Summary: Jinshi Data
At the NVIDIA GTC Taipei conference held in Taipei, Taiwan, NVIDIA (NVDA.O) unveiled the NVIDIA DSX platform, further extending its business reach into the field of AI factory infrastructure.
Unlike its past focus on GPU sales, DSX aims to provide enterprises with a complete AI factory solution encompassing design, simulation, deployment, and operational management.
As AI models continue to scale, the challenges faced by data centers are no longer just about chip performance, but also involve power supply, cooling capacity, resource scheduling, and overall operational efficiency. NVIDIA believes that future competition in the AI industry will increasingly shift key metrics from single-chip performance to overall infrastructure efficiency—that is, how to produce more computing power and intelligent services under limited power, space, and resource constraints.
To this end, the DSX platform integrates NVIDIA's chips, systems, software, reference architectures, and partner technologies, covering the entire lifecycle of AI factory construction and operation. By unifying technology stacks across computing, software, and facilities, the platform helps customers improve deployment speed, reliability, and operational efficiency while reducing 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 the entire factory without spending a dime, validate performance before installing the first rack, and operate with the reliability required for production-grade AI."
The software suite announced this time primarily includes DSX MaxLPS and DSX OS.
Among them, DSX MaxLPS leverages 45-degree liquid cooling and rack-level power optimization technologies to increase token output per megawatt of power. NVIDIA stated that this technology allows for the deployment of up to 40% more GPUs with minimal impact on performance, thereby further reducing computing costs within a fixed power budget.
DSX OS is an open-source software platform for AI factory operations, supporting functions such as 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.
In addition to the core software, DSX integrates several existing capabilities. DSX Reference Design provides reference architectures covering computing, networking, storage, power, and cooling systems. DSX Sim supports digital twin simulation and optimization from planning to operation. DSX Flex dynamically adjusts workloads based on grid load and electricity price fluctuations. DSX Exchange facilitates data collaboration between computing, networking, energy, and cooling systems.
Regarding commercial deployment, cloud service providers such as CoreWeave, Crusoe, IREN, and Lambda have already deployed core DSX components to improve GPU utilization and shorten the time to market for AI cloud services.
The hardware ecosystem is also expanding simultaneously. Manufacturers including Dell Technologies (DELL.N), Hewlett Packard Enterprise (HPE.N), Lenovo Group (0992.HK), Supermicro (SMCI.O), ASUS, Foxconn, GIGABYTE, Pegatron, and QCT are developing NVIDIA DSX Ready systems to help customers build full-stack AI factories.
Meanwhile, DSX Flex has initiated commercial pilot projects with Emerald AI and Silicon Valley Power to validate the capability of AI factories to dynamically adjust power consumption based on grid demand.
From a strategic perspective, DSX marks NVIDIA's continued transition from an AI chip supplier to an AI infrastructure platform provider. By incorporating chips, software, data center architecture, operational management, and energy scheduling into a unified system, NVIDIA aims to establish industry standards covering the entire lifecycle of AI factories and further solidify its leading position in the global AI infrastructure market.
