Autonomous vehicle (AV) development often relies on separate models for trajectory generation, high-level intent prediction, scene understanding, and data...

Autonomous vehicle (AV) development often relies on separate models for trajectory generation, high-level intent prediction, scene understanding, and data labeling. This separation makes it hard to compare related outputs, investigate model behavior, and reuse the same representations across the development workflow. NVIDIA Alpamayo 2 Super is an open 34-billion-parameter reasoning vision…

Source

Running a dedicated Kubernetes cluster per team often results in more isolation than an organization requires. While one cluster can be successfully shared...

Running a dedicated Kubernetes cluster per team often results in more isolation than an organization requires. While one cluster can be successfully shared across many teams, the coordination costs increase as the number of teams grows. Challenges include conflicting CRD versions, overlapping RBAC, and no clean way to carve GPU capacity into team-level budgets. At a certain scale…

Source

As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1). Because...

As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1). Because attention now dominates that cost, how it is designed—not just how it is implemented—increasingly determines a model’s inference performance. Shaping model architecture around how GPUs execute it is the premise of AI model co-design.

Source

NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users...

NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X math libraries. It gives Python users access to CUDA-X performance for common math operations without disrupting existing workflows. Depending on the API, operations can run on a CPU, CUDA-enabled GPU, or distributed multi-GPU, multi-node systems.

Source

Two AI computing clusters built from identical NVIDIA H100, GB200 NVL72, or GB300 NVL72 systems can deliver materially different training throughput. We...

Two AI computing clusters built from identical NVIDIA H100, GB200 NVL72, or GB300 NVL72 systems can deliver materially different training throughput. We routinely see 8% to 12% gaps between partner deployments and the corresponding NVIDIA reference architecture (RA) on the same workload, same model, same global batch size. The cause is often a stack of configuration choices in the kernel…

Source

Deploying an AI coding assistant in a regulated, sovereign, or source-sensitive environment, often comes with challenges. Three common issues are: the source...

Deploying an AI coding assistant in a regulated, sovereign, or source-sensitive environment, often comes with challenges. Three common issues are: the source cannot leave the network, the assistant occasionally invents package names that introduce supply-chain risk, and there is no audit trail when a generated change ships a defect. This tutorial walks you through how to self-host a validated…

Source

Modern chip design is increasingly limited by engineering time. Register transfer level (RTL) development and verification require specialized hardware...

Modern chip design is increasingly limited by engineering time. Register transfer level (RTL) development and verification require specialized hardware knowledge, precise reasoning, and repeated interaction with electronic design automation (EDA) tools. LLMs have accelerated code generation, and AI agents extend their impact by using verification feedback to iteratively correct errors.

Source

Every byte moved has a cost. As model checkpoints grow to hundreds of gigabytes or even a terabyte, that cost adds up quickly. To make things even worse, moving...

Every byte moved has a cost. As model checkpoints grow to hundreds of gigabytes or even a terabyte, that cost adds up quickly. To make things even worse, moving these model weights around the cluster is extremely common. For instance, a cold start may pull weights from remote storage into GPU memory; autoscaling and rolling updates must populate each new replica; and RL post-training continuously…

Source

NVIDIA OptiX ray tracing engine is an application framework for achieving optimal ray tracing performance on the GPU. Applications using OptiX can fail in ways...

NVIDIA OptiX ray tracing engine is an application framework for achieving optimal ray tracing performance on the GPU. Applications using OptiX can fail in ways that are difficult to diagnose: an invalid API argument, a black frame, or a GPU-side bug buried under thousands of concurrent threads. Debugging facilities in the NVIDIA OptiX Toolkit (OTK) can help. OTK is a GitHub repository…

Source

Customization is what enables developers to take a general model and tailor it to use cases, domains, languages, and more. However, customization comes with a...

Customization is what enables developers to take a general model and tailor it to use cases, domains, languages, and more. However, customization comes with a few challenges. It requires infrastructure, technical expertise, and software specific to the workflow, as well as resources such as GPUs and the ability to use them effectively. It also depends on specialized domain knowledge: What…

Source

A TensorRT engine build can take seconds to many minutes. Large strongly typed models, deep tactic search, and a cold timing cache on a brand-new GPU SKU can...

A TensorRT engine build can take seconds to many minutes. Large strongly typed models, deep tactic search, and a cold timing cache on a brand-new GPU SKU can leave developers, end users, or AI agents staring at a frozen terminal with no idea whether to wait, retry, or kill the process. Most NVIDIA TensorRT integrations report nothing during a build or provide no way to abort early.

Source

What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale....

What began as discrete AI model training and human-facing chat interfaces has evolved into always-on AI factories dedicated to producing intelligence at scale. These factories are now tasked with powering agentic workflows that reason, plan, use tools, verify intermediate results, and execute complex multistep tasks across vast contexts. Agentic workloads are not defined by a single prompt…

Source

Frontier model pre-training has converged on mixture of experts (MoE), which is fundamentally changing what limits large-scale AI training. As compute per token...

Frontier model pre-training has converged on mixture of experts (MoE), which is fundamentally changing what limits large-scale AI training. As compute per token falls, communication increasingly determines how efficiently models scale across thousands of GPUs. NVIDIA GB300 NVL72 set a world record for pre-training DeepSeek-V3 671B at 1,648 TFLOPs per GPU, showing how advances across the entire AI…

Source

A video analytics AI agent that can perceive, reason, and act based on massive amounts of video footage must be integrated with existing workflows and...

A video analytics AI agent that can perceive, reason, and act based on massive amounts of video footage must be integrated with existing workflows and applications to be useful. These include content management systems, messaging platforms, databases, ticket queue, and escalation paths. This integration is challenging because video systems, enterprise knowledge bases…

Source

OpenUSD is an open, extensible framework that provides a common scene description language for physical AI. It enables teams to bring CAD data, simulation...

OpenUSD is an open, extensible framework that provides a common scene description language for physical AI. It enables teams to bring CAD data, simulation assets, and real-world telemetry into a shared, physically accurate view of the world. Until now, building a USD implementation has typically required adapting a large existing codebase— even for teams that need a specific memory footprint…

Source

For over fifteen years, x86 CPUs have shipped with a dedicated hardware instruction for carryless multiplication. It’s a small but stubborn primitive that...

For over fifteen years, x86 CPUs have shipped with a dedicated hardware instruction for carryless multiplication. It’s a small but stubborn primitive that sits underneath authenticated encryption, error-correcting codes, and modern zero-knowledge proofs. Until now, NVIDIA GPUs lacked native support for this operation. NVIDIA CUDA 13.3 closes that gap with , a new PTX instruction available on…

Source

Coding AI agents are becoming practical operators for long-running machine learning (ML) workflows. They can inspect repositories, set up runtimes, resolve...

Coding AI agents are becoming practical operators for long-running machine learning (ML) workflows. They can inspect repositories, set up runtimes, resolve build issues, launch experiments, monitor execution, analyze metrics, and summarize results. For reinforcement learning (RL) research, this matters because meaningful metrics often appear only after the essential experiment infrastructure…

Source

Useful quantum computers will require fault tolerant logical operations. Researchers are actively exploring many different quantum error correction (QEC) codes...

Useful quantum computers will require fault tolerant logical operations. Researchers are actively exploring many different quantum error correction (QEC) codes to enable this, improving the Logical Error Rates (LER) of Quantum Processing Units (QPUs). While it is well understood how to run logical operations with surface codes (which belong to the topological code family) via lattice surgery…

Source

Large language model (LLM) training workloads increasingly run into GPU memory limits before compute is fully used. Model weights, gradients, optimizer states,...

Large language model (LLM) training workloads increasingly run into GPU memory limits before compute is fully used. Model weights, gradients, optimizer states, communication buffers, and intermediate activations all compete for GPU high-bandwidth memory (HBM). As model size, sequence length, and batch size grow, HBM capacity often becomes the primary scaling bottleneck. This post explains how…

Source

There are many ways to optimize code for GPUs. In this post, you’ll learn how kernel fusion can improve memory bandwidth and reduce kernel launch overhead,...

There are many ways to optimize code for GPUs. In this post, you’ll learn how kernel fusion can improve memory bandwidth and reduce kernel launch overhead, along with multiple ways to apply it in NVIDIA CUDA code. A common bottleneck when writing GPU code is that GPU compute is so fast that even high-bandwidth device memory doesn’t use the GPU kernel fully. Kernel fusion addresses this by…

Source