NVIDIA Launches DSX Platform, Further Expanding into AI Factory Infrastructure
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NVIDIA Launches DSX Platform, Further Expanding into AI Factory Infrastructure
Simulate Your Entire Factory—Free of Charge!
Navigating Web3 tides with focused insightsCompiled by: JINSHI Data
At the NVIDIA GTC Taipei conference held in Taipei, Taiwan, NVIDIA (NVDA.O) unveiled the NVIDIA DSX platform, extending its business further into AI factory infrastructure.
Unlike its past focus on GPU sales, DSX aims to provide enterprises with a complete AI factory solution—from design and simulation to deployment and operations management.
As AI model sizes continue expanding, data centers face challenges that go beyond chip performance—encompassing power supply, thermal management, resource scheduling, and overall operational efficiency. NVIDIA believes that the key competitive metric for the future AI industry will gradually shift from individual chip performance to holistic infrastructure efficiency—that is, how to produce more compute and intelligent services within constrained power, space, and resource budgets.
To this end, the DSX platform integrates NVIDIA’s chips, systems, software, reference architectures, and partner technologies across the entire AI factory lifecycle—from construction to operations. By unifying compute, software, and facility technology stacks, the platform helps customers accelerate deployment, improve reliability and operational efficiency, and reduce the cost of generating tokens during AI inference.
Jensen Huang stated:
“We’re not just delivering chips—we’re providing every infrastructure builder with a complete methodology for building AI factories. With the DSX platform, you can simulate your entire factory at zero cost, validate performance before installing the first rack, and operate with production-grade AI reliability.”
The newly launched software suite includes DSX MaxLPS and DSX OS.
DSX MaxLPS leverages 45°C liquid cooling and rack-level power optimization to increase token output per megawatt of electricity. NVIDIA states that this technology enables up to an additional 40% GPU deployment with minimal impact on performance—further lowering compute costs within fixed power budgets.
DSX OS is an open-source software platform designed for AI factory operations, supporting lifecycle management, intelligent scheduling, health automation, multi-tenant operations, and platform services. NVIDIA will also open-source modular software libraries, APIs, reference designs, and accelerated computing platforms to build a unified software architecture.
Beyond core software, DSX integrates several existing capabilities. The DSX Reference Design provides reference architectures covering compute, networking, storage, power delivery, and cooling systems; DSX Sim supports digital twin simulation and optimization across planning and operations; DSX Flex dynamically adjusts workloads based on grid load and electricity pricing; and DSX Exchange enables data coordination among compute, networking, energy, and cooling systems.
On the commercial front, cloud service providers including CoreWeave, Crusoe, IREN, and Lambda have already deployed DSX core components to boost GPU utilization and shorten time-to-market for AI cloud services.
The hardware ecosystem is also expanding rapidly. Dell Technologies (DELL.N), Hewlett Packard Enterprise (HPE.N), Lenovo Group (0992.HK), Super Micro Computer (SMCI.O), ASUS, Foxconn, Gigabyte, Pegatron, and Quanta Cloud Technology are developing NVIDIA DSX-ready systems to help customers build full-stack AI factories.
Meanwhile, DSX Flex has commenced commercial pilot projects with Emerald AI and Silicon Valley Power to validate AI factories’ ability to dynamically adjust power consumption in response to grid demand.
Strategically, DSX marks NVIDIA’s continued evolution from an AI chip supplier to an AI infrastructure platform provider. By integrating chips, software, data center architecture, operations management, and energy scheduling into a unified framework, NVIDIA aims to establish an industry standard spanning the entire AI factory lifecycle—and further solidify its leadership position in the global AI infrastructure market.
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