# NVIDIA DSX

> Source: https://aiwiki.ai/wiki/nvidia_dsx
> Updated: 2026-10-25
> Fact-checked: 2026-10-18
> Categories: AI Compute, AI Hardware, Data Centers, NVIDIA
> License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) - attribute to "AI Wiki (aiwiki.ai)"
> Cite as: AI Wiki. "NVIDIA DSX." aiwiki.ai, 25 Oct 2026. https://aiwiki.ai/wiki/nvidia_dsx
> From AI Wiki (https://aiwiki.ai), the free encyclopedia of artificial intelligence. Reuse freely with attribution.

**NVIDIA DSX** is a platform from [NVIDIA](https://aiwiki.ai/wiki/nvidia) for designing, simulating, building and operating large-scale data centers that the company calls [AI factories](https://aiwiki.ai/wiki/ai_factory). NVIDIA describes DSX as a "complete playbook" that pulls together reference designs, open-source software libraries, application programming interfaces, NVIDIA's accelerated computing platforms and a broad partner ecosystem into a single codesigned framework, so that operators can model an entire facility before construction and then run it with the kind of reliability that production AI workloads demand.[1][2]

NVIDIA formally introduced the DSX platform as a unified offering at GTC Taipei, held alongside COMPUTEX 2026 in Taiwan, where company founder and chief executive Jensen Huang delivered the keynote on June 1, 2026. The associated press release, titled "NVIDIA DSX Gives Infrastructure Builders the Playbook for AI Factories," carried a Taipei dateline of May 31, 2026.[1][3] DSX rounds out NVIDIA's three-letter naming scheme for its product tiers, sitting alongside RTX for consumer and workstation graphics and DGX for AI systems and supercomputers, with DSX positioned at the level of the [data center](https://aiwiki.ai/wiki/data_center) itself.[4]

## Key facts

| Attribute | Detail |
| --- | --- |
| Developer | [NVIDIA](https://aiwiki.ai/wiki/nvidia) |
| Type | AI factory reference design and infrastructure software platform |
| Announced | GTC Taipei at COMPUTEX 2026; keynote June 1, 2026 (press release dated May 31, 2026)[1] |
| Purpose | Design, simulation, build-out and operation of gigawatt-scale AI [data center](https://aiwiki.ai/wiki/data_center)s ("AI factories")[2] |
| Core software components | DSX OS, DSX MaxLPS, DSX Sim, DSX Flex, DSX Exchange[1][2] |
| Reference design | Vera Rubin DSX AI Factory reference design (built on the [NVIDIA Vera Rubin](https://aiwiki.ai/wiki/nvidia_vera_rubin) platform)[5] |
| [Digital twin](https://aiwiki.ai/wiki/digital_twin) | Omniverse DSX Blueprint, built on [NVIDIA Omniverse](https://aiwiki.ai/wiki/nvidia_omniverse)[6] |
| Scale targeted | 100 megawatts to multiple gigawatts per facility[6] |
| Key efficiency claim | Up to 40% more GPUs within the same megawatt budget via DSX MaxLPS[1] |
| Stated availability | Many components offered on a "when-and-if-available" basis[1] |

## The AI factory concept

NVIDIA frames DSX around its broader idea of the "AI factory," a term it uses to recast a data center as an industrial plant whose product is intelligence measured in tokens rather than a general-purpose computing facility. In NVIDIA's framing, a token is both the unit of AI output and the unit of revenue, so the central design goal of an AI factory becomes maximizing useful [token](https://aiwiki.ai/wiki/token) [inference](https://aiwiki.ai/wiki/inference) throughput for every watt of power the site can draw.[1][7] Huang has summarized the economics bluntly, saying that with a gigawatt of power, "throughput per watt is revenues, because every token is profitable, every token is revenues."[7]

The scale NVIDIA is targeting is large. In his GTC Taipei keynote, Huang described single AI factory sites heading toward 1 gigawatt, with capital costs "at $50 billion to $60 billion, and soon it will be $80 billion to $100 billion per gigawatt."[7] DSX is NVIDIA's attempt to give the companies building that capacity, including cloud providers, [neocloud](https://aiwiki.ai/wiki/neocloud)s, enterprises and governments, a repeatable method rather than a one-off engineering project for each site. The company has gone so far as to claim it "is the only company that builds the full AI factory," spanning silicon, systems, networking, software and the facility design itself.[1]

## Platform components

NVIDIA structures DSX as a set of modular software libraries and reference materials that map onto three phases of an AI factory's life: design, deployment and operations.[2] Several pieces were introduced over the preceding GTC events, and the GTC Taipei announcement added DSX MaxLPS and DSX OS, which NVIDIA described as "new open source software" (the Dynamic Power Software behind MaxLPS was later offered as a Developer Preview under an NVIDIA Software License Agreement).[3][13] The table below lists the named components NVIDIA has described.

| Component | What NVIDIA says it does |
| --- | --- |
| DSX Reference Design | Generation-specific, validated AI factory architectures covering compute, networking, storage, hardware cluster design and facilities infrastructure, including power, cooling and controls as well as civil, structural and architectural design.[1] |
| DSX MaxLPS | A suite of technologies to maximize token performance per megawatt within a fixed power budget. It combines 45 degrees Celsius liquid cooling with in-rack techniques so operators can run up to 40% more GPUs at their most energy-efficient operating point with minimal impact on workload performance.[1] |
| DSX OS | Open-source, modular software purpose-built for AI factory operations, providing lifecycle management, scheduling, runtime consistency, health automation, resiliency, multi-tenant operations and platform services.[1][8] |
| DSX Sim | A high-fidelity simulation layer for the AI factory lifecycle, used by NVIDIA, partners and customers to model, validate and optimize infrastructure decisions from planning and design through deployment and operations.[1] |
| DSX Flex | Connects AI factories to power-grid services, adapting workloads to grid signals such as load shedding, demand response and pricing events, and orchestrating renewable and hybrid power across utility, on-site renewables and storage.[1] |
| DSX Exchange | Enables scalable, secure integration of compute, network, energy, power and cooling plant signals between IT, operational technology and operations agents.[1] |

### DSX MaxLPS and power efficiency

DSX MaxLPS is the component NVIDIA positions as the one that lowers the cost of each token. According to NVIDIA, the name stands for "Maximum Land Power Shell," the site-level limits of land, utility power and the physical shell that bound an AI factory; the stated goal is to maximize token performance per megawatt.[13] For details, see [NVIDIA DSX MaxLPS](https://aiwiki.ai/wiki/nvidia_dsx_maxlps). Because an AI factory is constrained by the power it can secure rather than by floor space, NVIDIA's argument is that the operator who extracts the most performance from a fixed megawatt allocation wins on cost. According to NVIDIA, pairing 45 degrees Celsius liquid cooling with in-rack power management lets a site run as much as 40% more GPUs at their most energy-efficient operating point, with what NVIDIA calls "minimal impact on workload performance".[1]

DSX MaxLPS covers ground that NVIDIA earlier described under other library names. At GTC Washington, D.C. in October 2025 the Omniverse DSX materials referenced DSX Boost, and at GTC in March 2026 the company described DSX Max-Q as a library that maximizes computing output and token performance per watt, and in the Omniverse DSX materials it referenced DSX Boost as delivering up to 30% higher GPU throughput within the same power envelope.[5][6] 

### DSX OS

DSX OS is the operations layer, announced as open-source and modular software for running multi-tenant AI factories at scale.[8] NVIDIA's technical description lists a set of named subsystems rather than a single monolithic product. These include the NVIDIA Infra Controller (NICo) for API-driven bare-metal lifecycle management and tenant isolation through [NVIDIA BlueField](https://aiwiki.ai/wiki/bluefield) DPUs; the NVIDIA AI Cluster Runtime (AICR) for version-locked software recipes that prevent configuration drift; NVSentinel for Kubernetes-native GPU fault detection and automated remediation; NVIDIA Fleet Intelligence for fleet-wide visibility and integrity verification; the [KAI Scheduler](https://aiwiki.ai/wiki/kai_scheduler) together with NVIDIA [Run:ai](https://aiwiki.ai/wiki/run_ai) for GPU-aware workload placement and fractional allocation; and inference services such as [NVIDIA Dynamo](https://aiwiki.ai/wiki/nvidia_dynamo), Grove and Cloud Functions. DSX Exchange in this stack is described as an MQTT-based hub connecting IT and operational-technology systems.[8] NVIDIA's stated intent is that ecosystem partners build on these components to deliver AI services rather than rebuild operational tooling from scratch. NVIDIA says the collector component of [NVIDIA Minerva](https://aiwiki.ai/wiki/nvidia_minerva), its AIOps platform announced in October 2026, is part of DSX OS, and that customers can add Minerva's AI and machine-learning insights within DSX OS or run Minerva standalone.[20]

### DSX Sim and DSX Air

DSX Sim is the simulation layer, and a key part of it is DSX Air, a software-as-a-service platform NVIDIA introduced at GTC in March 2026 for logically simulating AI factories. NVIDIA says DSX Air produces high-fidelity digital simulations of its hardware, including GPUs, SuperNICs, DPUs and switches, and connects to partner tools for storage, routing, security and orchestration through open APIs. The company's pitch is that this kind of pre-build modeling can cut the time to a customer's first token from weeks or months down to days or hours.[9]

## Vera Rubin DSX reference design

The first generation-specific blueprint under the platform is the Vera Rubin DSX AI Factory reference design, built around the [NVIDIA Vera Rubin](https://aiwiki.ai/wiki/nvidia_vera_rubin) computing platform. NVIDIA describes it as a unified architecture that integrates compute, [NVIDIA Spectrum-X](https://aiwiki.ai/wiki/nvidia_spectrum_x) [Ethernet](https://aiwiki.ai/wiki/ethernet) networking, storage, power, cooling and facility controls so partners can design, deploy and scale gigawatt AI factories with maximum token throughput per watt and improved uptime.[5] It is intended to give operators a tested starting point covering everything from the GPU racks to the building's electrical and mechanical systems.

A defining feature of the Vera Rubin generation, and therefore of this reference design, is a shift in power architecture. NVIDIA and more than twenty partners are moving toward 800-volt direct current (VDC) distribution for the gigawatt era, replacing the traditional 415-volt or 480-volt alternating-current three-phase systems used in most data centers. NVIDIA says 800 VDC improves scalability and energy efficiency, reduces copper and other materials, and supports the high power density of its Kyber rack architecture for Rubin-class GPUs.[10] This power work connects DSX to the broader Vera Rubin rack and system roadmap, including the NVL72 rack-scale designs in NVIDIA's [MGX](https://aiwiki.ai/wiki/nvidia_mgx) open architecture.

## DSX POD modular deployment

NVIDIA introduced [DSX POD](https://aiwiki.ai/wiki/nvidia_dsx_pod) at GTC Berlin on October 21, 2026, as a partner-delivered reference design for factory-integrated, containerized Vera Rubin AI factories.[14] It specifies a physical implementation of the broader DSX framework, alongside conventional construction and other deployment approaches.[15]

| Name | Scope |
| --- | --- |
| DSX platform | Facility design framework and software for AI factory planning, deployment and operation.[2][15] |
| DSX POD | Container types, interfaces and validation requirements for factory-integrated AI infrastructure.[15][16] |
| Vera Rubin POD | The underlying computing platform, which combines five types of rack-scale systems into a multi-rack AI supercomputer.[17] |

The DSX POD design coordinates compute, networking, storage, power distribution, liquid cooling, cabling, controls and mechanical requirements. Partners install and test racks, internal network connections, wiring and plumbing at the factory while site teams prepare foundations and utilities. Its standard electrical input is 800 VDC; partners engineer the upstream power conversion and regional electrical infrastructure. After shipment, crews place the units, connect containers and site services, check transport integrity and commission the system.[15]

NVIDIA estimated that deployment at a prepared site could fall from six months to as little as 14 days.[14] This estimate concerns the installation and commissioning stage after site preparation, rather than the full schedule for acquiring land, obtaining permits or securing power. The product FAQ specifies that customers still need prepared power, facility water, cooling and network connections.[16] The announcement presents the timing as a company claim, rather than evidence of an independently measured completed deployment.[14] Jensen Huang gave a different figure in his GTC Berlin keynote: according to The Next Platform, he said the time from installation and arrival of the computing to production of tokens would go from "something along the lines of four to five months down to about a month."[19]

NVIDIA said it was working with computing and infrastructure partners to deliver DSX POD AI factories worldwide in early 2027.[14] Dell separately described its planned Modular Data Center with NVIDIA DSX POD as including compute, storage, networking, rack-level power and liquid cooling.[18] These were announced delivery plans as of October 2026, rather than a declaration that DSX POD systems were already shipping.[14][18]

## Omniverse DSX digital twin

The simulation and design half of DSX is anchored by the Omniverse DSX Blueprint, built on [NVIDIA Omniverse](https://aiwiki.ai/wiki/nvidia_omniverse). NVIDIA first introduced this blueprint at GTC Washington in October 2025 and describes it as an open, comprehensive framework for designing and operating gigawatt-scale AI factories.[6] Using OpenUSD assets and Omniverse libraries, the blueprint lets engineering teams build a physically accurate [digital twin](https://aiwiki.ai/wiki/digital_twin) of a planned facility, simulate its operation in real time and tune power, cooling, networking and operations before any concrete is poured. NVIDIA has said the blueprint is generally available through build.nvidia.com and is compatible with the Vera Rubin DSX reference design.[5][6] The blueprint targets facilities ranging from about 100 megawatts up to multiple gigawatts and supports both [NVIDIA Grace Blackwell](https://aiwiki.ai/wiki/nvidia_blackwell) and Vera Rubin platforms.[6]

## Ecosystem and partners

DSX is explicitly an ecosystem play, and NVIDIA's announcements name a long roster of partners across the hardware, cloud, software, facilities and power layers. The table below groups the partners NVIDIA cited as adopting or contributing to the platform.

| Category | Partners named by NVIDIA |
| --- | --- |
| Cloud and [neocloud](https://aiwiki.ai/wiki/neocloud) providers deploying DSX components | [CoreWeave](https://aiwiki.ai/wiki/coreweave), Crusoe, Firmus, IREN, Lambda, Nscale, [Nebius](https://aiwiki.ai/wiki/nebius), Yotta Data Services[1] |
| System manufacturers building DSX-ready systems | Dell Technologies, HPE, [Lenovo](https://aiwiki.ai/wiki/lenovo), Supermicro, ASUS, [Foxconn](https://aiwiki.ai/wiki/foxconn), GIGABYTE, Pegatron, Quanta Cloud Technology (QCT), Wistron, Wiwynn[1] |
| DSX OS ecosystem adopters | Aible, BeyondAI, Bhashini, DCAI, Mirantis, OpenNebula Systems, Rafay, [Red Hat](https://aiwiki.ai/wiki/red_hat), Sarvam, Simplismart, Spectro Cloud, vCluster, Vultr[1] |
| Omniverse DSX / [digital twin](https://aiwiki.ai/wiki/digital_twin) software | Cadence, Dassault Systèmes, PTC, [Siemens](https://aiwiki.ai/wiki/siemens)[1][6] |
| Power, grid and cooling | Emerald AI, Silicon Valley Power, Schneider Electric, Vertiv, Eaton, Trane Technologies, [GE Vernova](https://aiwiki.ai/wiki/ge_vernova), Hitachi, Siemens Energy[1][6] |
| Engineering and AI operations | Jacobs, Bechtel, Phaidra[6] |

NVIDIA has highlighted a few partner outcomes to make the case concrete. It says Phaidra's [AI agents](https://aiwiki.ai/wiki/ai_agents), integrated through the platform, can recover roughly 10% more compute by smoothing cooling spikes, and it has pointed to a project by Nscale and Caterpillar that is bringing the Vera Rubin DSX reference design to life on a multi-gigawatt site in West Virginia, which NVIDIA calls one of the largest AI factories in the world.[5][6]

Among the system builders, ASUS used COMPUTEX 2026 to announce that it had adopted the DSX platform for its AI factory offerings, pairing it with rack-scale systems such as its AI POD built on the Vera Rubin NVL72 design. ASUS framed the value as the ability to evaluate power delivery, cooling, networking topology, storage and facility readiness in simulation before physical build-out begins, which it tied to faster time to first token and revenue.[11]

## Reception and context

Trade and industry press generally read the DSX announcement as a strategic shift rather than a single new product. Several outlets characterized it as NVIDIA moving up the value chain from selling chips, and then racks, to selling a methodology and software stack for the entire facility, with DSX serving as the data center counterpart to its DGX systems and RTX graphics lines.[4][12] Coverage also placed DSX within NVIDIA's wider gigawatt-scale narrative at GTC Taipei, where the company put the Vera Rubin platform into full production and leaned heavily on the AI factory framing.[7][12]

It is worth noting what DSX is not, as of its announcement. NVIDIA cautioned that many of the products and features it described "remain in various stages and will be offered on a when-and-if-available basis," so component availability and final specifications may change.[1] The platform is also a moving target in naming and scope, having evolved across the October 2025, March 2026 and June 2026 GTC events, with several component names (DSX Boost in October 2025 and DSX Max-Q in March 2026, followed by DSX MaxLPS in May 2026) consolidating along the way.[5][6]

## See also

- [NVIDIA Vera Rubin](https://aiwiki.ai/wiki/nvidia_vera_rubin)
- [NVIDIA Omniverse](https://aiwiki.ai/wiki/nvidia_omniverse)
- [NVIDIA Blackwell](https://aiwiki.ai/wiki/nvidia_blackwell)
- [NVIDIA GB300 NVL72](https://aiwiki.ai/wiki/nvidia_gb300_nvl72)
- [NVIDIA Spectrum-X](https://aiwiki.ai/wiki/nvidia_spectrum_x)
- [NVIDIA Dynamo](https://aiwiki.ai/wiki/nvidia_dynamo)


## References

1. NVIDIA Newsroom. "NVIDIA DSX Gives Infrastructure Builders the Playbook for AI Factories." May 31, 2026. https://nvidianews.nvidia.com/news/dsx-infrastructure-ai-factory
2. NVIDIA. "Build and operate efficient AI factories with the NVIDIA DSX platform." https://www.nvidia.com/en-us/data-center/products/dsx/
3. GlobeNewswire (NVIDIA press release). "NVIDIA DSX Gives Infrastructure Builders the Playbook for AI Factories." June 1, 2026. https://www.globenewswire.com/news-release/2026/06/01/3303978/0/en/NVIDIA-DSX-Gives-Infrastructure-Builders-the-Playbook-for-AI-Factories.html
4. VideoCardz. "NVIDIA confirms Jensen Huang GTC Taipei keynote on June 1st, day before Computex." https://videocardz.com/newz/nvidia-confirms-jensen-huang-gtc-taipei-keynote-on-june-1st-day-before-computex
5. NVIDIA Newsroom. "NVIDIA Releases Vera Rubin DSX AI Factory Reference Design and Omniverse DSX Digital Twin Blueprint With Broad Industry Support." March 2026. https://nvidianews.nvidia.com/news/nvidia-releases-vera-rubin-dsx-ai-factory-reference-design-and-omniverse-dsx-digital-twin-blueprint-with-broad-industry-support
6. NVIDIA Blog. "NVIDIA Launches Omniverse DSX Blueprint, Enabling Global AI Infrastructure Ecosystem to Build Gigawatt-Scale AI Factories." October 28, 2025. https://blogs.nvidia.com/blog/omniverse-dsx-blueprint/
7. SiliconANGLE. "Five thoughts from Nvidia CEO Jensen Huang's GTC Taipei 2026 keynote." June 1, 2026. https://siliconangle.com/2026/06/01/five-thoughts-nvidia-ceo-jensen-huangs-gtc-taipei-2026-keynote/
8. NVIDIA Technical Blog. "NVIDIA DSX OS Delivers Open, Modular Software for Operating AI Factories at Scale." May 31, 2026. https://developer.nvidia.com/blog/nvidia-dsx-os-delivers-open-modular-software-for-operating-ai-factories-at-scale/
9. NVIDIA Blog. "NVIDIA DSX Air Boosts Time to Token With Accelerated Simulation for AI Factories." March 16, 2026. https://blogs.nvidia.com/blog/dsx-air-simulation-ai-factories/
10. NVIDIA Blog. "NVIDIA, Partners Drive Next-Gen Efficient Gigawatt AI Factories in Buildup for Vera Rubin." https://blogs.nvidia.com/blog/gigawatt-ai-factories-ocp-vera-rubin/
11. ASUS. "ASUS Adopts NVIDIA DSX AI Factory Platform to Accelerate Time to First Revenue." Computex 2026. https://press.asus.com/news/press-releases/asus-nvidia-dsx-ai-factory-platform-computex-2026
12. ServeTheHome. "NVIDIA Computex 2026 Keynote Live Coverage." https://www.servethehome.com/nvidia-computex-2026-keynote-live-coverage/
13. Sarah McKenney and Harry Petty. "Maximizing AI Factory Performance per Watt with NVIDIA DSX MaxLPS." NVIDIA Technical Blog, August 24, 2026. https://developer.nvidia.com/blog/maximizing-ai-factory-performance-per-watt-with-nvidia-dsx-maxlps/
14. NVIDIA Newsroom. "NVIDIA Introduces DSX POD AI Factories to Accelerate Europe's Infrastructure Deployment." October 21, 2026. https://nvidianews.nvidia.com/news/nvidia-introduces-dsx-pod-ai-factories-to-accelerate-europes-infrastructure-deployment
15. Premal Savla. "Achieve Productive AI Capacity Faster and More Predictably with NVIDIA DSX POD." NVIDIA Technical Blog, October 21, 2026. https://developer.nvidia.com/blog/achieve-productive-ai-capacity-faster-and-more-predictably-with-nvidia-dsx-pod/
16. NVIDIA. "DSX POD Reference Design for AI Factory Infrastructure." Accessed October 23, 2026. https://www.nvidia.com/en-us/data-center/products/dsx/pod/
17. NVIDIA. "NVIDIA Vera Rubin Platform." Accessed October 23, 2026. https://www.nvidia.com/en-us/data-center/technologies/rubin/
18. Dell Technologies. "Dell Technologies Advances Enterprise Agentic AI from the Deskside to Data Center with NVIDIA." October 21, 2026. https://investors.delltechnologies.com/news-events/press-releases/detail/1659/dell-technologies-advances-enterprise-agentic-ai-from-the-deskside-to-data-center-with-nvidia
19. Joe Fay. "Huang Shrugs Off Protestors To Kick Off GTC Berlin With DSX Pods, Robots." The Next Platform, October 21, 2026. https://www.nextplatform.com/control/2026/10/21/huang-shrugs-off-protestors-to-kick-off-gtc-berlin-with-dsx-pods-robots/5304447
20. NVIDIA. "NVIDIA Minerva: AI-Native AIOps for AI Factories." Accessed October 25, 2026. https://www.nvidia.com/en-us/networking/products/software/minerva/