser to the operating system to crush Netscape, Nvidia is accused of tying its CUDA software ecosystem to its GPU hardware to crush AMD and Intel. The EULA change regarding translation is the modern equivalent of Microsoft making Windows incompatible with rival software. It is an active measure to disable competition. The DOJ is also scrutinizing the acquisition of Run: ai, a software company that helps manage GPU workloads. Regulators fear this acquisition allows Nvidia to control the software that orchestrates chips, further entrenching its ability to block non-Nvidia hardware from data centers.

The Cost of the Walled Garden

The economic impact of this lock-in is measurable. By early 2026, the price of high-end AI accelerators remained artificially high because no viable substitute existed for the majority of CUDA-based workloads. Competitors like AMD’s ROCm and Intel’s OneAPI have improved, yet they cannot run the vast back-catalog of CUDA applications without significant friction. The ban on translation ensures this friction remains high. It forces the market to reinvent the wheel. Every competitor must rebuild the last 20 years of software libraries from scratch because Nvidia has legally forbidden the construction of a.

This strategy protects Nvidia’s margins, which hovered near 75 percent in 2025, a figure unheard of for a hardware commodity. These margins are a direct tax on the AI industry. Startups burn venture capital not on innovation on the “CUDA premium.” The antitrust scrutiny aims to this structure. If regulators succeed in clear down the exclusionary clauses in the EULA, it would legalize translation. This would allow AMD and Intel chips to run existing AI models instantly. The hardware market would become competitive overnight. Prices would fall. Innovation would accelerate as developers could choose chips based on performance per watt rather than software captivity.

The defense from Nvidia remains consistent. They that CUDA is a product of their own investment and they have no obligation to help competitors free-ride on their intellectual property. They claim the EULA changes protect the integrity of the software. Yet the timing of the ban, implemented exactly when rival hardware began to offer competitive performance, suggests the motivation was strategic foreclosure. The French and American investigations are racing to prove that this “protection” is actually an illegal restraint of trade. The outcome determine whether the future of artificial intelligence remains the property of a single corporation.

The Shift to Compulsory Process

The investigation into Nvidia Corporation underwent a dramatic escalation in September 2024. The United States Department of Justice transitioned from voluntary information requests to legally binding subpoenas. This move signaled that antitrust officials had moved past preliminary suspicion. They were gathering evidence for a chance enforcement action. The subpoenas targeted Nvidia and several other technology companies. These legal demands compelled the recipients to turn over internal communications. Investigators sought documents regarding sales strategies and pricing models. They also demanded records related to hardware allocation decisions. The shift to compulsory process indicated that Assistant Attorney General Jonathan Kanter and his team had identified specific theories of harm. The “fishing expedition” phase had ended. The prosecutorial phase had begun.

Antitrust division leadership had previously described their focus on “monopoly choke points.” The issuance of subpoenas confirmed that Nvidia’s control over the supply of H100 and Blackwell processors constituted such a choke point. The Department of Justice began examining whether Nvidia used this control to punish disloyalty. The investigation focused on allegations that the company retaliated against customers who engaged with competitors. This marked a significant departure from standard market dominance inquiries. Most antitrust cases focus on pricing or mergers. This case focused on the weaponization of the supply chain itself. The government sought to prove that Nvidia did not just win customers through superior engineering. The theory was that Nvidia retained customers through fear.

The Allocation Black Box

The core of the DOJ investigation revolves around the unclear method Nvidia uses to distribute its chips. In a functional market, a customer places an order and receives a delivery date based on manufacturing capacity. The market for AI accelerators does not function this way. Nvidia uses an “allocation” system to decide who gets chips and when. This system operates with minimal transparency. Industry insiders describe it as a black box where delivery schedules shift based on factors unrelated to the order date. The Department of Justice is investigating whether “loyalty” is one of those factors. Evidence suggests that the allocation process serves as a compliance method. Customers who align their entire infrastructure with Nvidia receive priority. Those who experiment with alternatives find their orders delayed.

This power creates a culture of silence. Executives at major cloud providers and AI startups have privately expressed terror at the prospect of angering Nvidia. They fear that a single public criticism could ruin their business. If a cloud provider cannot secure the latest GPUs, they cannot serve their clients. Their clients leave for a provider who has the hardware. This reality gives Nvidia life-or-death power over its customers. The DOJ subpoenas aim to uncover the internal criteria for these allocation decisions. Investigators are looking for emails or chat logs that link shipment delays to competitive behavior. Any evidence that a sales executive paused an order because a client met with AMD would be catastrophic for Nvidia’s defense.

Allegations of Retaliation

Specific complaints from rivals triggered the intensified scrutiny. Competitors like AMD and Groq have struggled to gain traction even with offering viable alternatives. Their struggle is not solely due to technical deficits. It is also due to the “fear tax” imposed on their chance clients. Jonathan Ross is the CEO of Groq. He publicly stated that customers are afraid to admit they are meeting with him. He claimed that clients would deny these meetings if Nvidia confronted them. This level of intimidation is rare in the technology sector. It suggests that Nvidia monitors the market for signs of defection. The DOJ is investigating whether Nvidia uses its market intelligence to identify these defectors and punish them.

Scott Herkelman formerly served as a senior vice president at AMD. He used the term “GPU Cartel” to describe Nvidia’s behavior. His comments reflect a widespread sentiment among hardware manufacturers. They believe Nvidia acts less like a vendor and more like a regulator of the AI industry. The retaliation tactics allegedly extend beyond simple delays. Reports indicate that Nvidia may restrict access to essential networking gear for disloyal clients. The H100 chips require specialized cabling and switches to function in large clusters. Nvidia dominates this networking market through its InfiniBand technology. The DOJ is examining whether Nvidia charges higher prices for networking gear if the customer buys chips from a rival. This practice would constitute an illegal tying arrangement under the Sherman Act.

The Run: ai Acquisition Probe

The Justice Department also focused its subpoenas on the acquisition of Run: ai. Nvidia announced its intent to buy the Israeli startup for approximately $700 million in early 2024. Run: ai specializes in software that optimizes GPU utilization. Their technology allows companies to run more AI workloads on fewer chips. This efficiency presents a strategic paradox for Nvidia. Nvidia’s revenue model depends on selling as chips as possible. A software tool that reduces the need for chips creates a conflict of interest. The DOJ investigation seeks to determine if Nvidia bought Run: ai to bury this technology. Alternatively, they may have bought it to ensure it only works with Nvidia hardware.

Regulators view this acquisition as a “killer acquisition.” This term describes a dominant firm buying a nascent threat to eliminate it. If Run: ai remained independent, it could have helped AMD or Intel chips perform better. It could have allowed data centers to mix and match hardware from different vendors. By absorbing Run: ai, Nvidia removed a tool that facilitated hardware agnosticism. The subpoenas requested documents detailing the post-acquisition plans for the Run: ai platform. Investigators want to know if Nvidia intends to restrict the software’s compatibility. Limiting Run: ai to CUDA-enabled devices would further entrench the software moat. It would force customers to buy Nvidia hardware to access the efficiency gains they need.

The French Precursor

The American investigation did not happen in a vacuum. It followed a similar aggressive action in Europe. French antitrust authorities raided Nvidia’s local offices in September 2023. This dawn raid involved law enforcement agents seizing physical and digital records. The French Competition Authority acted on concerns regarding the cloud computing sector. They suspected that Nvidia engaged in anticompetitive practices to lock out rivals. The materials seized in France likely provided a roadmap for American investigators. International cooperation between antitrust agencies has increased significantly under the Biden administration. Information shared between the French FCA and the US DOJ likely accelerated the decision to problem subpoenas.

The French inquiry highlighted the dependency on CUDA software. They noted that the industry’s reliance on this proprietary stack created high blocks to entry. The raid demonstrated that regulators were to use physical force to obtain evidence. It shattered the aura of invincibility surrounding the company. The DOJ took note of the findings from Paris. The specific allegations regarding “abuse of economic dependence” in France mirror the “retaliatory tactics” narrative in the United States. Both agencies are pulling at the same threads. They are unraveling a strategy that appears to rely on coercion rather than pure innovation.

The Networking Tie-In

A serious component of the DOJ’s theory involves the “full stack” argument. Nvidia does not just sell a chip. It sells a server rack. It sells the cables. It sells the switches. It sells the software. The company that this integration provides the best performance. Regulators that it kills competition. The subpoenas seek information on pricing bundles. Investigators suspect that Nvidia penalizes customers who try to break the bundle. A customer might want to buy Nvidia GPUs use Ethernet cables from Arista Networks or Cisco. that such customers face longer lead times for the GPUs. This forces the customer to capitulate. They buy the Nvidia networking gear to ensure they get the chips on time.

This tactic use a monopoly in one product to conquer an adjacent market. Nvidia has a monopoly on AI training chips. It does not have a natural monopoly on networking cables. By linking the two, Nvidia artificially its market share in networking. This harms companies that specialize in high-speed data transmission. It also increases costs for the end user. The DOJ is looking for internal memos that instruct sales teams to enforce this bundling. Proof of such instructions would be damning. It would demonstrate a deliberate intent to foreclose the market to competitors.

The Fear of “Zero Allocation”

The threat in the AI industry is “zero allocation.” This term refers to being completely cut off from Nvidia’s supply. For a cloud provider or an AI lab, this is a death sentence. The mere possibility of this outcome ensures compliance. Customers do not need to receive a written threat to understand the. They observe how Nvidia treats its partners. They see which companies get the headlines and the early shipments. They see which companies are left waiting. The DOJ investigation aims to document this implicit coercion. Investigators are interviewing executives who have felt this pressure. They are building a case based on the pattern of conduct. The subpoenas are the tool to turn these whispers into admissible evidence.

The timeline of shipments tells the story. If a customer announces a partnership with Intel and their Nvidia delivery slips by six months, the correlation is suspicious. If this happens to ten different customers, the correlation becomes a conspiracy. The DOJ is aggregating this data. They are matching public announcements of rival partnerships with private shipping logs. This data analysis form the backbone of any chance lawsuit. It moves the case beyond “he said, she said.” It grounds the allegations in hard numbers. The delay of thousands of H100 units to millions of dollars in lost revenue for the customer. It is a tangible economic harm.

The Dawn Raid: A Physical Breach of the Digital

On September 27, 2023, the abstract legal threats facing Nvidia materialized into physical reality. Officers from the French Autorité de la concurrence (Competition Authority) executed a surprise dawn raid on the company’s local offices in France. This operation was not a polite request for information; it was a seizure of evidence authorized by a liberty and custody judge. The raid marked the time a major regulatory body moved from observation to direct enforcement action against the GPU giant. Investigators seized physical and digital materials, looking for internal communications that would prove Nvidia used its market dominance to suffocate competition in the graphics card and cloud computing sectors.

The Autorité utilized its ex officio powers to launch this inquiry, bypassing the need for a specific external complaint to trigger the investigation. This aggressive maneuver signaled that French regulators viewed the situation as an urgent matter of economic sovereignty. While the agency initially withheld the name of the target, the Wall Street Journal and other outlets confirmed Nvidia as the subject within days. The raid focused on allegations that the company had implemented anticompetitive practices, specifically examining whether Nvidia restricted access to its hardware or used its software ecosystem to lock clients into its infrastructure.

The Statement of Objections: Formalizing the Accusation

Following months of analyzing the seized data, the investigation culminated in a formal “Statement of Objections” issued in July 2024. This document served as a prosecutorial charge sheet, accusing Nvidia of abusing its dominant position. France became the nation to formally charge the company, placing it ahead of the U. S. Department of Justice and the European Commission in the regulatory timeline. The charges alleged that Nvidia’s business practices went beyond aggressive competition and crossed into exclusionary conduct designed to eliminate rivals before they could gain a foothold.

The timing of these charges coincided with Nvidia’s ascent to the most valuable company in the world, briefly surpassing Microsoft and Apple. Yet, the French regulator remained undeterred by the company’s market capitalization. The Statement of Objections specifically targeted the symbiotic, and allegedly coercive, relationship between Nvidia’s GPU hardware and its proprietary CUDA software. The regulator argued that this bundle created a self-reinforcing barrier to entry that no competitor could reasonably breach, regardless of the technical merit of their alternative chips.

The CUDA Moat: Technical need or Illegal Lock-In?

At the heart of the French investigation lies the Compute Unified Device Architecture (CUDA). For nearly two years, the Autorité examined how this software functions not just as a tool, as a market gatekeeper. The regulator’s findings suggested that Nvidia’s dominance is not solely a result of superior silicon is artificially maintained by the CUDA ecosystem, which is 100% compatible only with Nvidia GPUs. This exclusivity forces developers to remain within the Nvidia walled garden, as porting code to open standards like OpenCL or rival platforms like AMD’s ROCm requires prohibitive amounts of time and capital.

The French charges posit that this absence of interoperability is a feature, not a bug. By ensuring that the industry standard for AI development runs exclusively on its hardware, Nvidia taxes the entire sector. The Autorité highlighted that this practice discourages innovation from rival chipmakers, as their hardware is rendered useless for the vast majority of existing AI software libraries. The investigation found evidence suggesting that Nvidia actively discouraged customers from exploring alternative software stacks, so cementing its monopoly through technical incompatibility rather than pure performance metrics.

The June 2024 Report: The Intellectual Blueprint

Prior to the formal charges, the Autorité published a scathing opinion on competition in the generative AI sector on June 28, 2024. This 100-page document provided the intellectual framework for the subsequent prosecution. In it, the regulator identified “high blocks to entry” caused by the accumulation of computing power and data in the hands of a few vertically integrated giants. The report explicitly named Nvidia, noting that the sector’s dependence on its chips and CUDA software represented a serious risk to fair competition.

The opinion detailed how the “accretion of market power” allows incumbent firms to strangle new entrants. It raised concerns about “price fixing, production restrictions, unfair contractual conditions, and discriminatory behavior.” The regulator noted that because Nvidia controls the supply of the scarce H100 and Blackwell chips, it holds the power of life and death over AI startups. The report suggested that Nvidia could, and perhaps did, allocate chips preferentially to firms that agreed to use its full software stack or to cloud providers in which it had a financial interest.

The CoreWeave Connection and Vertical Integration

A specific area of scrutiny in the French probe was Nvidia’s investment strategy, particularly its backing of CoreWeave, a cloud service provider focused exclusively on GPU acceleration. The Autorité investigated whether Nvidia provided CoreWeave with preferential access to scarce GPU inventory, so distorting the downstream cloud market. By favoring a partner that relies entirely on Nvidia infrastructure, the chipmaker could indirectly control the pricing and availability of compute power, bypassing the neutrality expected of a hardware supplier.

This vertical integration concern mirrors the tactics used by Standard Oil in the early 20th century, controlling not just the commodity (oil/chips) the distribution network (railroads/cloud). The French investigation sought to prove that these investments were not passive financial moves active strategic decisions to enforce the CUDA lock-in at the cloud server level. If Nvidia can determine which cloud providers succeed by controlling their supply line, it regulates the entire AI economy.

Benoît Cœuré: The Architect of Enforcement

The driving force behind this aggressive posture is Benoît Cœuré, President of the Autorité de la concurrence. A former central banker, Cœuré has taken a distinctively macroeconomic view of antitrust enforcement. He has publicly stated that the digital economy cannot function if “gatekeepers” are allowed to set the rules of engagement. Under his leadership, the French authority has shifted from reactive fines to proactive structural investigations. Cœuré confirmed in July 2024 that the investigation would result in charges if “fruitful,” a statement that was quickly followed by the formal objections.

Cœuré’s strategy involves clear early. By moving before the U. S. Department of Justice concluded its own probe, France established the legal facts on the ground. This ” -mover” advantage allows the French regulator to set the precedent for how CUDA is treated under competition law, not as a value-add service, as an essential facility that must be opened to competitors. His method challenges the Silicon Valley narrative that regulation stifles innovation, arguing instead that monopolies stifle innovation by preventing alternative solutions from reaching the market.

The Financial: A Multi-Billion Euro Threat

The financial of the French charges are severe. Under French and EU antitrust laws, a company found guilty of anticompetitive behavior faces fines of up to 10% of its global annual turnover. With Nvidia’s revenue skyrocketing past $60 billion in 2024 and continuing to climb through 2025, the chance penalty could exceed $6 billion (approximately €5. 5 billion). This would be one of the largest antitrust fines in history, surpassing the penalties previously levied against Google and Apple in Europe.

Yet, the monetary fine is secondary to the structural remedies the Autorité could demand. The regulator has the power to order behavioral changes, such as forcing Nvidia to make CUDA compatible with rival GPUs or requiring the company to divest from certain cloud partnerships. Such a ruling would strike at the core of Nvidia’s business model, the proprietary moat that protects its high margins. The threat of these remedies has forced Nvidia to engage legal counsel across multiple jurisdictions, as a ruling in France could serve as a template for the European Commission’s broader investigation.

Global Effects

The actions taken in Paris have resonated in Washington and Brussels. The evidence seized during the September 2023 raid has reportedly been shared with the European Commission, which is conducting a parallel informal inquiry. Also, the specifics of the French charges, focusing on the software-hardware bundle, have influenced the scope of the U. S. Department of Justice’s investigation. By isolating CUDA as the method of abuse, France has provided a roadmap for other regulators to follow.

This regulatory pressure has created a paradox for Nvidia. While its financial performance remains stellar, its legal foundation is fracturing. The French case demonstrates that regulators are no longer to accept “efficiency” as a defense for monopoly. The Autorité has made it clear: being the best is legal; preventing others from becoming the best is not. As the case moves toward a final ruling in 2026, the industry watches to see if the “French Exception” become the global rule.

The Silent Insertion: Weaponizing the End User License Agreement

In March 2024, the technology sector uncovered a quiet devastating alteration to Nvidia’s software licensing terms, a change that signaled a shift from competitive dominance to aggressive legal exclusion. Buried within the installation files of the CUDA 11. 6 toolkit was a new clause User License Agreement (EULA) that explicitly prohibited the use of translation , software designed to allow code written for Nvidia chips to run on competitor hardware. This revision was not announced via press release or developer conference; it was discovered by a software engineer known as “Longhorn,” who noticed the new language in the installed documentation, distinct from the online version which had arguably contained similar less enforced vague restrictions earlier. The specific text of the clause represents a precise legal strike against interoperability. It reads: “You may not reverse engineer, decompile or disassemble any portion of the output generated using Software elements for the purpose of translating such output artifacts to target a non-NVIDIA platform.” This sentence outlawed the operation of binary translation tools, which are essential for running compiled CUDA applications on hardware manufactured by AMD, Intel, or emerging Chinese semiconductor firms. By focusing on the “output artifacts”, the compiled binaries, Nvidia extended its control beyond the source code and into the executable files themselves, asserting a claim over how the software behaves even after it has left the developer’s hands. This legal maneuver fundamentally altered the nature of the CUDA ecosystem. Previously, Nvidia’s dominance was maintained by superior hardware performance and a rich library of software tools. The EULA revision 11. 6 transformed that merit-based advantage into a contractual lock-in. It created a scenario where a developer could legally write code using CUDA, a third party, or even the developer themselves, could face breach of contract allegations for attempting to run that compiled code on a Radeon or Arc GPU using a translation. This poisoned the binaries, ensuring that any software built with Nvidia’s toolkit remained tethered to Nvidia’s silicon, not by technical need, by legal threat.

The ZLUDA Threat: A Case Study in Disruption

The timing and specificity of the ban point directly to the rise of ZLUDA, an open-source project that demonstrated the fragility of Nvidia’s software moat. Developed by Andrzej Janik, ZLUDA began as a project to run CUDA applications on Intel GPUs. It functioned by translating the binary instructions intended for Nvidia’s hardware into instructions that other GPUs could understand and execute. This process, known as binary translation, is distinct from porting source code; it allows existing, compiled applications to run on new hardware without the original developer needing to lift a finger. Janik’s work was technically impressive and, for Nvidia, strategically dangerous. After an initial stint with Intel, the project was quietly funded by AMD for two years. During this period, ZLUDA evolved to support AMD’s ROCm platform, achieving near-native performance for CUDA applications. This capability threatened to the primary barrier to entry for AMD in the data center and AI markets: the massive backlog of legacy CUDA software. If ZLUDA worked, enterprise clients would not need to rewrite their codebases to switch to AMD; they could simply run their existing binaries on cheaper or more available AMD hardware. yet, in early 2024, AMD abruptly withdrew its funding for ZLUDA. While AMD did not publicly cite the EULA change as the sole reason, the correlation is impossible to ignore. Shortly after the funding stopped, Janik released the code as open source, revealing that it was capable of running proprietary CUDA renderers and AI workloads on Radeon cards. Nvidia’s EULA update appeared in the installed files around the same time the industry became aware of ZLUDA’s chance. The ban on “translating such output artifacts” was a direct torpedo aimed at the method ZLUDA used. It rendered the project legally radioactive for any corporate entity that risked being sued by Nvidia for facilitating a breach of license.

The Chinese Connection and Geopolitical Moats

Beyond the immediate rivalry with AMD and Intel, the EULA revision also targeted a growing threat from the East. As US export controls restricted the flow of high-end Nvidia GPUs to China, domestic Chinese chipmakers like Moore Threads and Biren Technology began accelerating their own hardware development. To make their chips viable, these companies needed access to the vast ecosystem of CUDA-based AI software. They began developing their own translation tools, such as Moore Threads’ MUSIFY, to allow CUDA code to run on their domestic architectures. The 11. 6 EULA update served as a preemptive strike against this “compatibility” strategy. By forbidding the translation of output artifacts to non-Nvidia platforms, Nvidia created a legal basis to pursue action against these Chinese firms or the international partners who might assist them. While enforcing US contract law in China presents challenges, the clause creates a chilling effect for global software vendors. A US-based software company selling a CUDA-accelerated application could technically be in violation of their Nvidia license if they knowingly allowed their software to be “translated” for use on a banned Chinese chip. This deputized software developers as enforcers of Nvidia’s hardware monopoly, forcing them to police where their binaries were executed. This move complicates the narrative of Nvidia as a purely neutral technology provider. It suggests a company actively fortifying its market position against geopolitical shifts and competitor innovation by leveraging intellectual property law to prevent interoperability. The ban does not protect trade secrets, reverse engineering for interoperability is generally protected under US and EU law, rather restricts the *use* of the software output, a distinction that antitrust regulators are likely to examine closely.

Antitrust: The Refusal to Deal

Legal experts and industry observers have drawn parallels between Nvidia’s EULA revision and the exclusionary tactics used by Microsoft in the 1990s. The prohibition on translation can be interpreted as a “refusal to deal” or an artificial restriction on interoperability designed to maintain a monopoly. In the landmark *Google v. Oracle* Supreme Court case, the judiciary recognized the importance of APIs (Application Programming Interfaces) in innovation and interoperability. By banning the translation of the *output* of these APIs, Nvidia is arguably circumventing the spirit of such rulings, using contract law to achieve what copyright law might not permit. The European Union’s competition authorities, known for their aggressive stance on tech gatekeepers, view such interoperability blocks with deep suspicion. The French Competition Authority’s raid on Nvidia’s offices was predicated on concerns about cloud computing and graphics card dominance; the EULA restriction provides concrete evidence of exclusionary conduct. It demonstrates an intent to lock customers into the Nvidia ecosystem not by making the ecosystem better, by making it illegal to leave. also, the clause creates a “catch-22” for competitors. To compete with Nvidia, they must support the dominant software standard (CUDA). Nvidia has made it a contractual breach to build the (translation ) necessary to support that standard. This forces competitors to rely on source-code porting tools like AMD’s HIP or Intel’s SYCL, which require developer buy-in and active code modification, a much higher barrier to entry than the direct binary translation offered by tools like ZLUDA.

The Chilling Effect on Open Source Innovation

The immediate victim of Revision 11. 6 was the open-source community. Projects like ZLUDA depend on the freedom to tinker and the legal safety of interoperability. By casting a legal shadow over the act of translation, Nvidia discouraged individual developers and small companies from contributing to or using these tools. Andrzej Janik’s struggle to find a sponsor after AMD’s withdrawal highlights this toxicity; chance backers must weigh the technical benefits of the tool against the risk of drawing Nvidia’s legal ire. This restriction also stifles academic and scientific progress. Researchers who use CUDA for simulations frequently want to run their models on whatever supercomputer is available, which might be powered by AMD Instinct or Intel Ponte Vecchio accelerators. The EULA technically forbids them from using a translation to move their compiled experiments to these machines, forcing them to either rewrite their code or wait for Nvidia hardware. This slows down scientific discovery in service of corporate market share., the EULA 11. 6 revision stands as a testament to Nvidia’s defensive posture. It reveals a corporation that recognizes its software monopoly is the only thing protecting its hardware margins. As hardware competitors close the performance gap, Nvidia has resorted to mining the that connect its island to the mainland, ensuring that the only way off the island is to swim—or to drown. This tactic, while in the short term, provides regulators with a clear, written example of anti-competitive intent, chance serving as the smoking gun in future antitrust litigation.

The Strategic Choke Point: Beyond Silicon

Nvidia’s 2020 acquisition of Mellanox Technologies for $6. 9 billion represents the single most decisive maneuver in its transition from a component vendor to a data center architect. While regulators initially viewed the deal as a vertical integration of complementary hardware, evidence suggests it functioned as the placement of a toll booth on the only highway capable of supporting modern AI workloads. By controlling the interconnects, the high-speed cables and switches that allow GPUs to communicate, Nvidia neutralized the threat of commoditized networking standards and created a hardware environment where its GPUs cannot reach peak performance without its proprietary plumbing. The industry standard for decades was Ethernet, a protocol championed by vendors like Cisco, Arista, and Broadcom. It allowed data centers to mix and match servers, storage, and switches from different manufacturers. Nvidia’s integration of Mellanox’s InfiniBand and the subsequent development of the NVLink protocol dismantled this interoperability. For high-performance computing (HPC) and large- AI training, the network is no longer a passive pipe; it is an active extension of the GPU itself.

The SuperPOD Mandate: “Only NVIDIA Networking”

The most visible manifestation of this exclusionary tactic is the DGX SuperPOD reference architecture. Marketed as the gold standard for AI infrastructure, the SuperPOD is not a suggestion; it is a rigid prescription. Technical documentation for the H100 SuperPOD explicitly states: “Only NVIDIA networking is supported.” This requirement forces enterprise customers and cloud providers to abandon existing vendor relationships. A data center manager cannot simply plug Nvidia DGX systems into a Cisco Nexus or Arista switch fabric and expect them to function as a cluster. The “compute fabric”, the inner ring where GPUs exchange gradients during training, must run on Nvidia Quantum InfiniBand or the newer Spectrum-X Ethernet, both of which are controlled by Nvidia. The technical justification relies on the need of Remote Direct Memory Access (RDMA) and in-network computing, features where Mellanox historically excelled. Yet, by hard-coding these dependencies into the DGX software stack and the management plane (Base Command), Nvidia ensures that introducing a third-party switch triggers compatibility errors or degrades performance to unacceptable levels. The message to the market is binary: buy the full stack, or build a crippled system.

Weaponizing the Interconnect: The DOJ and SAMR Probes

By late 2024 and throughout 2025, antitrust officials in the United States and China began investigating allegations that Nvidia weaponized this dominance to punish disloyalty. The U. S. Department of Justice (DOJ) focused on reports that Nvidia sales teams explicitly linked GPU allocation to networking purchases. Investigators examined claims that customers who attempted to pair Nvidia GPUs with Broadcom or Ethernet-based networking gear faced shipment delays or reduced allocation of the scarce H100 and Blackwell processors. More damning were allegations that Nvidia imposed a “competitor tax”: charging significantly higher prices for its networking cables and switches if the customer intended to use them with rival accelerators from AMD or Intel. In September 2025, China’s State Administration for Market Regulation (SAMR) escalated the scrutiny, accusing Nvidia of breaching the conditions set during the original Mellanox approval. The 2020 agreement required Nvidia to maintain interoperability and refrain from bundling. SAMR’s preliminary findings indicated that Nvidia had ignored these constraints, using technical incompatibilities and commercial pressure to force Chinese hyperscalers into an all-Nvidia ecosystem. The regulator noted that Nvidia’s control over both the computation (GPU) and the transmission (Mellanox) allowed it to strangle domestic competitors who could build chips absence the high-speed fabric to connect them.

NVLink and the Closed Loop

While InfiniBand secures the rack-level connection, Nvidia’s proprietary NVLink and NVSwitch technologies lock down the server internals. Standard PCIe connections, used by AMD and Intel, offer that pale in comparison to NVLink. With the release of the GB200 NVL72, Nvidia moved the switching fabric directly into the compute tray. The NVL72 design connects 72 GPUs as a single massive logical GPU. This architecture relies entirely on copper NVLink connections, bypassing standard optical networking within the rack. No third-party switch maker can participate in this domain. Broadcom, which supplies the Tomahawk and Jericho series chips that power the internet, is physically excluded from the most valuable real estate in the AI data center. The “backend” network, where the heavy lifting of AI training happens, is a closed loop owned 100% by Nvidia.

The Spectrum-X Offensive: Attacking Ethernet

Recognizing that enterprise data centers refuse to deploy InfiniBand due to its complexity, Nvidia launched Spectrum-X. This product line the Ethernet market, traditionally the stronghold of Arista and Broadcom. Nvidia markets Spectrum-X not as standard Ethernet, as “AI-optimized” Ethernet that requires both Nvidia switches and Nvidia BlueField SuperNICs to function correctly. This strategy attempts to bifurcate the Ethernet standard. There is “Standard Ethernet” (slow, lossy, unsuitable for AI) and “Nvidia Ethernet” (fast, lossless, proprietary). By marketing Spectrum-X as a requirement for maximizing GPU utilization, Nvidia extends its bundling tactics into the general enterprise market. A Chief Information Officer (CIO) looking to deploy a moderate-sized AI cluster is told that using their existing Arista switches result in “stranding” expensive GPU pattern. The economic pressure to buy Nvidia networking becomes irresistible, not because it is the only technical solution, because the cost of GPU is too high to risk.

The Competitor Squeeze

The impact on the broader networking industry is severe. Broadcom and the Ultra Ethernet Consortium (UEC) are racing to define an open standard that can match NVLink’s performance, they are fighting a moving target. Every time the open standard method parity, Nvidia releases a new proprietary iteration (e. g., NVLink 5. 0) that pushes the performance bar higher, keeping the ecosystem closed. For startups and rivals like AMD, the Mellanox tie-in creates a “chicken and egg” problem. Even if AMD produces a chip that matches the H100 in raw compute, they absence the mature, low-latency fabric required to that performance across thousands of nodes. Customers who buy AMD chips must cobble together networking solutions from third parties, introducing integration risks that do not exist in Nvidia’s walled garden.

Conclusion

The acquisition of Mellanox was not a merger; it was a annexation of the data center’s nervous system. By fusing the processor to the network, Nvidia has engineered a reality where competition at the component level is rendered irrelevant. A rival cannot simply build a better GPU; they must build a better data center architecture. The antitrust are clear: Nvidia has used its monopoly in one market (GPUs) to force a monopoly in another (networking), creating a self-reinforcing that no single competitor can.

### The Virtualization Trap The technical method of lock-in here is subtle yet absolute. Run: ai operates by hooking into Kubernetes, the standard for container management. It intercepts requests for compute resources and allocates them based on policies. By owning this interception point, Nvidia gains visibility into exactly how customers are using AI workloads across the industry. This data advantage is for competitors. Nvidia can see which models are being run, at wh, and with what, allowing them to tailor future CUDA updates to further entrench their hardware. also, the acquisition prevents the emergence of a standardized, cross-vendor virtualization interface. In the CPU market, virtualization software like VMware allowed x86 software to run on various hardware configurations with relative ease. A similar abstraction for GPUs would break the CUDA stranglehold. By buying the leading startup in this space, Nvidia ensures that GPU virtualization remains a proprietary feature rather than an open standard. The “open-source” pledges made during the acquisition process offer little solace; the history of tech acquisitions shows that open projects frequently languish or fork once the parent company shifts focus to proprietary integration. ### Retaliatory Capability through Software The integration of Run: ai also provides Nvidia with a new lever for enforcement. Antitrust complaints have frequently alleged that Nvidia delays shipments to customers who examine rival hardware. With Run: ai, the enforcement can become digital. A data center using a mix of Nvidia and non-Nvidia chips could find that the orchestration software “optimizes” workloads by routing all serious tasks to Nvidia silicon, leaving rival chips idle or assigned to low-priority jobs. This creates a self-fulfilling prophecy of performance: the Nvidia chips appear faster and more reliable simply because the scheduler favors them. This capability aligns with the broader pattern of “exclusionary bundling” identified in the DOJ’s subpoenas. Nvidia does not just sell a chip; it sells a mandatory ecosystem. The Run: ai deal closes one of the few remaining gaps in that ecosystem. It removes the option for a customer to say, “I use Nvidia for training AMD for inference, and manage it all with Run: ai.” The unified management pane is an Nvidia product, and it is unlikely to treat a competitor’s hardware as a -class citizen. ### The Illusion of Choice Nvidia’s defense of the acquisition rested on the claim that it would help customers save money by improving utilization. This argument mirrors the ” ” defense frequently used in monopoly cases. Yet, the savings come with a catch: they are only accessible if the customer remains within the Nvidia walled garden. The efficiency is a reward for loyalty, and the penalty for defection is technical obsolescence. The DOJ’s investigation into this deal, part of the broader antitrust probe, signifies a shift in regulatory strategy. Agencies are no longer looking solely at horizontal mergers (chipmaker buying chipmaker) at vertical integration that cements platform control. The Run: ai acquisition is a textbook example of a dominant firm buying a complement to prevent it from becoming a substitute. By controlling the software that makes chips, Nvidia ensures that no other chip can ever be enough to compete. The deal privatized the concept of GPU optimization, turning a general technical problem—how to use processors better—into a proprietary service sold by the dominant monopolist.

Discriminatory Pricing: Alleged Penalties for Rival Hardware Integration

The antitrust case against Nvidia Corporation has expanded beyond software lock-in to encompass allegations of punitive economic coercion. Investigators in the United States, France, and China are examining evidence that the company enforces a de facto “loyalty tax” on data center operators. The core of this scrutiny focuses on discriminatory pricing structures that allegedly penalize customers who integrate rival accelerators from AMD, Intel, or Groq. These financial penalties are not always explicit surcharges frequently manifest as the withdrawal of serious volume discounts or the imposition of unfavorable supply terms, raising the total cost of ownership for any entity attempting to build a multi-vendor infrastructure.

The “Full Stack” Premium

At the heart of the discriminatory pricing allegations is Nvidia’s “full stack” sales philosophy. While the company publicly markets its H100 and Blackwell GPUs as standalone products, industry insiders report that the most favorable pricing is reserved for customers who purchase the entire “DGX” ecosystem, comprising GPUs, Mellanox InfiniBand networking, and the AI Enterprise software suite. Reports from *The Information* and filings with the DOJ suggest that customers who attempt to decouple these components face steep financial consequences. A data center operator seeking to pair Nvidia GPUs with Ethernet networking from Broadcom or AI accelerators from AMD may find that the per-unit cost of the Nvidia GPUs increases significantly. This price differential is allegedly structured to erase the cost savings of using cheaper rival hardware, rendering the “hybrid” method economically irrational. By pricing the standalone GPU at a premium while offering deep discounts for the bundled solution, Nvidia imposes a tariff on diversification.

Retaliatory Supply Allocation

In the supply-constrained environment of 2023 and 2024, the most potent currency was not capital availability. Antitrust probes have uncovered testimony suggesting that Nvidia used shipment schedules as a method to enforce loyalty. Scott Herkelman, a former senior executive at AMD, publicly described Nvidia’s tactics as akin to a “GPU cartel,” alleging that the company delays shipments to customers who engage with competitors. This “retaliatory allocation” functions as a severe economic penalty. For a cloud provider or AI startup, a three-month delay in receiving H100 clusters can mean missing a serious model training window, leading to loss of market share and investor confidence. The Department of Justice has issued subpoenas investigating whether Nvidia’s sales teams implicitly or explicitly threatened to deprioritize orders for clients exploring alternative silicon. The fear of being sent to the “back of the line” has reportedly silenced customers, creating a market where loyalty to Nvidia is coerced through existential risk rather than earned through product superiority.

The Mellanox Lever and FRAND Violations

The 2020 acquisition of Mellanox Technologies gave Nvidia control over InfiniBand, the high-speed interconnect standard serious for linking thousands of GPUs in a supercomputer. China’s State Administration for Market Regulation (SAMR) approved this deal with strict conditions: Nvidia was required to supply Mellanox products on “fair, reasonable, and non-discriminatory” (FRAND) terms. In 2025, SAMR opened a formal investigation into violations of these commitments. The probe focuses on evidence that Nvidia began bundling Mellanox networking gear with its GPUs in a way that disadvantaged Chinese server makers who wanted to use domestic interconnects or alternative GPUs. The allegation is that Nvidia made the purchase of essential networking equipment conditional on the purchase of its compute units, or vice versa, violating the non-discrimination clauses. If a customer attempted to buy Mellanox cables to connect Huawei Ascend chips, they allegedly faced inflated prices or refusal of service. This investigation highlights how control over the “plumbing” of the data center allows Nvidia to exert pricing pressure across the entire hardware stack.

Operational “Taxes” on Multi-Vendor Systems

Beyond direct pricing, regulators are examining the operational costs imposed on mixed-hardware environments. Nvidia’s pricing for its AI Enterprise software suite is frequently tied to the underlying hardware. Allegations suggest that the licensing costs for essential orchestration tools skyrocket if the software detects non-Nvidia hardware in the cluster. This creates a “hidden tax” on interoperability. A data center attempting to run a 70/30 split of Nvidia and AMD chips must not only pay for the hardware also navigate a software licensing regime that penalizes the mixed environment. The French Competition Authority has flagged this as a chance abuse of dominance, noting that it artificially the cost of switching or diversifying. The result is a market where the “safe” financial option is total capitulation to the Nvidia ecosystem, while the “competitive” option carries punitive costs and logistical risks that few enterprises can afford to bear.

The 2020 Mandate: A Conditional Approval

The legal method for China’s antitrust assault on Nvidia was established years before the current trade war intensified. On April 16, 2020, the State Administration for Market Regulation (SAMR) became the final global regulator to approve Nvidia’s $6. 9 billion acquisition of Mellanox Technologies. Unlike approvals from the United States or the European Union, Beijing’s consent came with “restrictive conditions” (behavioral remedies) designed to prevent the exact scenario that unfolded in the subsequent half-decade.

SAMR’s 2020 decision explicitly prohibited Nvidia from engaging in exclusionary practices. The regulator mandated that the merged entity must not “attach any unreasonable trading conditions” to the sale of hardware in China. Specifically, the order forbade the tying of GPU accelerators with Mellanox’s high-speed networking equipment. The conditions required Nvidia to maintain interoperability with third-party networking hardware and to supply products on fair, reasonable, and non-discriminatory (FRAND) terms. These remedies were not indefinite suggestions; they were binding legal obligations set to remain in force for at least six years, covering the serious period of 2020 to 2026.

By late 2024, evidence mounted that Nvidia had systematically disregarded these constraints. As the demand for AI compute surged, Nvidia allegedly weaponized the scarcity of its China-specific chips, specifically the H20 and H800, to force the adoption of its full networking stack. Customers seeking allocation of these processors reported that sales representatives made delivery conditional on the simultaneous purchase of Mellanox InfiniBand switches and BlueField data processing units (DPUs). This tactic nullified the “choice” mandated by SAMR, converting the GPU monopoly into a networking monopoly.

The December 2024 Investigation

On December 9, 2024, SAMR formally announced an investigation into Nvidia for suspected violations of the Anti-Monopoly Law. The probe specifically the breach of the 2020 merger commitments. This action marked a significant escalation from routine regulatory oversight to a targeted enforcement campaign. The timing was not coincidental; it followed a tightening of U. S. export controls, yet the legal basis was grounded entirely in domestic antitrust statutes regarding the abuse of market dominance.

The investigation focused on the “tying” arrangement. Investigators gathered evidence showing that Nvidia’s sales structure penalized customers who attempted to integrate Nvidia GPUs with domestic networking solutions, such as those from Huawei or H3C. In high-performance computing (HPC) clusters, the interconnect is as important as the processor. By enforcing a hardware bundle, Nvidia ensured that its proprietary networking , specifically NVLink and the closed-source optimizations within InfiniBand, became the default standard in Chinese data centers, displacing open Ethernet alternatives.

This bundling strategy served a dual purpose. Finanically, it captured a larger share of the data center capital expenditure (CapEx). Strategically, it entrenched the CUDA software ecosystem deeper into the infrastructure. The Mellanox networking gear runs its own proprietary software stack, frequently referred to as DOCA (Data Center on a Chip Architecture). By tying the GPU to the network, Nvidia extended the “CUDA moat” to the entire server rack, making it technically and financially punitive for a customer to switch to a competitor’s GPU in the future, as the entire networking fabric would also need replacement.

September 2025: The Preliminary Finding of Guilt

The regulatory pressure culminated on September 15, 2025, when SAMR released the results of its preliminary inquiry. The findings were unequivocal: Nvidia had violated the Anti-Monopoly Law and breached the restrictive conditions of the Mellanox acquisition. The regulator stated that the company had failed to comply with the prohibition on forced bundling and had imposed unreasonable trading terms on Chinese clients.

This “preliminary” finding triggered the launch of a “further investigation,” a procedural step in Chinese antitrust law that precedes the calculation of penalties. Unlike the initial probe, which establishes whether a violation occurred, this phase focuses on the extent of the damage and the severity of the punishment. The announcement sent immediate shockwaves through the market, causing Nvidia’s stock to dip as investors calculated the chance financial exposure. Under China’s Anti-Monopoly Law, fines can reach up to 10 percent of a company’s annual revenue from the preceding year. For Nvidia, whose China revenue remained in the billions even with sanctions, the chance penalty could eclipse any previous antitrust fine levied against a foreign tech firm.

The September announcement also highlighted the failure of Nvidia’s “compliance” chips to appease Beijing. While the H20 GPU was designed to meet U. S. export performance caps, its commercial distribution became the vector for the alleged antitrust violations. SAMR’s stance indicated that complying with U. S. technical limits did not grant immunity from Chinese market conduct rules. The regulator’s message was clear: a monopoly position, even one constrained by foreign sanctions, cannot be exploited to crush domestic competition in adjacent markets like networking.

The Interoperability Breach

Beyond the financial tying, the investigation scrutinized the technical blocks erected by Nvidia. The 2020 approval required Nvidia to ensure its GPUs worked direct with third-party networking gear. yet, technical audits revealed that Nvidia’s drivers and CUDA libraries contained optimizations that were only active when detected alongside Mellanox hardware. When paired with non-Nvidia switches, the GPUs would frequently default to lower data transfer rates or disable specific direct memory access (RDMA) features.

This “soft” incompatibility rendered the theoretical choice of networking hardware an illusion. A data center operator could technically buy Huawei switches, the performance penalty imposed by the Nvidia software stack made such a decision economically irrational. This practice directly contravened the “interoperability” clause of the merger agreement. It demonstrated that the exclusionary tactics were not just policies of the sales department were hard-coded into the silicon and software architecture itself.

The probe also examined the “DOCA” software framework. Much like CUDA locked developers to Nvidia GPUs, DOCA aimed to lock data center architects to Mellanox DPUs. By integrating DOCA deeply into the AI training workflow, Nvidia created a dependency where the network card became an intelligent processor that only spoke the language of the Nvidia GPU. This integration violated the spirit, if not the letter, of the requirement to keep the networking division neutral and open to third-party accelerators.

Geopolitical and Market

The SAMR investigation represents a sophisticated counter-maneuver in the chip war. While the United States uses export controls to limit the capability of chips entering China, China is using antitrust law to limit the profitability and control Nvidia exercises within its borders. By targeting the bundling of networking gear, Beijing aims to break the full-stack dominance that allows Nvidia to dictate data center architecture.

If the “further investigation” results in a behavioral remedy alongside a fine, Nvidia could be forced to unbundle its sales officially. This would open the door for domestic Chinese networking companies to capture the interconnect market for AI clusters, even those running Nvidia GPUs. It would force a decoupling of the GPU-Network stack, weakening the “system-level” lock-in that is central to Nvidia’s trillion-dollar valuation thesis.

As of March 2026, the industry awaits the final penalty decision. The precedent set here determine whether multinational technology giants can continue to use product ecosystems to bypass local competition laws, or if the “walled garden” strategy be dismantled by regulators to use the full weight of antitrust statutes to protect their domestic supply chains.

The Shift to Formal Inquisition

Brussels escalated its scrutiny of Nvidia Corporation in December 2024. The European Commission moved beyond informal information gathering and issued formal questionnaires to the GPU giant’s rivals and customers. These documents signaled a transition from preliminary observation to active investigation under Article 102 of the Treaty on the Functioning of the European Union. The regulator sought evidence of abusive bundling practices that lock clients into the Nvidia ecosystem. This phase marked a dangerous pivot for the company. Antitrust officials no longer asked general questions about market. They demanded specific data regarding contractual obligations and pricing structures that penalize multi-vendor strategies.

Anatomy of the Questionnaire

The inquiry focused on the technical and commercial tying of hardware with software. Regulators asked recipients if Nvidia requires the purchase of specific networking equipment to access high-performance GPUs. This line of questioning directly targeted the integration of Mellanox InfiniBand interconnects with H100 and Blackwell accelerators. The Commission also examined whether Nvidia offers discounts that function as loyalty rebates. Such pricing method frequently induce customers to purchase a full stack of proprietary technology rather than mixing components from competitors like AMD or Intel. The questionnaire specifically requested internal emails and negotiation records that might show pressure tactics used by sales teams to enforce these bundles.

The Orchestration Software Trap

of the document addressed the role of GPU orchestration software. Following the scrutiny of the Run: ai acquisition the Commission investigated if Nvidia use its hardware dominance to force adoption of its workload management tools. The questionnaire asked if customers receive better hardware allocation or pricing when they agree to use Nvidia’s proprietary orchestration. This tactic would neutralize the utility of open-source alternatives that allow data centers to run workloads across different hardware brands. By tying the software to the physical chip the company creates a barrier where switching hardware providers a complete and costly rewrite of the software infrastructure.

Vestager’s Warning

Competition Commissioner Margrethe Vestager foreshadowed this escalation in July 2024. She described the supply of Nvidia GPUs as a huge bottleneck during a visit to Singapore. Her comments indicated that the Commission viewed the secondary market and open standards as important for innovation. The formal questionnaires followed her warning that dominant companies face special responsibilities under EU law. These responsibilities prohibit conduct that might be permissible for smaller firms is considered abusive when practiced by a monopoly. The Commission aims to determine if Nvidia restricts the ability of rivals to compete on merit by manipulating the supply chain through these bundled contracts.

The Threat of Article 102

A finding of abuse under Article 102 carries severe consequences. The European Commission holds the power to impose fines of up to 10 percent of a company’s global annual turnover. For Nvidia this penalty could amount to tens of billions of dollars. The investigation also threatens the core business model of the company. A decision against Nvidia could force the unbundling of CUDA from GPU hardware or mandate interoperability with rival networking standards. Such a remedy would the proprietary moat that protects the company’s high margins. The regulator is currently analyzing the terabytes of data returned in response to the questionnaires. This analysis determine if a Statement of Objections is the logical step.

Parallel Pressure from France

The European Commission’s action runs parallel to the investigation by the French Autorité de la concurrence. France conducted dawn raids on Nvidia’s local offices in September 2023. The French regulator focuses on similar problem operates under its own national timeline. Evidence gathered during the Paris raids likely informed the broader European strategy. The coordination between national and supranational bodies creates a pincer movement. Nvidia faces a unified front of regulators who share data and legal theories. The questionnaires sent by Brussels reflect a deep understanding of the technical nuances of the AI stack. the authorities have already secured technical advisors capable of dissecting the complex interplay between CUDA libraries and GPU architecture.

Market Reaction and Defense

Nvidia maintains that its success from superior engineering rather than exclusionary tactics. The company that customers choose its full stack because it offers the best performance for AI workloads. Yet the detailed nature of the EU questionnaires suggests that regulators are skeptical of this merit-based defense. They are looking for evidence of coercion. The inquiry asks if customers fear retaliation in the form of delayed shipments if they refuse to buy the bundled software. In a market where GPU supply is the lifeblood of AI development the threat of delay is a potent weapon. The Commission seeks to prove that this fear, rather than technical superiority, drives the high attach rates of Nvidia’s ancillary products.

The Loyalty Rebate method

The investigation places heavy emphasis on the structure of discounts. Antitrust law views conditional rebates as a tool for foreclosure when offered by a dominant firm. The Commission asked customers to quantify the financial penalty they would incur if they switched a portion of their procurement to a rival. If the loss of the discount on the dominant product exceeds the savings from the rival product the pricing scheme is considered exclusionary. This mathematical test is a standard tool in EU competition law. The questionnaire required detailed accounting data to perform this calculation. Regulators are building a quantitative case to show that no rational competitor can match Nvidia’s prices without selling cost.

Looking Ahead

The deadline for responses to the questionnaires passed in early 2025. The Commission is in the assessment phase. Legal experts anticipate that the sheer volume of complaints from rivals compel the regulator to move forward. The focus on “inducement” and “technical tying” indicates that the case rely on established legal precedents from the Microsoft and Intel eras. Yet the speed of the AI market adds a of urgency. A remedy that comes five years from may be too late to preserve competition. This reality drives the aggressive pace of the current investigation. Brussels intends to intervene before the market structure calcifies completely around the CUDA ecosystem.

The Binary Threat: Deconstructing the ZLUDA Interoperability

In the annals of the GPU antitrust investigation, few chapters illustrate the active suppression of interoperability as as the rise and forced retreat of ZLUDA. While Nvidia publicly attributes its market dominance to superior hardware engineering and the organic adoption of its software ecosystem, the ZLUDA timeline reveals a different narrative: a calculated legal and strategic campaign to destroy a technology that threatened to render the CUDA moat permeable. ZLUDA was not a competitor; it was a translation capable of running compiled CUDA binaries on non-Nvidia hardware with near-native performance. Its existence proved that the barrier keeping developers locked into Nvidia GPUs was not an technical hurdle, an artificial legal construct maintained through exclusionary licensing. The project, spearheaded by developer Andrzej Janik, originated as an experimental effort to the gap between Nvidia’s proprietary compute language and Intel’s hardware. When Intel declined to pursue the project commercially, citing a perceived absence of business value, AMD stepped in. For two years, beginning roughly in 2022, AMD quietly funded the development of ZLUDA. This period, which investigators refer to as the “covert pilot” phase, represented a direct attempt by Nvidia’s primary hardware rival to commoditize the CUDA software stack. Unlike previous translation tools that required developers to recompile their source code, a friction point that frequently deterred adoption, ZLUDA operated at the binary level. It allowed existing, already-compiled CUDA applications to execute on AMD’s ROCm platform without modification. This capability struck at the heart of Nvidia’s vendor lock-in strategy.

The EULA Counter-Strike and Legal Encirclement

Nvidia’s response to the emergence of binary translation was neither technical competition nor performance optimization. Instead, the corporation deployed its legal department to erect a new barrier. As detailed in previous sections regarding the EULA modification, Nvidia introduced a specific clause into its licensing agreement, starting with CUDA 11. 6, that explicitly prohibited the use of its software elements for the purpose of translating output artifacts to non-Nvidia platforms. This clause was not a standard protection of intellectual property; it was a targeted weapon designed to illegalize the function of tools like ZLUDA. By modifying the terms of use, Nvidia created a legal minefield for any enterprise considering ZLUDA. While the software itself might be technically sound, using it to run CUDA libraries on AMD hardware constituted a breach of contract. This maneuver neutralized the threat of ZLUDA without Nvidia needing to alter a single line of code in its drivers or hardware. The “chilling effect” was immediate. Corporate legal teams, risk-averse by nature, advised engineering departments to avoid any tool that could invite litigation from the $3 trillion incumbent. The EULA change demonstrated an intent to maintain monopoly power not by innovating faster, by outlawing the method that would allow customers to switch providers.

AMD’s Retreat and the Takedown Demand

The effectiveness of Nvidia’s legal intimidation became clear in early 2024. even with the technical pledge of ZLUDA, which showed performance metrics on AMD Radeon GPUs rivaling native execution on Nvidia hardware for certain workloads, AMD abruptly terminated its funding. The decision, shrouded in non-disclosure agreements, signaled a capitulation to the legal risks established by Nvidia’s licensing updates. AMD, rather than challenging the validity of the exclusionary EULA clause in court, chose to withdraw. The situation escalated in August 2024 when AMD’s legal department demanded the removal of the ZLUDA code from public repositories. Janik had released the code under an open-source license, believing he had secured permission via email to do so upon the contract’s termination. AMD’s lawyers subsequently argued that the email approval was not legally binding. This reversal forced the removal of the code, burying the progress made during the two-year funded period. The takedown request suggests that AMD feared contributory liability, that by distributing a tool designed to bypass Nvidia’s restrictions, they could be sued for inducing breach of contract or interfering with Nvidia’s business relationships. This sequence of events highlights how a dominant firm’s restrictive licensing can coerce competitors into policing their own innovation to avoid litigation.

The Guerilla Resurrection: Clean-Room Engineering

The suppression of ZLUDA did not end the project, it forced it into the shadows. Following the AMD takedown, Janik announced a “clean-room” rewrite of the software, funded by an unnamed “stealth” sponsor. This new iteration, developed without any of the code paid for by AMD, aimed to restore the functionality while circumventing the specific copyright claims that might arise from the previous corporate sponsorship. By late 2025, the resurrected ZLUDA had achieved support for ROCm 7, demonstrating resilience that Nvidia’s legal team likely did not anticipate. This guerilla phase of development, yet, operates under a permanent cloud of uncertainty. Without the official backing of a major hardware vendor like AMD or Intel, ZLUDA remains a tool for hobbyists and researchers, rather than a viable enterprise solution. Large- data centers and AI startups cannot build their infrastructure on a library that exists in a legal gray zone, maintained by a small team and funded by anonymous sources. Nvidia has successfully relegated a serious competitive threat to the fringes of the ecosystem. The “moat” remains intact not because the is impossible to build, because Nvidia has successfully criminalized the act of crossing it.

Technical Viability vs. Artificial blocks

The tragedy of the ZLUDA saga lies in the technical reality it exposed. Benchmarks conducted by independent reviewers in 2024 and 2025 showed that ZLUDA could run complex CUDA applications, including rendering engines and scientific simulations, with minimal performance overhead. In specific vector operations, the translation on AMD hardware actually outperformed native CUDA execution on equivalent Nvidia GPUs. These findings the long-standing argument that CUDA’s dominance is solely the result of deep hardware-software integration that cannot be replicated. The existence of a functional binary translation proves that the instruction sets are not so as to prevent interoperability. The barrier is legal and commercial, not physical. Nvidia’s refusal to allow translation is analogous to a operating system vendor banning the use of emulators that allow their software to run on rival computers. In other sectors, such conduct has triggered immediate antitrust intervention. The European Commission’s questionnaires, sent to industry participants in late 2024, specifically probed this area, asking whether Nvidia’s licensing terms prevented the porting of code. ZLUDA serves as Exhibit A in this line of inquiry.

The 2026 Standoff

As of March 2026, ZLUDA represents a dormant chance. The software exists, the code is available, and the technical capability to break the CUDA monopoly is proven. Yet, the ecosystem remains frozen. Developers continue to write CUDA code, knowing it locks them to Nvidia, because the alternative—relying on a legally besieged translation —presents an unacceptable business risk. Nvidia has used its EULA to extend its patent-like protection over the *functionality* of its API, a legal theory that remains untested in high court in the market. The “Operation ZLUDA” narrative confirms that the suppression of cross-platform compatibility is a deliberate strategy. It involves the coordination of technical obfuscation, aggressive licensing restrictions, and the implicit threat of litigation against any entity, regardless of size, that attempts to the divide. For antitrust regulators, the case of ZLUDA provides concrete evidence of exclusionary conduct. It demonstrates that the monopolist is not competing on the merits is actively the ladders that would allow customers to climb out of its walled garden. The suppression of this project denied the market a genuine choice and artificially prolonged the lifespan of the CUDA monopoly, imposing higher costs and reduced innovation on the entire AI sector.

Supply Allocation as use: Coercing Exclusivity from Cloud Providers

In the high- theater of generative AI, the H100 GPU is not a component; it is the currency of survival. For cloud service providers (CSPs), securing a steady stream of these accelerators determines whether they thrive or wither. Investigative scrutiny has pierced the veil of Nvidia’s allocation committees, revealing a system where hardware delivery is allegedly contingent upon total fealty to the CUDA ecosystem. By weaponizing the scarcity of its own silicon, Nvidia has deputized a new class of “neoclouds”, such as CoreWeave and Lambda Labs, to serve as loyal vassals, while simultaneously throttling the supply lines of hyperscalers who dare to design their own rival chips.

The “Neocloud” Vassal State

Nvidia’s strategy to counter the independence of Amazon Web Services (AWS), Google Cloud, and Microsoft Azure is the cultivation of a parallel, compliant infrastructure. Companies like CoreWeave and Lambda Labs, once niche players, have been elevated to kingmakers through preferential access to Nvidia’s most advanced silicon. This relationship is not subtle. In 2023 and 2024, while AWS and Azure faced months-long backlogs for H100 clusters, CoreWeave, backed by direct Nvidia investment, secured thousands of units, allowing it to offer immediate availability. This preferential treatment comes with strings attached. These providers operate as “Nvidia shops,” offering infrastructure that is purely optimized for CUDA and devoid of rival accelerators like AMD’s MI300 or Intel’s Gaudi. By funneling supply to these dependent partners, Nvidia creates a market where availability is synonymous with exclusivity. The message to the broader market is unambiguous: loyalty yields inventory; exploration of alternatives yields delays. This tactic mirrors the controversial GeForce Partner Program (GPP) of 2018, adapted for the data center, an unwritten “Green Partnership” where the penalty for defection is business failure.

DGX Cloud Lepton: The Digital GPP

The coercion method has evolved beyond simple hardware shipments into the software with the introduction of **DGX Cloud Lepton**. Ostensibly a marketplace to connect developers with GPU capacity, Lepton functions as a strategic choke point. By aggregating supply from its partners into a single Nvidia-controlled interface, the company inserts itself between the cloud provider and the end customer. For a CSP to participate in Lepton, and thus access the lucrative stream of enterprise AI workloads routed by Nvidia, they must adhere to strict hardware and software standardization requirements. This commoditizes the cloud provider’s infrastructure while cementing Nvidia’s software stack as the immutable interface. Reports indicate that participation in such programs is frequently a prerequisite for receiving “Elite” partner status in the Nvidia Partner Network (NPN). This status is not a vanity metric; it is the gatekeeper classification that determines priority during supply constraints. Consequently, cloud providers are forced into a prisoner’s dilemma: adopt Nvidia’s full stack and surrender customer control to Lepton, or maintain independence and face the “sold out” sign.

Retaliation and the “GPU Cartel”

The enforcement of this regime is reportedly punitive. Scott Herkelman, former head of graphics at AMD, has publicly characterized Nvidia as a “GPU cartel” that controls all supply and retaliates against defectors. Industry insiders describe an atmosphere of fear where CSPs conceal their testing of rival chips (such as Groq’s LPUs or Amazon’s Trainium) to avoid “shipping delays” or “allocation de-prioritization.” The Department of Justice (DOJ) and the French Competition Authority are actively investigating these retaliatory tactics. Their probes focus on whether Nvidia modifies shipment schedules or pricing based on a customer’s purchase of competing hardware. Evidence suggests that “bundling” extends beyond software; it includes the physical coercion of buying Nvidia’s InfiniBand networking gear (Mellanox) to secure GPU orders. A cloud provider attempting to pair Nvidia GPUs with standard Ethernet or Broadcom networking gear frequently finds their GPU lead times extending indefinitely. This “tying” ensures that Nvidia captures the entire value of the data center rack, not just the compute slot.

The Hyperscaler Squeeze

The target of these tactics is the “Big Three” hyperscalers. AWS, Google, and Microsoft have the capital to design custom silicon (Trainium, TPU, Maia) that could eventually break the CUDA monopoly. Nvidia’s allocation strategy is a direct counter-measure to this existential threat. By starving these giants of the necessary volume of H100s to meet peak demand, Nvidia forces their customers to migrate to the “neoclouds” or use Nvidia’s own DGX Cloud service, which runs atop the hyperscalers’ infrastructure under Nvidia’s terms. This creates a parasitic: Nvidia rents the bare metal from a provider like Azure, installs its own software, and resells it to the enterprise customer at a premium. The hyperscaler is reduced to a dumb pipe, providing power and cooling while Nvidia captures the high-margin software revenue and retains the customer relationship. Resistance is futile; refusing to host DGX Cloud could result in a severe reduction of GPU allocation for the hyperscaler’s own public cloud offerings.

Regulatory “Dawn Raids” and the Paper Trail

The unclear nature of these allocation decisions makes antitrust enforcement difficult not impossible. The “dawn raids” conducted by French authorities and the subpoenas issued by the DOJ aim to uncover the internal communications that codify these unwritten rules. Investigators are looking for the “smoking gun”: internal emails or slide decks that explicitly link allocation tiers to the exclusion of rival hardware. While Nvidia publicly maintains that supply is allocated based on “readiness” and “efficiency,” the correlation between a CSP’s loyalty and their inventory levels is statistically. The “readiness” metric is frequently a proxy for “CUDA-only.” A data center designed to host a mix of AMD and Nvidia chips is deemed “less ready” than one built to Nvidia’s exact reference architecture. This circular logic allows Nvidia to claim meritocratic allocation while enforcing strict exclusionary conduct.

The are severe. By controlling the physical of the AI revolution, Nvidia dictates the pace of innovation for the entire industry. A cloud provider cannot compete on price or performance if they cannot get the chips. The “supply constraint” is not a manufacturing bottleneck; it is a governance method. Until regulatory intervention forces a transparent, audit-based allocation process, the cloud remains a fiefdom where the Lord of Silicon decides who eats and who starves.

The ‘Full Stack’ Trap: Tying DGX Systems to Enterprise Software

Nvidia’s transformation from a component manufacturer to a “data center- computing” entity represents more than a marketing pivot; it constitutes a calculated architectural strategy designed to eliminate modularity in the artificial intelligence supply chain. By redefining the unit of computing from the individual Graphics Processing Unit (GPU) to the integrated DGX system, Nvidia has constructed a walled garden that extends from the silicon transistor to the application. This “full stack” method, while technically defensible under the guise of performance optimization, functions economically as a method to enforce software tying arrangements that violate the spirit, and chance the letter, of global antitrust statutes. The core of this strategy lies in the inextricable linkage between the company’s coveted hardware—specifically the H100 and Blackwell generations—and its proprietary enterprise software suite, creating a dependency loop that competitors cannot break. The primary instrument of this lock-in is the DGX appliance model. Unlike traditional server procurements where enterprise customers purchase commodity chassis from OEMs like Dell or HPE and populate them with selected accelerators, the DGX is sold as a monolithic “AI supercomputer in a box.” While this standardization simplifies deployment, it serves a darker purpose: the mandatory attachment of software licensing. Investigative analysis of procurement contracts for the H100 NVL and H200 NVL series reveals that these units are frequently sold with a non-negotiable, five-year subscription to Nvidia AI Enterprise (NVAIE). This software suite, marketed as the “operating system for AI,” includes essential libraries, frameworks, and validation tools required to run the hardware at peak efficiency. By bundling the software cost into the hardware acquisition price, Nvidia imposes a tax on the hardware that subsidizes its software dominance. Customers cannot opt out of the NVAIE subscription to use open-source alternatives or competitor-optimized stacks without forfeiting support or facing technical blocks. This practice mirrors the “tying” arrangements prohibited under the Sherman Act, where a monopolist forces the purchase of a tied product (software) as a condition of obtaining the tying product (the GPU). For a data center operator deploying thousands of units, this mandatory subscription represents hundreds of millions of dollars in operational expenditure that is contractually funneled back to Nvidia, starving chance software rivals of revenue and user base. The exclusionary nature of this ecosystem is further cemented by **Nvidia Base Command**, the cluster management and orchestration platform designed to control DGX infrastructure. Base Command acts as the central nervous system for AI training jobs, managing resources, scheduling workloads, and monitoring telemetry. Yet, its architecture is aggressively proprietary. It is engineered to manage Nvidia silicon exclusively, treating non-Nvidia hardware as second-class citizens or incompatible endpoints. Once an enterprise standardizes its workflow on Base Command, introducing AMD Instinct or Intel Gaudi accelerators becomes operationally impossible. The “single pane of glass” pledge becomes a prison; the cost of re-architecting the orchestration to accommodate a second vendor exceeds the chance savings from cheaper hardware, insulating Nvidia from price competition. This strategy escalated significantly with the introduction of **DGX Cloud**, a service that thrust Nvidia into direct competition with its own best customers—the hyperscale cloud providers (CSPs) like AWS, Microsoft Azure, and Google Cloud. In a move characterized by industry insiders as a “Trojan Horse” maneuver, Nvidia leveraged its allocation power during the peak H100 absence of 2023 and 2024 to coerce CSPs into hosting DGX Cloud instances. Under this arrangement, the CSP provides the power, cooling, and floor space, Nvidia retains control over the customer relationship, the software stack, and the pricing. This disintermediation strategy allowed Nvidia to rent “bare metal” from the CSPs and resell it as a premium, fully managed AI service. The antitrust are severe. By forcing CSPs to host a competing service to receive chip allocation, Nvidia engaged in a vertical squeeze. The CSPs were relegated to the role of utility providers, while Nvidia captured the high-margin software and service revenue. Reports from late 2025 indicate that this aggressive posture eventually backfired, leading to a restructuring of the DGX Cloud division after significant pushback from Azure and AWS. Yet, the existence of the program demonstrates Nvidia’s intent to use its hardware monopoly to force its way up the value stack, foreclosing the cloud market to competitors who absence a comparable hardware lever. The “Grace Hopper” Superchip (GH200) and subsequent GB200 Blackwell designs represent the physical manifestation of this exclusionary philosophy. By integrating the ARM-based Grace CPU directly with the Hopper GPU via the high- NVLink-C2C interconnect, Nvidia eliminates the traditional PCIe bottleneck. While the performance benefits are real, the anticompetitive side effect is the total removal of the x86 CPU from the equation. In a standard server, a customer could pair an Nvidia GPU with an AMD EPYC or Intel Xeon processor. In the Grace Hopper architecture, the CPU and GPU are fused. This integration forecloses the market for rival CPU manufacturers within the high-performance AI segment. It is a hardware bundle solidified in silicon; to get the best GPU performance, the customer must abandon the x86 ecosystem entirely. Regulators have taken notice of this “system-level” dominance. The **French Competition Authority (FCA)**, in its June 2024 opinion and subsequent raid justifications, explicitly flagged the “cloud computing sector” and the risks of abuse arising from Nvidia’s dual role as a supplier and a competitor. The FCA’s analysis highlighted how the combination of CUDA, NVAIE, and DGX hardware creates high blocks to entry. They noted that the “full stack” strategy allows Nvidia to cross-subsidize its products, using high margins on GPUs to artificially lower the entry cost of its software, or conversely, using software lock-in to maintain high hardware prices. This creates a feedback loop where the more “integrated” the solution becomes, the harder it is for a modular competitor to gain a foothold. also, the pricing mechanics of the NVAIE license reveal a discriminatory structure. Evidence suggests that the cost of running Nvidia’s enterprise software stack on non-DGX hardware (e. g., commodity servers from Supermicro or Gigabyte) is frequently structured to be less attractive than the bundled DGX rate. This pricing incentivizes the purchase of the proprietary appliance over the modular alternative. It is a subtle form of predatory pricing, where the bundle is priced to undercut the sum of the parts if those parts were sourced from a mix of vendors. The “Full Stack” trap also impacts the independent software vendor (ISV) market. By bundling a detailed suite of pre-trained models, transfer learning tools, and inference engines (Nvidia NIMs) with the hardware, Nvidia reduces the addressable market for third-party MLOps and AI platform companies. Startups attempting to build better orchestration or model management tools find themselves competing against a “free” (bundled) product that comes pre-installed on the hardware. This stifles innovation in the software, as venture capital dries up for tools that directly compete with the NVAIE stack. In the context of the Department of Justice investigation, these tying arrangements are serious. The DOJ is examining whether Nvidia’s refusal to allow its software to run on rival hardware—or its technical obfuscation that degrades performance on non-DGX systems—constitutes an illegal maintenance of monopoly power. The “technical need” defense frequently by Nvidia—that the hardware and software must be tightly paired for performance—is being scrutinized against internal documents that may reveal a deliberate engineering choice to break compatibility., the DGX system is not a product; it is a compliance device. It enforces the use of the entire Nvidia stack, ensuring that every dollar spent on AI infrastructure flows to a single entity. The “Full Stack” strategy nullifies the concept of a multi-vendor data center. For the enterprise customer, the short-term ease of deployment offered by the DGX appliance comes at the long-term cost of total vendor capture. Once the data center is filled with DGX units, the software workflows are hardened around Base Command, and the developers are trained on NVAIE tools, the switching costs become. This is not a moat; it is a, built with the bricks of exclusionary bundling and the mortar of proprietary interconnects. The retreat of the DGX Cloud initiative in late 2025 does not absolve the company of the anticompetitive intent behind its creation. It signals that the hyperscalers, possessing their own immense market power, were able to resist the encroachment. Smaller enterprises and second-tier cloud providers, absence such use, remain trapped in the full stack ecosystem. As the industry moves toward the trillion-parameter model era, the requirement to use the “full stack” to achieve necessary performance benchmarks ensures that Nvidia’s dominance is self-perpetuating, immune to the corrective forces of a free market. The “trap” is set, and for the majority of the AI industry, the door has already closed.

The transition of Nvidia’s rivals from aggressive marketing to active regulatory testimony marks a definitive shift in the semiconductor industry’s power. For years Advanced Micro Devices (AMD) and Intel Corporation fought Nvidia in benchmark charts and server racks. By 2024 and 2025 they had moved the battlefield to the deposition rooms of the U. S. Department of Justice and the European Commission. The core of their testimony alleges that Nvidia’s dominance is no longer a result of superior silicon alone is maintained through a calculated campaign of market foreclosure. These complaints describe an environment where the CUDA software stack functions not as a tool for developers as a digital perimeter fence designed to repel interoperability and punish defection.

The “Shallow Moat” and the UXL Alliance

Intel CEO Pat Gelsinger became the most vocal public critic of this in late 2023. He famously characterized Nvidia’s CUDA moat as “shallow and small” during a launch event in New York. His argument rested on the industry’s shared desire to migrate toward open standards like Python and PyTorch. Yet this public bravado masked a more urgent strategic maneuvering occurring behind closed doors. Gelsinger’s testimony to regulators reportedly emphasized that while the industry wanted to move away from CUDA it was structurally prevented from doing so by Nvidia’s exclusionary licensing and hardware bundling.

This frustration birthed the Unified Acceleration Foundation (UXL). Formed in late 2023 and expanding through 2025 this coalition includes Intel, Google, Qualcomm, and Samsung. Their explicit goal is to build an open-source software suite capable of running AI code on any machine regardless of the underlying chip. While publicly framed as a technical collaboration the UXL Foundation serves as a central pillar in the antitrust argument against Nvidia. It provides tangible evidence that the entire technology sector, minus Nvidia, is forced to pool resources to construct a bypass around a single company’s proprietary blockade. The existence of UXL demonstrates that market forces alone have failed to break the lock-in.

The ZLUDA Incident: A Smoking Gun for Foreclosure

The most damaging evidence provided by competitors revolves around the suppression of translation. For years developers sought ways to run CUDA-compiled applications on non-Nvidia hardware without rewriting the code. A project known as ZLUDA emerged as a promising solution. It allowed CUDA binaries to run on AMD’s ROCm platform with near-native performance. Intel and AMD both reportedly provided funding or technical support to the project at various stages to interoperability.

Nvidia’s response was swift and legalistic. With the release of CUDA 11. 6 the company quietly updated its End User License Agreement (EULA). The new clause explicitly prohibited the use of reverse engineering to translate CUDA output for non-Nvidia platforms. This change outlawed ZLUDA and similar compatibility tools. AMD and Intel pointed to this EULA revision in their complaints to the French Competition Authority and the DOJ. They argued that banning translation serves no technical security purpose. Its only function is to destroy the that would allow customers to leave the Nvidia ecosystem. This specific tactic mirrors the “embrace, extend, extinguish” strategies of the 1990s browser wars and has become a focal point for investigators examining exclusionary intent.

AMD’s “Wartime Mode” and the Software Barrier

AMD CEO Lisa Su has taken a pragmatic yet aggressive stance. While Gelsinger attacked the moat verbally Su mobilized AMD’s engineering resources into what analysts called “Wartime Mode” by early 2025. This shift occurred after internal and external reports criticized AMD’s ROCm software stack for bugs that made training large models nearly impossible. Su’s testimony and public comments acknowledge that hardware performance is irrelevant if the software creates friction. The MI300X accelerator matched or beat Nvidia’s H100 in raw metrics. Yet adoption lagged because the software ecosystem remained fragmented.

Competitors have told regulators that Nvidia exploits this fragmentation. Reports from the DOJ investigation indicate that rival chipmakers provided evidence of Nvidia sales representatives warning customers about “compatibility risks” if they mixed hardware. The allegation is that Nvidia defines “compatibility” as strict adherence to the full proprietary stack. When a customer attempts to integrate an AMD GPU for inference while keeping Nvidia GPUs for training they face artificial bottlenecks. These include slower interconnect speeds or disabled features in the software management. This technical foreclosure forces Chief Information Officers to standardize on Nvidia to avoid operational headaches.

Retaliatory Allocation and the “Networking Tax”

The most serious allegations involve direct economic retaliation. Testimony from competitors and anonymized customers suggests that Nvidia use its supply chain dominance to coerce exclusivity. During the peak absence of 2023 and 2024 demand for the H100 GPU far outstripped supply. Competitors allege that Nvidia prioritized allocation to customers who agreed to buy the “full stack” which includes not just the GPU also Nvidia’s networking cables, switches, and software licenses.

Information provided to the DOJ reportedly details a “networking tax.” If a cloud provider wanted to buy Nvidia GPUs use standard Ethernet cabling or rival networking gear from Broadcom or Cisco Nvidia would allegedly delay the GPU shipment or charge a premium. This bundling practice forecloses the market for high-speed interconnects. It forces rivals like AMD and Intel to compete not just against a GPU against an entire pre-integrated data center architecture. Customers fear that purchasing a cluster of Intel Gaudi 3 or AMD MI300 chips result in their Nvidia allocation being cut for the pattern. This fear freezes the market. It prevents diversification even when rival products are cheaper or more available.

The PyTorch Pivot and the Pythonic Defense

Intel and AMD have also centered their defense on the evolution of PyTorch. They that modern AI development is moving to a “Pythonic” that abstracts the underlying hardware. By optimizing PyTorch 2. 0 to run on any backend the industry attempts to commoditize the GPU. Nvidia’s counter-strategy has been to CUDA-specific libraries deep into the workflow before the Python is even reached. Competitors pointed out that Nvidia’s libraries for data loading (DALI) and communication (NCCL) are optimized to work exclusively with proprietary hardware. This forces rivals to reverse-engineer basic utility functions before they can even run the benchmark.

The testimony from AMD and Intel paints a picture of a market that is technically broken. They that the “meritocracy” Nvidia claims to lead is an illusion maintained by legal threats and supply chain bullying. The UXL Foundation and the push for open standards represent a desperate attempt to rebuild the road while driving on it. Until regulators intervene to the artificial blocks of the CUDA EULA and the bundling practices competitors that no amount of silicon innovation be sufficient to correct the market imbalance.

The Brussels-Washington-Beijing Triangle

By March 2026, the regulatory perimeter around Nvidia had tightened into a synchronized siege. The era of, jurisdiction-specific inquiries has ended. In its place, a global enforcement network has emerged, characterized by high-level intelligence sharing and a convergence of legal theories. The Department of Justice in the United States, the European Commission in Brussels, and the State Administration for Market Regulation (SAMR) in China have surrounded the corporation. While their geopolitical motivations differ, their operational target is identical: the exclusionary bundling of CUDA software with GPU hardware. This phenomenon represents a historic shift in antitrust enforcement, moving from reactive fines to proactive, coordinated structural pressure.

The operational nerve center for the transatlantic portion of this offensive is the U. S.-EU Joint Technology Competition Policy Dialogue (TCPD). Established to align digital enforcement strategies, this body transformed into a tactical war room regarding Nvidia’s market dominance throughout 2024 and 2025. Meetings between Assistant Attorney General Jonathan Kanter and Executive Vice President Margrethe Vestager moved beyond diplomatic pleasantries. They focused on the specific mechanics of “lock-in” effects. The dialogue allowed U. S. prosecutors to examine the evidence seized during the French Competition Authority’s dawn raids in September 2023. Those raids, initially viewed by the market as a localized flare-up, provided the raw data, internal emails, discount structures, and supply allocation logs, that underpins the DOJ’s subpoenas issued in late 2024. The French authority acted as the evidence extraction team, while the DOJ assumed the role of the primary litigator, using the seized documents to build a case under the Sherman Act.

China’s Strategic Offensive: The Mellanox Breach

While Western regulators coordinated on theories of software exclusion, Beijing opened a second front focused on merger conditions. On September 15, 2025, SAMR issued a preliminary finding that Nvidia had violated the Anti-Monopoly Law regarding its 2020 acquisition of Mellanox Technologies. This ruling marked a severe escalation. The Chinese regulator alleged that Nvidia failed to honor commitments to keep Mellanox’s InfiniBand networking equipment interoperable with third-party hardware. SAMR’s investigation, which began in December 2024, concluded that Nvidia used its post-merger control of Mellanox to penalize Chinese server manufacturers who attempted to integrate rival accelerators, such as those from Huawei or Biren Technology.

The timing of the SAMR decision was calculated. By issuing a violation finding rather than just opening a probe, Beijing placed a specific financial threat on the table: a fine of up to 10% of Nvidia’s revenue in China. For the fiscal year ending January 2025, this exposure amounted to approximately $1. 7 billion. Yet the financial penalty is secondary to the strategic use. The ruling forces Nvidia to negotiate behavioral remedies with Beijing at the exact moment it attempts to defend its business model in Washington. This creates a “pincer movement” where concessions made to satisfy Chinese interoperability demands could be used by U. S. and EU regulators as proof that technical blocks to entry are artificial and removable. If Nvidia can unlock interoperability for Chinese clients to avoid a fine, Western regulators demand the same openness for AMD and Intel.

The Asian Supply Chain Revolt: South Korea and Japan

The regulatory contagion has spread to the semiconductor supply chain powerhouses of South Korea and Japan. The Korea Fair Trade Commission (KFTC) abandoned its historically passive stance with the release of its “Generative AI and Competition” report in December 2024. This document, the of its kind from the KFTC, explicitly identified the “tying” of proprietary software stacks to hardware as a primary threat to the domestic AI ecosystem. The KFTC’s concern is rooted in national industrial policy. South Korean conglomerates like Naver, KT, and Samsung are attempting to build sovereign AI capabilities find themselves dependent on Nvidia’s allocated supply. The KFTC report warned that vertical integration by a dominant foreign supplier could permanently stunt the growth of Korea’s domestic NPU (Neural Processing Unit) sector.

Following the report, the KFTC moved to strengthen its penalty guidelines in March 2025, specifically targeting “unfair business opportunity provision.” This legal concept allows the regulator to punish dominant firms that use their market position to deny competitors access to essential inputs. In the context of Nvidia, the “essential input” is not just the GPU, the CUDA libraries required to train models on those GPUs. Japan’s Fair Trade Commission (JFTC) has signaled a parallel trajectory, initiating a market study in late 2025 that mirrors the European method. The between Seoul, Tokyo, and Brussels creates a unified regulatory bloc across the G7, ensuring that Nvidia cannot play one jurisdiction against another to preserve its closed ecosystem.

The Convergence of Legal Theories

A distinct pattern has emerged in the legal arguments used by these agencies. In previous tech monopoly cases, regulators frequently struggled to define the relevant market. Google argued it competed for “attention,” while Facebook argued it competed for “social connection.” With Nvidia, regulators have converged on a precise definition: the market for “accelerated computing training and inference.” Within this defined market, the theory of harm is uniform. It posits that CUDA is no longer a value-added feature an “essential facility”, infrastructure so serious that denying access to it, or degrading its performance on rival hardware, constitutes an antitrust violation.

The DOJ and the European Commission are treating the CUDA-GPU bundle as a tie-in arrangement similar to the Microsoft browser cases of the 1990s. The argument is that Nvidia is using its monopoly in the hardware market to maintain an illegal monopoly in the software development market, which in turn protects the hardware monopoly. This circular reinforcement is the core of the “moat” that regulators intend to breach. The SAMR ruling adds a of “refusal to deal” theory, arguing that Nvidia’s restrictions on Mellanox interoperability amount to a refusal to supply essential networking components to rivals. This convergence means that Nvidia’s defense team cannot use contradictory arguments in different courts. A claim made in Delaware that “CUDA is integral to the hardware” be used in Brussels to prove illegal tying.

The Threat of a Global Settlement

The simultaneous pressure from the US, EU, and China raises the probability of a forced “global settlement” or a series of cascading judgments that shatter the current business model. In 2026, Nvidia faces a choice. It can continue to litigate in each jurisdiction, risking a breakup order in the US or a massive revenue seizure in China. Or, it can propose a set of global remedies. Legal analysts predict that regulators demand the “unbundling” of CUDA from the hardware. This would require Nvidia to document its APIs publicly and allow translation like ZLUDA to function without legal threats. It would also mandate that Nvidia sell its GPUs without the “CUDA tax,” creating a market for bare-metal hardware where rival software stacks can compete on merit.

Such a remedy would decimate the company’s gross margins. Nvidia’s valuation is built on the premise that it sells a complete, proprietary platform, not just silicon commodities. If the software barrier falls, the hardware becomes fungible. Hyperscalers like Microsoft and Google, who are already designing their own chips, would immediately switch their software stacks to open standards like OpenAI’s Triton or the Linux Foundation’s UXL, breaking the pattern of dependency. The regulators know this. Their coordinated actions are not designed to extract fines, which Nvidia can easily pay from its cash reserves. They are designed to commoditize the hardware by liberating the software.

The End of the Walled Garden

The investigation into Nvidia is the major test of the post-globalization antitrust framework. It demonstrates that when a single company captures the infrastructure of the future economy, nations suspend their geopolitical rivalries to break that hold. The DOJ does not care that SAMR is a Chinese regulator; it cares that SAMR has evidence of supply chain manipulation. The European Commission does not care that the KFTC is protecting Korean chaebols; it cares that the KFTC has established a precedent for defining CUDA as an essential facility. This cross-pollination of enforcement has stripped Nvidia of the ability to hide behind jurisdictional borders.

As 2026 progresses, the timeline for a resolution accelerates. The DOJ is expected to file its formal complaint before the summer recess. The European Commission is preparing a Statement of Objections based on the Article 102 TFEU probe. SAMR is awaiting Nvidia’s final response to the violation finding. The walls of the green garden are being breached from the outside. The question remains whether Nvidia the gate voluntarily to save the castle, or watch as regulators tear down the walls stone by stone. The era of the proprietary AI monopoly is drawing to a close, not through market competition alone, through the blunt force of sovereign law.