How NVIDIA Uses Apache

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

As more teams move from humanoid robot bring-up to task-specific skill development, the need for repeatable development workflows is growing.

Industrial machinery generates more alarms than technicians can triage. For each important alarm requiring follow-up, the technician pulls historical context…

GPU-accelerated query engines are often constrained by memory and I/O bandwidth. NVIDIA hardware advances—including high bandwidth memory (HBM)…

Maximizing the value of AI infrastructure demands deep visibility into GPU utilization. Yet many platform teams running AI workloads on Kubernetes operate with…

NVIDIA Ising is the world’s first family of open AI models for building quantum processors, launching with two model domains: Ising Calibration and Ising…

The Gemmaverse expands with the launch of the latest Gemma 4 multimodal and multilingual models, designed to scale across the full spectrum of deployments…

Developing new protein-based therapies and catalysts involves the challenging task of designing protein binders, or proteins that bind to a target protein or…

AI has evolved from assistants following your directions to agents that act independently. Called claws, these agents can take a goal, figure out how to achieve…

The article discusses Project Aether, a tool developed by NVIDIA to facilitate the migration of CPU-based Apache Spark workloads to GPU-accelerated environments on Amazon EMR.

NVIDIA's Sirius, an open-source GPU-native SQL engine, has set a new performance record on ClickBench, enhancing DuckDB with GPU-accelerated analytics.

The article discusses the development of scientific AI agents using reinforcement learning (RL) techniques, specifically through the NVIDIA NeMo framework.

The article discusses the collaboration between IBM and NVIDIA to enhance large-scale data analytics through GPU-native Velox and NVIDIA cuDF, highlighting significant performance improvements over...

This article discusses the integration of the Newton physics engine with NVIDIA Isaac Lab for training quadruped locomotion policies and simulating cloth manipulation.

The article discusses the advancements in NVIDIA cuVS, a GPU-accelerated vector search library designed for high-performance indexing and low-latency retrieval.

The article discusses the deployment of a serverless, distributed data processing architecture using Apache Spark and NVIDIA AI on Azure.

The article discusses NVIDIA's advancements in molecular AI modeling through the introduction of cuEquivariance and NIM microservices, which enhance the speed and efficiency of training and inferen...

The article discusses how NVIDIA cuOpt, an open-source GPU-accelerated optimization tool, enhances decision-making processes in businesses by efficiently solving complex linear programming (LP), mi...

The article discusses the application of Graph Neural Networks (GNNs) in enhancing fraud detection within financial services.

The article discusses the latest enhancements in RAPIDS, including zero-code-change acceleration for Python machine learning, significant IO performance improvements, and out-of-core XGBoost capabi...

The article discusses how Atgenomix SeqsLab leverages NVIDIA technologies to enhance health omics analysis for precision medicine.

The article discusses the use of GPU acceleration to enhance performance in Apache Spark applications, highlighting the challenges of migrating workloads from CPUs to GPUs.

The article discusses how to accelerate Deep Learning (DL) and Large Language Model (LLM) inference using Apache Spark in cloud environments.

The article discusses the integration of the 3D Gaussian Unscented Transform (3DGUT) into the gsplat library, enhancing neural rendering and scene reconstruction for realistic 3D simulations.

NVIDIA cuPyNumeric 25. 03 is a fully open-source library designed as a drop-in replacement for NumPy, leveraging the Legate framework for accelerated computing.

The article discusses how to accelerate Apache Parquet scans on Apache Spark using GPUs, specifically through the RAPIDS Accelerator for Apache Spark.

NVIDIA has open-sourced the KAI Scheduler, a Kubernetes-native GPU scheduling solution under the Apache 2. 0 license, originally developed for the Run:ai platform.

This article discusses strategies for preventing GPU fragmentation in the Volcano Scheduler, focusing on an enhanced scheduling approach that integrates bin-packing with gang scheduling.

The article discusses the performance and energy efficiency of the NVIDIA Grace CPU Superchip for ETL workloads, comparing it with AMD and Intel CPUs.

The article discusses how the NVIDIA RAPIDS Accelerator for Apache Spark enables zero code change for GPU-accelerated data processing, enhancing the performance of Apache Spark ML applications.

The article discusses optimizing high-performance remote I/O operations using NVIDIA KvikIO for data analysis workloads on cloud object storage services.

The article discusses how to read JSON Lines data using NVIDIA's cuDF library, achieving performance improvements of up to 100 times faster than traditional pandas methods.

The article discusses the collaboration between BRLi and Toulouse INP to develop AI-based flood models using NVIDIA PhysicsNeMo, addressing the limitations of traditional physics-based numerical si...

The article discusses the optimization of JSON processing on Apache Spark using GPU acceleration, highlighting significant performance improvements achieved by a Fortune 100 retail company.

IBM has launched Granite 3. 0, a new generation of generative AI models that are compact yet deliver high accuracy and efficiency.

NVIDIA has announced that its CUDA-X platform now accelerates the Polars Data Processing Library, enhancing its performance for data analytics.

The article discusses how RAPIDS AI can accelerate predictive maintenance in manufacturing by leveraging advanced data analytics to minimize downtime and optimize maintenance schedules.

The article discusses the NVIDIA GH200 Grace Hopper Superchip, highlighting its significant advancements in energy efficiency and node consolidation for Apache Spark workloads.

The article discusses the Mistral NeMo 12B model, a next-generation language model developed by NVIDIA and Mistral, designed for high performance on a single GPU.

This article provides a comprehensive guide on encoding and compression techniques for string data in the Parquet format using RAPIDS.

The article introduces five new technical courses offered by NVIDIA aimed at enhancing skills in AI and data science.

This article provides a comprehensive guide on leveraging RAPIDS for GPU-accelerated data processing on Databricks.

The article discusses the application of Graph Neural Networks (GNNs) in optimizing the design and simulation of lattice structures in additive manufacturing.

The article discusses the release of the NVIDIA NeMo Canary model, a state-of-the-art multilingual model for speech recognition and translation.

The article discusses the NVIDIA NeMo Curator framework, an open-source tool designed to streamline the data curation process for training large language models (LLMs).

The article discusses the evaluation of Retrieval-Augmented Generation (RAG) systems, emphasizing the importance of embedding models and systematic evaluation processes.

The article discusses the use of nested data types in RAPIDS libcudf for optimizing ETL workflows.

The article discusses deploying large language models (LLMs) at the edge using the NVIDIA IGX Orin Developer Kit.

A Stanford University team is revolutionizing cardiovascular care through AI-driven simulations that provide patient-specific blood flow visualizations.

The article discusses the integration of RAPIDS and Vadalog Parallel to enhance the performance of neurosymbolic AI systems, particularly in processing large knowledge graphs.