Dh🅰️rmesh Patel

From DGX to DSX — NVIDIA’s Secret Weapon Is $IREN DGX was the pivotal turning point that transformed NVIDIA from a chip company into a systems company. From the original ambition of creating a “unified data center standard,” DGX encountered resistance from the hyperscalers. They refused to adopt NVIDIA’s unified standard and instead developed their own chips, frustrating NVIDIA’s vision of becoming the dominant systems platform of the AI era. Google is perhaps the most notable example: after initially falling out of the core AI race, it rapidly recovered and mounted a full-scale counterattack, at one point nearly matching NVIDIA’s market capitalization and challenging NVIDIA’s status as the “godfather” of AI. DGX failed to conquer the cloud giants’ strongholds. NVIDIA’s massive sales still primarily came from individual GPU chips, while its plan to establish DGX as a new systems standard combining GPUs and software did not succeed. However, strategically, DGX laid an extremely important foundation for NVIDIA. Customers could reject the complete DGX system, but they still had to remain compatible with NVIDIA’s software management stack, otherwise GPU performance could not be fully utilized. As a result, technologies such as NVLink, NVSwitch, and Base Command matured alongside the market, enabling NVIDIA to evolve from simply selling GPUs into a company with full-stack platform control capabilities, while solidifying its dominance in scientific computing and private cloud markets. Entering the Blackwell era, the physical limits of power consumption, interconnect complexity, and liquid cooling made it impossible for the industry to continue operating independently. NVIDIA formally introduced the standardized AI factory architecture known as DSX, positioning it as the optimal path for building large-scale AI data centers. From this point onward, DGX evolved into DSX. In other words, it evolved from a “single-machine AI supercomputer” into a “data-center-scale AI factory standard,” completing the transition from standardizing one machine to standardizing an entire factory. During the Blackwell generation, AI training systems pushed power consumption, interconnect complexity, and thermal management close to physical limits: single rack power draw surpassed hundreds of kilowatts, NVLink/NVSwitch topologies became dramatically more complex, and liquid cooling shifted from optional to mandatory. In theory, this generation already required a standardized architecture like DSX. However, the supply chain ecosystem was not yet mature, and no partner possessed the full engineering capability necessary to build a true “system-level AI factory.” As a result, DSX remained only a concept and reference design. By the Vera Rubin era, NVLink 6, NVSwitch 6, and NVL72 rack systems formed a scalable, reproducible interconnect foundation, finally giving DSX the conditions necessary for practical deployment using NVIDIA’s full-stack technology. But that alone was still insufficient. To fully realize DSX, the industry also required: High-density interconnected rack architecture capabilities Large-scale liquid cooling expertise and construction experience GW-scale single-site campuses with stable long-term power supply These became the necessary conditions for constructing a flagship DSX factory. And only one company in the world possesses all three simultaneously. At this point, IREN enters the stage. Beyond those three core requirements, IREN possesses several additional strategic characteristics: Grid-based power supply. First, grid power solves the stability problem. To become a flagship DSX standard site, power interruptions and voltage fluctuations are unacceptable. Large-scale grid infrastructure provides industrial-grade voltage stability guarantees. Second, relying on the grid offers superior cost economics. Third, it provides regulatory compliance as public infrastructure, removing the unpredictable risks often associated with behind-the-meter (BTM) power systems, which frequently carry “gray-area” or temporary characteristics and therefore lack sufficient long-term reliability. GW-scale infrastructure. This enables the creation of multiple DSX modular standards. Small and medium-sized data centers become trivial by comparison — deployments from 10MW to over 1GW can all be standardized. This makes IREN the ideal flagship demonstration platform. We already know there will likely be SW2 and potentially additional nearby expansion sites. The total power capacity is enormous. DSX only truly begins with Rubin, and the upgrade path beyond that will continue for many years. Therefore, possessing ultra-large campus-scale sites within a single region is critically important. This advantage makes IREN the one unavoidable choice for NVIDIA. No other company possesses such massive strategic power infrastructure concentrated within a single region. The long-term significance and moat of such infrastructure can hardly be overstated. Small scattered sites stitched together — even if they collectively total several GW — are simply incomparable to IREN’s grid-connected GW-scale campuses concentrated in single regions. Green energy. As global concern over AI energy consumption rises, future “carbon footprint” metrics will become core evaluation standards for sovereign AI procurement. IREN’s long-term commitment to renewable energy allows NVIDIA’s DSX standard to become not only “the most powerful,” but also “the greenest.” This is critically important for attracting national-level infrastructure customers. Owned land and expansion capability. DSX requires data centers to be constructed from the ground up, including specialized transformers, ultra-heavy rack support systems, and complex liquid cooling pipelines. Only companies with full ownership of their land can customize AI factories entirely according to NVIDIA’s blueprint without facing endless approval bottlenecks or third-party building restrictions. Vertical integration and data center engineering expertise. IREN is not merely a data center operator. It is one of the only vertically integrated companies in the industry that owns everything from greenfield development, site development, power procurement, to operations and maintenance. For a DSX flagship factory, NVIDIA needs a partner capable of rapidly executing its “reference designs.” IREN’s model of “designing, building, and operating everything itself” dramatically shortens the timeline from blueprint to first deployed GPU. Liquid cooling capability. DSX is fundamentally a liquid-cooled era architecture. Liquid cooling becomes a central requirement. IREN already possesses high-density rack deployment experience through the Horizon project. Its Chief Innovation Officer is one of the most influential and experienced engineering experts in the United States in data center liquid cooling, high-density thermal architecture, and ASHRAE standards systems. He joined IREN specifically to help establish standards. Long-term operational data accumulation. IREN has years of operational experience managing large-scale, high-heat-density facilities running at full load. The physical environment of Bitcoin mining is remarkably similar to AI inference: both involve 24/7 full-load operations with extreme thermal output. This long-term expertise in managing massive electrical and thermal loads is, in reality, an extremely competitive advantage within the industry. From the analysis above, one can understand why IREN possesses such uniqueness and strategic importance in NVIDIA’s DSX ecosystem, while also inferring the likely development path of DSX itself: DSX will likely follow a “top-down” design philosophy. Using IREN’s massively scalable GW-scale sites and specialized engineering capabilities, NVIDIA can define a flagship standard that is “multi-scale, most advanced, most efficient, and greenest,” then deconstruct that blueprint into modular, reproducible AI factory units. In the future, whether it is a GW-scale campus or merely a company operating a single row of racks, as long as they purchase NVIDIA’s “DSX-certified package,” they could theoretically produce tokens with the same efficiency as IREN. This strategy of “defining the upper limit, then distributing the standard downward” reflects NVIDIA’s true ambition to control the global AI infrastructure ecosystem. IREN’s Sweetwater site — along with future surrounding expansion campuses — could become the incubation base for future AI intelligence factories. The scale of this project may become one of the largest engineering undertakings in human industrial history: “Intelligent factories produce intelligence, and DSX defines how those factories are built and run.” This concept has already moved beyond theoretical logic into actual execution. The reason I am able to describe this vision is because I have been observing this direction consistently for a long time. In reality, developments do appear to be moving this way. The broader historical backdrop behind the emergence of the DSX system comes primarily from three major forces: First, the rapid development of the AI industry has positioned DSX at the center of a major inflection point in compute infrastructure. DSX is a natural product of the industry reaching a new stage of maturity. AI is no longer confined to internal model training inside a few hyperscalers. The entire world now requires AI compute — including sovereign AI, enterprise private AI, neo-clouds, AI inference platforms, agent networks, token factories, vertical-specific models, and national AI infrastructure. Many countries — particularly in the Middle East, Europe, and Southeast Asia — are unwilling to place core AI workloads inside the public clouds of U.S. tech giants due to data sovereignty concerns. Through DSX templates, NVIDIA can help these nations rapidly build their own “national AI factories.” Hyperscalers can no longer monopolize AI infrastructure. This has become one of the most important changes of the past two years, and it forms the foundational soil for DSX to grow. Second, hyperscalers themselves are now constrained by power, land, permitting, transformers, and cooling systems. They are no longer in a state of unlimited expansion. AI inference also requires broader distributed deployment. In the future, there will be large numbers of regional AI factories, national AI nodes, and enterprise private clusters whose operators do not want to rely entirely on hyperscalers. Meanwhile, Google TPU, Amazon Trainium, and Microsoft Maia are all rapidly advancing. Over time, they may reduce GPU purchases, form closed ecosystems, and sell their own AI services externally — creating a strategic threat to NVIDIA. Therefore, NVIDIA must cultivate a “non-hyperscaler AI ecosystem.” Third, by the Blackwell and Vera Rubin eras, single-rack power consumption has already reached the 100kW–200kW range. Traditional air cooling, cabling, and power topology can no longer support these systems. This means that if data centers are not built according to NVIDIA’s DSX standards — system-level liquid cooling, GB200 NVL72 architecture, and related infrastructure — they simply will not be able to run the highest-efficiency compute systems. In other words, physical laws themselves are forcing the market to adopt NVIDIA’s standards. DSX effectively becomes the “entry ticket” to the AI era. Under this backdrop, DSX attempting to define the entire AI factory standard becomes a completely natural progression. It encompasses GPU architecture, network topology, liquid cooling standards, power design, rack standards, software orchestration, inference optimization, and token factory production pipelines — reflecting an ambition to turn AI compute into something like an “industrial iPhone operating system.” After understanding the broader context, one can then better appreciate the deeper strategic meaning behind IREN’s acquisition of Mirantis. To build a standardized flagship DSX factory, IREN already possesses massive GW-scale physical infrastructure, liquid cooling capability, and engineering expertise, but it still lacked the software layer needed to bridge “hardware” and “cloud services.” Mirantis perfectly fills this gap. Its deep experience in OpenStack, Kubernetes, and bare-metal management enables IREN to transform DSX into a directly usable cloud platform, allowing customers to immediately deploy AI workloads out of the box. For NVIDIA, this acquisition enables its key partner IREN to free DSX from dependence on AWS, Google, and other cloud giant software ecosystems, establishing an independent vertically integrated stack. For IREN, the acquisition elevates it from a power and infrastructure supplier into a true “neo-cloud” platform capable of delivering sovereign AI and national-scale AI infrastructure. Mirantis will also integrate NVLink topologies and DSX-specific features directly into software orchestration, enabling AI factories to achieve automated scheduling and token-level operational stability. Although CRWV and NBIS also possess software with somewhat similar functionality, their stacks are largely designed for internal use and are difficult to standardize for export. Mirantis, by contrast, is inherently a cloud-native software company serving global customers. This allows IREN to transform DSX into an exportable “software-defined AI factory” template. Its core product, k0rdent, can unify bare metal, virtual machines, and Kubernetes management while deeply optimizing for NVIDIA GPUs — a capability IREN could not realistically develop internally. One could speculate that NVIDIA itself encouraged this acquisition (especially given how inexpensive the deal appeared, with IREN seemingly receiving extraordinary value). The ultimate objective may be to give DSX an independent software control layer outside AWS and Google while creating a sovereign AI solution deliverable globally. Mirantis upgrades IREN from a hardware host into the software brain of DSX, while giving NVIDIA a strategic ally in global AI infrastructure that is open-source-oriented, conflict-free, economically aligned, and technologically synchronized. NVIDIA choosing not to acquire Mirantis directly — instead allowing IREN to do so — likely centers on avoiding antitrust concerns, maintaining delicate relationships with hyperscalers, and ensuring the software layer remains closely aligned with practical AI factory operations. An IREN acquisition appears as ecosystem collaboration rather than market domination. At the same time, Mirantis software must deeply integrate with IREN’s GW-scale power, liquid cooling, and operations systems, making IREN the more efficient owner. Financially, NVIDIA benefits through warrants tied to IREN’s growth without needing to bear integration costs itself. Through this strategy, NVIDIA effectively supports the emergence of a fully aligned DSX flagship manufacturing partner while preserving its own asset-light structure and strategic control position. A full-scale DSX rollout would potentially: Form the foundation for NVIDIA reaching a $10–15 trillion valuation Become the inevitable path for NVIDIA’s vision of AI intelligence factories and operational control Represent the most economical and efficient path for AI industry development Solve the post–Vera Rubin scaling direction for compute growth Become NVIDIA’s only viable method for breaking out of hyperscaler encirclement IREN becoming the sole top-level collaborator in such a massive project could not have happened spontaneously. Planning something of this scale would likely require at least a year or more of preparation. Ever since interactions between NVIDIA and IREN began to appear unusually secretive, I have noticed multiple examples suggesting unusual behavior between the two companies — almost like two people who already know each other pretending not to in public. Overall, they likely did not want the industry to speculate too early about their true intentions, while also minimizing regulatory attention. Even IREN, once an unusually transparent Bitcoin mining company, has become more guarded. In that sense, the limited interaction between IREN’s investor relations team and the market may actually make sense. At this point, IREN has already completed the most difficult parts of its AI industrial expansion: High-quality, massive-scale, long-term stable power supply, still growing further Secured supply access to the latest GPUs Developed engineering teams and supply chain maintenance capabilities Obtained status as a flagship manufacturing partner for next-generation AI intelligence factories The next inevitable step is filling IREN’s enormous power capacity with high-quality customer contracts. Unlike before, however, IREN may no longer need to build a traditional sales force or aggressively market its software capabilities. NVIDIA itself would likely help facilitate customer adoption while emphasizing the superior token-generation efficiency of the DSX system, because the economic interests of both companies are now deeply aligned. Under the DSX standard, NVIDIA could gradually evolve from a “supplier” into a “global orchestrator.” Securing partnerships with companies like Anthropic would no longer be solely IREN’s concern. NVIDIA itself has strong incentives to push major AI companies already experimenting with TPU systems toward using more NVIDIA-based infrastructure. Second, NVIDIA holds massive warrants in IREN. Every major contract signed by IREN potentially increases its stock price, allowing NVIDIA not only to profit from GPU sales but also from appreciation in IREN’s equity value. Jokingly speaking, one could say IREN “used warrants to buy itself a world-class salesman.” Third, the emergence of sovereign AI has opened an entirely new market. Since IREN acquired Mirantis, the term “sovereign AI” has appeared increasingly frequently. In fact, when evaluating IREN’s sites originally, many observers already noted their suitability for sovereign AI deployments. The strategic quality of IREN’s sites is fundamentally incomparable to the fragmented infrastructure assembled by many competitors. For NVIDIA, it needs a GW-scale “pure-blood” flagship to demonstrate to sovereign AI customers globally that NVIDIA’s DSX architecture can achieve superior token efficiency. Sovereign AI customers may not want to hand their compute, data, models, or orchestration layers to the three major U.S. hyperscalers, but they may still accept supplier sovereignty. The distinction is subtle but important. IREN’s careful positioning and boundary management become critical here. Even the Mirantis acquisition did not overextend into hyperscaler territory; in fact, sovereign AI is already one of Mirantis’ core areas. From this perspective, NBIS may actually be poorly positioned for sovereign AI because its full-stack platform structure is precisely what sovereign AI customers are attempting to avoid. Overall, IREN appears to be positioning itself at a point that maximizes strategic optionality and economic upside. If it attempted to define itself as a fully integrated hyperscaler-like platform, cooperation with a company at NVIDIA’s level would likely become far more difficult. This partnership with NVIDIA may sacrifice some of IREN’s historical emphasis on flexibility and optionality, but technological evolution tends to follow efficiency. The emergence of the “Magnificent Seven” itself demonstrates that antitrust frameworks increasingly must adapt to technological realities. For IREN, the most important objective during this enormous capital expenditure cycle is rapidly establishing scale advantages. These data center assets ultimately become long-term hard assets fully owned by the company. The more infrastructure accumulated now, the greater IREN’s strategic flexibility becomes in the future. From that perspective, this is an extremely rational strategy. As IREN gradually becomes one of the standard-setters for the next-generation compute ecosystem, it could eventually open additional monetization paths such as standardized AI factory design fees, consulting and licensing revenue, and software licensing income. Compared to its core business, these may remain relatively small, but the strategic value of occupying the top layer of the ecosystem could become nearly limitless. Many people — especially institutions — already seem to recognize these dynamics. IREN’s stock price may not have risen dramatically yet, but its trading volume appears to reveal something unusual. The volume itself has become almost phenomenon-level behavior. Meanwhile, IREN’s $6 billion ATM facility has remained active, and immediately after earnings the company issued a $2 billion convertible bond deal, later increased to $3 billion due to overwhelming demand. The intensity of demand, favorable interest rates, and high conversion prices were genuinely surprising. If the narrative described above is even partially correct, such investor enthusiasm becomes entirely understandable. Furthermore, the remaining $5 billion of ATM financing demand will likely be sold at significantly higher prices. At this point, CRWV, NBIS, NSCALE, and LAMBADA increasingly appear to function as alliance members within NVIDIA’s broader ecosystem. Capital markets have seen constant fighting among supporters of the three neo-cloud stocks, especially between NBIS and IREN supporters — almost to the point of ideological warfare. But IREN may ultimately represent NVIDIA’s final and most important strategic move: the piece that controls the overall board. Importantly, IREN achieved this position through its own decisions and execution. It was not merely “chosen” or artificially supported. Yet at the same time, NVIDIA likely must publicly deny any direct support relationship — readers can think carefully about the reasons themselves. NVIDIA’s earlier strategic investments were designed primarily to secure the GPU deployment ecosystem. As the DSX system matures, companies like CRWV, NBIS, NSCALE, and LAMBADA may increasingly become deployment and implementation partners. Interestingly, during the earlier NBIS-versus-IREN debates, some NBIS supporters argued that the two companies did not need to be adversaries and might eventually cooperate — for example, IREN leasing power capacity to NBIS. Looking at things now, cooperation indeed seems possible, but perhaps in the opposite direction: IREN may ultimately become the holder of the standard itself, licensing intellectual property outward. Finally, this article is ultimately just speculative corporate-strategy fiction — written mainly for entertainment purposes, not investment advice.

I strongly believe in hybrid models when scaling into the unknowns ... ie this new AI stratosphere ... at times, particularly if/when you need a ton of capital, you may want to take note of at least some investors preferences market is telling, quite loudly, colo is an appreciated model, as it gives (perception of) stability for much longer than csp (10/15 years) that means higher market cap, easier access to capital and - above all - lower dilution for current shareholders going all in GPU as-a-service seems more ideological than first principled (at least for me & several investors I regularly talk to) ... it's a bet without para-shoots ... closer to reasoned gambling than fundamental investing ... it may turn out to be a huge win, as possibly a dramatic defeat ... 90/10 ... 50/50 ... 10/90 ... nobody knows odds now days ... we'll know in two years maybe I understand NBIS & CRWV seemingly winners in the GPU-only model ... sort of supporting the idea that a vertically integrated GPU as-a-service provider will necessarily win even more ... but that's a wrong take imo !! they seem to win as they are 1yr ahead in the cycle (ahead by leasing DCs here & there instead of building them out/owning) ... their anticipated cash flows are winning (as it happened with MARA & RIOT back in the day, in mining) ... but the business model - beside initial anticipation - is structurally weak and their current market cap proves nothing (they better raise at these levels, in fact). All in all ... GPU as-a-service only is an unproven model and colo is a fantastic tool for smoothing/balancing out upside & downside. No need to prefer one over the other ... just do both. Pretty sure IREN would trade at $100/sh if it had signed a colo deal with MSFT. Or if it would sign a similar size colo-only deal with any top HS in coming weeks/months. My take, in a world of unknowns and having secured a ton of GWs, is to present the market - aspirationally - a 50/50 colo/GPU model. Taking the most out of either. Going all in GPU-only is limiting your many options. For no reasons.

Is anyone even looking at $PENG right now? Memory is the variable that determines GPU utilization. When memory is the bottleneck, GPUs sit idle. When memory scales, cost per token drops and inference demand expands. $PENG - Penguin is building exactly that layer. What they launched: In March 2026, Penguin released the MemoryAI CXL-based KV Cache server. The industry's first production-ready server built on CXL memory disaggregation. It gives inference workloads 11 TB of attached memory beyond what the GPU's HBM can hold. This directly solves the Memory Wall. The point where large model inference exceeds onboard GPU memory and performance collapses. The photonic angle: Penguin is also developing a Photonic Memory Appliance (PMA) using optical interconnects to extend memory bandwidth at scale. They were early investors in Celestial AI, acquired by $MRVL Marvell for billions. This is a pure-play AI infrastructure company transitioning to high-margin products. Same space as $SMCI, significantly less covered. The memory wall is real. $PENG is building the solution.

STILL Incredibly excited about the portfolio. Portfolio Update: 1. $VPG ($45) -> NEW ATH and ER 5/12! 2. $FCEL ($9.15) -> On verge of breakout. Any data center order news and this is a $30+ stock 3. $PENG ($28.4) -> Will be the TML 4. $NBIS ($111.5) -> ER next week 5. $HIMX ($15.4) -> Monster! 6. $FLNC ($16.7) -> Monster! 7. $SHMD ($6.6) -> tiny Focus List: $VECO $POWI $KLIC $OUST $CEVA $NVTS $VICR $SYNA $INDI $HLIT