GPU applications are processing ever-growing datasets with increasingly powerful accelerators. According to the 2025 Stanford AI Index, compute for notable AI...

GPU applications are processing ever-growing datasets with increasingly powerful accelerators. According to the 2025 Stanford AI Index, compute for notable AI models has been doubling approximately every five months, while the size of datasets used to train LLMs has been doubling approximately every eight months. As computation and data scale together, moving data efficiently from storage becomes…

Source

Protein design is a search problem. Choose amino acids for an eight-residue protein sequence and more than 25 billion combinations are possible. Choose amino...

Protein design is a search problem. Choose amino acids for an eight-residue protein sequence and more than 25 billion combinations are possible. Choose amino acids for a 100-residue protein sequence and the number of possibilities exceeds the number of atoms in the observable universe. Somewhere in that space is a protein that opens a path to a new therapy. There are too many combinations to…

Source

Building an AI factory digital twin requires developers to bring site plans, equipment assets, and engineering requirements into a consistent model that teams...

Building an AI factory digital twin requires developers to bring site plans, equipment assets, and engineering requirements into a consistent model that teams can inspect, revise, and prepare for simulation. Connecting these inputs into an editable workflow helps teams explore design alternatives and incorporate engineering feedback as the design evolves. The NVIDIA Omniverse DSX Blueprint…

Source

AI factories turn energy into intelligence. But before an AI workload can produce its first token, operators must bring an interconnected system of compute,...

AI factories turn energy into intelligence. But before an AI workload can produce its first token, operators must bring an interconnected system of compute, networking, storage, power, cooling, and software online as one productive unit. In a conventional data center build, teams must first select the site, design power and cooling for the deployment, lay out the data center…

Source

After stock crashes and Firmus's scrapped IPO, industry executives expect only seven to ten of the hundreds of AI cloud providers to survive. CoreWeave lost a quarter of its market value, and Microsoft pulled out of a deal with Nscale for 23,000 Nvidia chips. Nvidia has a lot riding on the outcome, since customers outside the hyperscalers make up 45 percent of its data center revenue.

The article The neocloud shakeout is coming, and Nvidia has the most to lose appeared first on The Decoder.

NVIDIA is making the full NVIDIA Run:ai platform available as open source software, giving customers, developers, and ecosystem partners access to...

NVIDIA is making the full NVIDIA Run:ai platform available as open source software, giving customers, developers, and ecosystem partners access to production-grade AI workload and GPU orchestration capabilities. This release expands the NVIDIA DSX AI Factory Platform by establishing NVIDIA Run:ai as an open foundation that enables the broader ecosystem to bring differentiated solutions to market…

Source

Bitwise determinism makes large-scale pretraining easier to debug, validate, and resume reproducibly. These benefits become especially valuable when training...

Bitwise determinism makes large-scale pretraining easier to debug, validate, and resume reproducibly. These benefits become especially valuable when training models with trillions of parameters across thousands of GPUs, where multiple parallelism dimensions, low-precision computation, and distributed checkpointing complicate failure reproduction and fix validation.

Source

AI factories are some of the most complex operations in the world, combining GPUs, CPUs, switches, DPUs, and SuperNICs alongside schedulers, orchestration...

AI factories are some of the most complex operations in the world, combining GPUs, CPUs, switches, DPUs, and SuperNICs alongside schedulers, orchestration services, security controls, and a rapidly changing software stack. Deploying this infrastructure effectively is a challenge. But so is being able to validate that the infrastructure, software, and policies work together for the workloads the…

Source

A robot arm doesn’t care where the server lives There is no single safest option. Cloud, on-premises and hybrid setups can all protect robotics systems well. They just split responsibility differently, and most factories end up blending them. Sounds neat on paper. In practice, it’s messy. A welding cell, a fleet of warehouse robots and […]

GPU applications increasingly need networking and data movement to behave like first-class GPU-controlled operations rather than host-driven services. When the...

GPU applications increasingly need networking and data movement to behave like first-class GPU-controlled operations rather than host-driven services. When the CPU sits in the middle of every network transaction, it becomes a bottleneck on the critical path, adding latency and limiting how efficiently distributed applications can respond in real time. NVIDIA DOCA GPUNetIO…

Source

GPU-accelerated Kubernetes clusters depend on compatible versions across dozens of components, each on its own release cycle: host kernels, GPU drivers,...

GPU-accelerated Kubernetes clusters depend on compatible versions across dozens of components, each on its own release cycle: host kernels, GPU drivers, container runtimes, networking, storage, operators, and workload frameworks. A configuration that works for one service, GPU generation, and Kubernetes release may silently fail for another, and tracing version conflicts after deployment is…

Source

GPU applications increasingly consist of multiple independent components running at the same time within a single process: a latency-sensitive operator...

GPU applications increasingly consist of multiple independent components running at the same time within a single process: a latency-sensitive operator alongside a throughput-oriented background kernel; a data preprocessing stage alongside model inference; or multiple stages of a processing workflow sharing a single GPU. Controlling how GPU resources are shared between them remains difficult.

Source

AI agents are becoming a standard part of development workflows, but general-purpose agents weren't built with specialized infrastructure software such as...

AI agents are becoming a standard part of development workflows, but general-purpose agents weren’t built with specialized infrastructure software such as NVIDIA DOCA in mind. Without domain-specific knowledge, agents may fall back on guesswork. This is an issue in infrastructure development because every correction cycle takes time away from deployment. DOCA is the unified software platform…

Source

AI infrastructure engineers, storage developers, and cloud service providers need fast and secure access to high-capacity file and object storage to support AI...

AI infrastructure engineers, storage developers, and cloud service providers need fast and secure access to high-capacity file and object storage to support AI workloads. AI workloads increasingly require high-speed data access for training, fine-tuning, inference context, tool calls, searches, and database lookups. Much of this data lies in files and objects stored both on-premises and in…

Source

Parallel work, model-family isolation, reversible changes, and GPU-backed validation shaped an open source project designed around coding agents NVIDIA TensorRT...

Parallel work, model-family isolation, reversible changes, and GPU-backed validation shaped an open source project designed around coding agents NVIDIA TensorRT Model Connect is an open source collection of AI model reference implementations in C++, built on top of NVIDIA TensorRT. It began with a practical question: could the performance of the NVIDIA inference stack be made accessible to…

Source

Every unused watt is capacity left on the table. AI factories are typically provisioned for the unlikely moment when every GPU reaches peak power, creating a...

Every unused watt is capacity left on the table. AI factories are typically provisioned for the unlikely moment when every GPU reaches peak power, creating a protective buffer that can leave valuable infrastructure underused during normal operation. NVIDIA DSX MaxLPS uses policy-governed power sharing to allocate power across participating resources dynamically, enabling customers to deploy up to…

Source

As language models grow, scaling dense architectures becomes increasingly expensive. In a dense transformer, every token passes through every layer, so adding...

As language models grow, scaling dense architectures becomes increasingly expensive. In a dense transformer, every token passes through every layer, so adding capabilities increases computation for both training and inference. Mixture-of-experts (MoE) architectures take a different approach to scaling by using many subnetworks, or experts, while activating only a small subset for each token.

Source

A GPU cluster can pass every health check and still fail to run an AI workload. Even when every GPU, network link, and pod reports healthy, a 512-GPU training...

A GPU cluster can pass every health check and still fail to run an AI workload. Even when every GPU, network link, and pod reports healthy, a 512-GPU training job can underperform or fail. The cause may be one slow GPU, a link that degrades under load, or a configuration that quietly routes traffic over a slower path. Operators may not discover the problem until hours into the run or until a…

Source

Kubernetes manages what runs on your nodes. Managing the nodes themselves is the challenge: kernel settings, system packages, storage layouts, security agents,...

Kubernetes manages what runs on your nodes. Managing the nodes themselves is the challenge: kernel settings, system packages, storage layouts, security agents, and the host-level tuning that GPU workloads depend on. Many teams manage this with Ansible playbooks, custom scripts, and manual runbooks. That works until a new cluster comes up in a different region, a kernel upgrade breaks RDMA…

Source

As large language model (LLM) inference increasingly processes sensitive information and proprietary model context across personal, enterprise, and regulated...

As large language model (LLM) inference increasingly processes sensitive information and proprietary model context across personal, enterprise, and regulated settings, data must be processed inside a trusted environment. NVIDIA Confidential Computing (CC) provides a pathway for running these workloads securely using memory-encrypted confidential virtual machines (CVMs), confidential GPUs…

Source