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Received β€” 18 August 2026 ⏭ NVIDIA Technical Blog

Run Massive-Scale UMAP in Minutes Using Multiple GPUsβ€”Without Losing Accuracy

18 August 2026 at 16:48
Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications...

Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications range across exploratory data analysis, topic modeling, and single-cell analysis. Many of these workflows are iterative and exploratory, requiring UMAP to be run repeatedly as users analyze their data or tune parameters. As datasets grow…

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Received β€” 30 July 2026 ⏭ NVIDIA Technical Blog

Run High-Performance Core Math at Scale with NVIDIA nvmath-python

30 July 2026 at 22:43
Decorative math image.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...Decorative math image.

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.

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Received β€” 27 July 2026 ⏭ NVIDIA Technical Blog

Advancing Semiconductor Innovation Across Materials Engineering and Manufacturing

27 July 2026 at 00:45
As AI workloads increase, explosive compute demand is pushing the semiconductor industry to meet unprecedented performance targets. Even small delays can have...

As AI workloads increase, explosive compute demand is pushing the semiconductor industry to meet unprecedented performance targets. Even small delays can have outsized financial impact in fast-moving AI hardware cycles. Simultaneously, the shift from chip-level optimization to system-level engineering is compounding thermal and power challenges. Meeting these demands requires breakthroughs…

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Received β€” 21 July 2026 ⏭ NVIDIA Technical Blog

Inside NVIDIA Rubin GPU Architecture: Powering the Era of Agentic AI

23 July 2026 at 18:15
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…

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Received β€” 20 July 2026 ⏭ NVIDIA Technical Blog

NVIDIA NVLink: The Scale-Up Network for AI Factories

20 July 2026 at 15:46
The demand for AI continues to accelerate. Workloads are getting larger, models are becoming more complex, and there is mounting pressure to deploy AI compute...

The demand for AI continues to accelerate. Workloads are getting larger, models are becoming more complex, and there is mounting pressure to deploy AI compute infrastructure faster than ever. AI factoriesβ€”data center-scale systems that continuously convert data and energy into intelligenceβ€”are being deployed to meet this insatiable demand. This AI factory approach to the data center has…

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Received β€” 16 July 2026 ⏭ NVIDIA Technical Blog

Scaling Agentic AI Factories Through Extreme Co-Design with NVIDIA BlueField

16 July 2026 at 16:00
Decorative image.Agentic AI changes the infrastructure pattern for AI factories. One request can trigger many model calls, tool calls, memory lookups, policy checks, storage...Decorative image.

Agentic AI changes the infrastructure pattern for AI factories. One request can trigger many model calls, tool calls, memory lookups, policy checks, storage accesses, and network transfers before a final answer is produced. As more agents run at once and carry context across steps, users, tools, services, and sessions, infrastructure must move, protect, retrieve, and reuse data fast enough to keep…

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Received β€” 30 June 2026 ⏭ NVIDIA Technical Blog

Designing GPU-Accelerated Query Engines with NVIDIA GQE

30 June 2026 at 17:36
Decorative image.GPU-accelerated query engines are often constrained by memory and I/O bandwidth. NVIDIA hardware advancesβ€”including high bandwidth memory (HBM), NVIDIA...Decorative image.

GPU-accelerated query engines are often constrained by memory and I/O bandwidth. NVIDIA hardware advancesβ€”including high bandwidth memory (HBM), NVIDIA NVLink-C2C, and dedicated decompression engines featured in NVIDIA GB200 NVL4β€”help remove these bottlenecks by increasing effective storage capacity, accelerating data movement between CPUs and GPUs, and speeding data access without consuming…

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Received β€” 25 June 2026 ⏭ NVIDIA Technical Blog

Q&A: How KRAFTON Built PUBG Ally, a Co-Playable Character Powered by NVIDIA ACE

25 June 2026 at 16:38
AI companions in games have long been constrained by fixed dialogue. PUBG Ally is a different kind of system. Built by KRAFTON for PUBG: BATTLEGROUNDS, this AI...

AI companions in games have long been constrained by fixed dialogue. PUBG Ally is a different kind of system. Built by KRAFTON for PUBG: BATTLEGROUNDS, this AI teammate is powered by NVIDIA ACE and its suite of efficient models and tooling. PUBG Ally uses automatic speech recognition, a 2B-parameter small language model, and text-to-speech to understand player voice…

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Received β€” 23 June 2026 ⏭ NVIDIA Technical Blog

Maximize AI Factory Energy Efficiency Through Full-Stack Inference and Training Optimizations

23 June 2026 at 16:30
Power can account for 40% of the operating expenses (OpEx) to run an AI factory. Each watt can be spent on overhead, data ingestion, training, or generating...

Power can account for 40% of the operating expenses (OpEx) to run an AI factory. Each watt can be spent on overhead, data ingestion, training, or generating tokens for customers. And most sites are capped at a fixed power level provided by a regional provider. Under these conditions, performance per watt becomes a key efficiency metric that directly translates to token costs.

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Received β€” 16 June 2026 ⏭ NVIDIA Technical Blog

Build On-Device AI Companions with the NVIDIA ACE Game Agent SDK and Unreal Engine 5 Plugins

16 June 2026 at 17:00
NVIDIA RTX technologies are deeply integrated into Unreal Engine 5 through the NVIDIA RTX Branch of Unreal Engine and the NVIDIA DLSS Unreal Engine plugin. This...

NVIDIA RTX technologies are deeply integrated into Unreal Engine 5 through the NVIDIA RTX Branch of Unreal Engine and the NVIDIA DLSS Unreal Engine plugin. This provides developers with direct access to advanced rendering, frame generation, and ray-traced lighting. NVIDIA is expanding this integration with new tools for building on-device AI characters and gameplay, as announced at Unreal Fest…

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Received β€” 10 June 2026 ⏭ NVIDIA Technical Blog

Designing Production-Ready Battery Energy Storage Systems for AI Factories

10 June 2026 at 15:00
AI factories are changing what data-center infrastructure must do. Unlike traditional data centers, AI factories are built to manufacture intelligence at scale....

AI factories are changing what data-center infrastructure must do. Unlike traditional data centers, AI factories are built to manufacture intelligence at scale. They run power-dense training and inference workloads, increasingly support agentic and reasoning models, and must deliver predictable performance even as compute demand shifts rapidly. In this environment…

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